Responsible Ar ﬁcial Intelligence for
Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

A WORKING PAPER

Nicholas Ozor
African Technology Policy Studies Network (ATPS)
Joel Nwakaire
ATPS
Alfred Nyambane
ATPS
Wentland Muha ah
ATPS
Henri TonnangInterna onal Centre of Insect Physiology and Ecology (icipe)
Daisy Salifu
icipeThe African Technology Policy Studies
Network (ATPS) is a transdisciplinary networkof researchers, policymakers, private sector
actors and the civil society promo ng thegenera on, dissemina on, use and masteryof Science, Technology and Innova ons (STI)
for African development, environmental
sustainability and global inclusion. In
collabora on with like-minded ins tu ons,
ATPS provides pla orms for regional andinterna onal research and knowledge
sharing in order to build Africa’s capabili es
in STI policy research, policymaking andimplementa on for sustainable development.

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Published by the African Technology
Policy Studies Network (ATPS)
P. O. Box 10081, 00100- GPO,
Nairobi, Kenya© 2023
ISBN: 978-9966-124-76-0

Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni esiTable of Contents
Acknowledgement .................................................................... iv
Abstract..................................................................................... viii
1. Introduc on ......................................................................... 1
1.1 Jus ﬁca on for responsible AI in Africa’s agriculture
and food systems .......................................................... 3
1.2 Purpose and Objec ves of the Research ...................... 5
2. Methodology........................................................................ 6
3. Theore cal underpinnings and principles aroundresponsible Ar ﬁcial Intelligence ........................................ 7
3.1 Theore cal background to taking responsibility in
AI4AFS .......................................................................... 7
3.1.1 What is Ar ﬁcial Intelligence ............................ 7
3.1.2 What is responsible Ar ﬁcial Intelligence......... 8
3.1.3 Responsible Design........................................... 8
3.1.4 Responsibility of the Designer/Grantee............ 8
3.1.5 Responsibility of the AI Stakeholders ............... 8
3.2 The principles of responsible AI ................................... 9
3.2.1 Ethical Purpose and Societal Beneﬁt................. 10
3.2.2 Accountability.................................................... 10
3.2.3 Transparency and Explainability ....................... 11
3.2.4 Fairness and Non-Discrimina on ..................... 12
3.2.5 Safety and Reliability......................................... 13
3.2.6 Open Data and Fair Compe on....................... 14
3.2.7 Privacy............................................................... 15
3.2.8 AI and Intellectual Property............................... 16
3.3 European Union Perspec ve on Principles of
Responsible AI ............................................................... 16
4. Ra onale for responsible AI in Africa’s Agriculture andFood Systems ....................................................................... 19
4.1 Ra onale for Gender Support and Inclusion................. 22
4.1.1 Overview of Gender Support and Inclusion...... 22
4.1.2. Designing safer and acceptable products......... 24
4.1.3 Reducing Gender Bias in AI............................... 25
4.1.4 Comba ng Stereotypes .................................... 26`
4.1.5 Gender and Science policy................................ 28ii

Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

4.2 The ra onale for Carbon Footprint Repor ng............... 29
4.3 Ethical considera on in non-model trainingbased ac vi es ............................................................. 32
5. Problem and Poten al areas of Agriculture and foodsystem for AI applica on...................................................... 33
5.1 Availability..................................................................... 33
5.2 Access............................................................................ 36
5.3 Use/U liza on............................................................... 37
5.4 Stability ......................................................................... 39
5.5 Poten al Areas of Ar ﬁcial Intelligence Applica on in
Agriculture and Food System in Africa.......................... 40
5.5.1 Poten als of AI in food availability.................... 40
5.5.2 Poten als of AI in Food Access......................... 42
5.5.3 Poten als of AI in food Use/U liza on............. 42
5.5.4 Poten als of AI in food Stability........................ 43
6. Challenges to AI applica on in Africa’s Agriculture andFood System......................................................................... 45
6.1 Lack of diverse datasets speciﬁc to localized issues...... 45
6.2 Privacy concerns............................................................ 45
6.3 Low digital literacy......................................................... 46
6.4 Gender gaps in AI in Africa............................................ 47
6.5 Environmental concerns ............................................... 47
7. Opportuni es in Ar ﬁcial Intelligence for Africa’s
Agriculture and Food Systems.............................................. 49
7.1 Availability of data......................................................... 49
7.2 Availability of technology................................................ 50
7.3 Improved infrastructure................................................. 51
7.4 Increase in technical skills............................................. 51
7.5 Remote agricultural opera ons.................................... 54
7.6 Weather forecas ng...................................................... 54
7.7 Enabling policies and ins tu ons.................................. 54
7.8 Increased youth and gender support ........................... 55
8. Conclusion............................................................................ 56
9. Recommenda ons for eﬀec ve development, deployment,
and scaling of responsible AI in AFS..................................... 58
References ........................................................................... 65

Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni esiiiAcknowledgement
This Working Paper is one of the series of knowledge products fromthe ATPS-led Ar ﬁcial Intelligence for Agriculture and Food Systems
Research and Innova on Network project, a four-year program
sponsored by the Interna onal Development Research Centre (IDRC)
and the Swedish Interna onal Development Agency (Sida) underthe Ar ﬁcial Intelligence for Development Africa (AI4D) Program.
Special thanks to the ATPS communica on team for their editorial
work especially Sheila Wanjeri. ATPS, Icipe, and Kumasi Hive dearly
acknowledge the support from our donors IDRC and Sida.

iv

Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni esAbout the Par cipa ng Organiza ons
The African Technology Policy Studies Network (ATPS):The ATPS is a trans-disciplinary network of researchers, policymakers,
private sector actors and civil society actors that promote thegenera on, dissemina on, use and mastery of STI for African
development, environmental sustainability and global inclusion.
ATPS has over 5,000 network members and 3,000 stakeholders inover 51 countries in 5 con nents with ins tu onal partnerships
worldwide. In the 1980s, two dis nct networks emerged in Africa:
Eastern and Southern Africa Technology Policy Studies and Western
Africa Technology Policy Studies.
In 1994, ATPS was established as a secretariat within the East andSouthern Africa Regional Oﬃce of the IDRC. In 2000, ATPS became
an autonomous interna onal organiza on with diploma c status
in Kenya and working on trans-disciplinary STI themes for African
development. Whilst retaining the STI focus, ATPS has moved towards
a “knowledge for development” network for Africa. Programs are
implemented through members in na onal chapters establishedin 30 countries (27 in Africa and 3 Diaspora chapters in Australia,
USA and UK). In collabora on with like-minded ins tu ons, ATPS
provides pla orms for regional and interna onal research andknowledge sharing to build Africa’s capabili es in STI policy research,
policymaking and implementa on for sustainable development.

Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni esv

The ATPS Mission is to improve the quality of STI systems research,
policy and prac ce by strengthening the capacity for STI knowledge
genera on, dissemina on, and use for sustainable development in
Africa. The overall objec ve is to build Africa’s science, technology,
and innova on capabili es for sustainable development and global
inclusion. The ATPS Phase VIII Strategic Plan, 2017-2022 iden ﬁesfour thema c priority areas of interven on namely: Agriculture,
food and nutri on; Energy; Climate change and environment; andHealth innova ons. The Strategic Plan also iden ﬁes ﬁve crosscu ng programma c priority areas namely: STI policy research,
policymaking and advocacy; Training, sensi za on and capacity
building; Youth and gender empowerment; Knowledge brokerage,
management and commercializa on; and Intra-Africa and global
collabora on. These priority areas align perfectly to deliver on theoverall and speciﬁc objec ves of the AI4AFS.
Interna onal Centre of Insect Physiology and Ecology (icipe):
Established in 1970, icipe is a Pan-African research-for-development
inter-governmental organiza on. With opera ons in 41 Africancountries, the Centre has more than 500 staﬀ based in Kenya, Ethiopia
and Uganda, and more than 300 partners globally, including 43
African universi es. Icipe is a Stockholm Conven on Regional Centre
aiming to reduce the use of persistent organic pollutants for pest
management, a World Organiza on for Animal Health Collabora ng
Centre for Bee Health in Africa, a FAO Reference Centre for Vectors
and Vector-borne Animal Diseases, and a WHO-AFRO partner forManagement of Vectors.
As a centre of excellence, icipe is commi ed to building the capacityof people and ins tu ons to respond to the development needsof Africa. It has a proud tradi on and an excellent reputa on as an
incubator of some of Africa’s best young scien sts, with 150-180
graduate students trained annually. The recently established icipe’s
Data Management, Modelling and Geo-informa on (DMMG) unit isvi

Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

mandated to advance applica on of responsible ar ﬁcial intelligencein Plant Health, Animal Health, Human Health and Environmental
Health, the 4Hs of icipe’s research focus. The unit is responsible foruse of advanced methods and analy cs including machine learning(ML), ar ﬁcial intelligence (AI), design thinking, system thinking andsystem dynamics and computer vision algorithms to exploit andinterpret ‘big data’ to develop geospa al cloud-based tools andmobile apps that can be opera onally u lized for ‘real- me’ insect,plant and environmental health indicators surveillance, monitoring
and forecas ng. Moreover, the unit team works on the use of hyperspa al unmanned aerial vehicles and satellite-based high deﬁni onvideos and al metric informa on that can improve the understandingof various plant, animal, human and environmental health issues.
Modelling ac vi es in the unit include the applica on of advanced
mathema cal, physical and sta s cal methods to data on pests/
diseases in animals, ecosystems, plants, and humans. Emphasis
is placed on research ac vi es aimed at developing, applying andexploring new computa onal techniques for decision support
in the context of agricultural systems, disease vectors control,
environmental assessment, integrated pest management (IPM)
implementa on prac ces and interven ons, and climate change andvariability impact assessments.
Kumasi Hive:
Kumasi Hive is a tech and innova on hub for rapid prototyping ofideas, budding local innova ons, impact start-up support and thepromo on of youth entrepreneurship as a way of addressing cri cal
social, economic, and developmental challenges. Kumasi Hivefocuses on building the capacity of young people in the technology
and business spaces while crea ng sustainable innova ons thatsolve local and global challenges. It exists to support entrepreneurs
and innovators of all types, par cularly to encourage social impact
businesses to develop innova ve physical products and processing
methods.
Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni esviiAbstract
Africa’s popula on is expected to reach about 2.6 billion by 2050.
This will require an increase in agricultural and food produc on by
up to 70% to meet the need of the growing popula on, a serious
challenge for the agriculture and food systems. Such requirement, in
a context of resource scarcity, climate change, COVID-19 pandemic,
global conﬂicts, and very harsh socioeconomic conjecture, is diﬃcult
to a ain unless we embrace emerging technologies and innova ons
such as ar ﬁcial intelligence to leapfrog the transforma ons requiredin the sector.
This Working Paper aims to showcase the challenges and opportuni es
in the responsible development, deployment, and scaling of homegrown ar ﬁcial intelligence research and innova ons in Africa to tackle
pressing challenges in agriculture and food systems. Key challenges
were iden ﬁed namely; lack of data for implementa on, inadequate
number of skilled researchers, unavailability of focused policies
and ins tu onal support, gender gaps, environmental concerns
embedded in carbon footprint informa on, and technological gaps.
Opportuni es iden ﬁed include; increased gender inclusion in
STEM, responsible carbon footprint repor ng with enhanced green
prac ce, increased new jobs in the AI workforce, increased research
opportuni es for donor funding, the forma on of strong synergy
between stakeholders, and innova ons to improve agriculture andfood produc on.

viii Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni esOther opportuni es include: se ng up of ar ﬁcial intelligenceinnova on research network for agriculture and food systems;
managing the innova on research network; and fosteringcollabora ons, knowledge exchange, and valoriza on within the
network and beyond. It is expected that through these interven ons,
responsible and homegrown ar ﬁcial intelligence research andinnova ons will be developed, deployed, and scaled to tackle
pressing challenges in agriculture and food systems in Africa.
The paper iden ﬁed cri cal guiding documents from the Interna onal
Technology Law Associa on (ITECHLAW) and the European Union
Commission as a reference to Ar ﬁcial Intelligence applica on in
Africa in general and speciﬁcally AI applica on in agriculture andfood systems. If responsibly applied, AI in agriculture and foodsystems is a panacea for Africa’s development. Hence core principles
of responsible AI use must be mainstreamed to ensure humancentered design, privacy, and intellectual property compliance, withethics and robustness.

Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni esix

1. Introduc on
A er decades of lethargy, most of Africa is experiencing rapid
economic transforma on. Half of the world’s ten fastest-growing
economies are in sub-Saharan Africa (Kearney, 2014). Africa’s
dynamism has spawned eﬀorts to iden fy the ‘megatrends’ driving
the region’s economic growth and an cipate the future opportuni es
and challenges associated with these trends. Among the mostfrequently cited trends are; the rise of the African middle class(African Development Bank, [AfDB], 2012), rapid urbaniza on andconsequent shi s in food demand and downstream moderniza onof agriculture and food systems (Tschirley et. al., 2015), a rapid shi
in the labour force from farming to non-farm jobs (Fine et. al., 2012),
and rising global interest in African farmland (Schoneveld, 2014).
Agriculture remains the bedrock of sustainability of African
economies (Kekane, 2013) where it plays a key part in long-term
economic growth and structural transforma on (Syrquin, 1988),
though, may vary by country (Dekle and Vandenbroucke, 2012). In
the past, agricultural ac vi es were limited to food crop produc on(Fan et. al., 2012), but in the last few decades, it has evolved
to encompass the food systems comprising the en re range ofactors and their interlinked value-adding ac vi es involved in the
produc on, aggrega on, processing, distribu on, consump on, anddisposal of food products that originate from agriculture, forestry or
ﬁsheries, and parts of the broader economic, societal and naturalenvironments in which they are embedded (Food and Agriculture
Organiza on [FAO], 2018). Agriculture and food systems (AFS) provide
the basic source of livelihood, improving Gross Domes c ProductResponsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es1

(GDP) (Oyakhilomen and Zibah, 2014), a source of na onal trade,reducing unemployment, providing raw materials for produc on inother industries, and overall development of economies (Awokuse,2009). That notwithstanding, AFS s ll fails to supply the required
food needs of the developing regions, especially in Africa. According
to FAO et. al. (2020), food security is not improving in Africa.
While about 795 million people are undernourished globally, including
90 million children under the age of ﬁve (FAO, Interna onal Fundfor Agricultural Development [IFAD] and World Food Programme[WFP], 2015), the vast majority of them totalling 780 million people
live in the developing regions, notably in Africa and Asia (United
Na ons Conference on Trade and Development [UNCTAD], 2017). In
par cular, sub-Saharan Africa (SSA) is worse, with almost 25% of the
popula on undernourished (FAO et. al., 2015).
The inability of African countries especially SSA to feed their
popula on poses greater challenges to their development. The
agricultural sector in the region is characterized by over-reliance on
primary products, low fer lity soils, minimal use of external farm
inputs, environmental degrada on, signiﬁcant pre, and post-harvest
losses, low yields, minimal value addi on, and inadequate foodstorage and preserva on that result in signiﬁcant commodity priceﬂuctua ons.
The COVID-19 pandemic and unprecedented desert locust outbreaks
in Eastern Africa have exacerbated economic prospects in ways no
one could have an cipated, and the situa on may only worsen if we
do not act urgently and take unprecedented measures (FAO et. al.,2020). These challenges occur mainly because of poorly developed
and non-adop on of good mixes of low to high technologies by smallscale farmers who form the bulk of the farming popula on in Africa(Ozor and Urama, 2013). With the current geometric rise in Africa’s
popula on es mated to reach about 2.6 billion by 2050 and a growth2

Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

rate of more than 2.5% p.a. (Popula on of Africa, 2019), it becomes
impera ve that agriculture and food systems (AFS) are reviewed toembrace innova ve approaches for sustaining and improving thesystem from produc on to u liza on. One of the most promising
ways for achieving this target is through science, technology, andinnova on (STI) (Ozor and Urama, 2013; UNCTAD, 2017).
STI is recognized as a means for achieving the sustainable development
goal (SDG) 2 (End hunger). Ar ﬁcial Intelligence (AI), which is thecapacity of a machine to perform cogni ve func ons associated withhuman minds, such as perceiving, reasoning, learning, interac ng
with the environment, solving problems, and even exercisingcrea vity (Manyika et. al., 2017), stands out as one of the emerging
technologies with a great poten al to transform the AFS and ensurethat all aspects of food security including food availability, access,u liza on, and stability are achieved even for small-scale farm
enterprises in Africa.
The introduc on of AI for AFS is enabled by other technologicaladvances such as big data, robo cs, machine learning (ML), Internetof Things (IoT), availability of aﬀordable sensors and cameras, drone
technology, and even wide-scale internet coverage of geographicallydispersed ﬁelds (Eli-Chukwu, 2019). Despite the growth of
movements applying ML, IoT, and AI among other tools to solve the
AFS challenges, there remains the need to iden fy how these tools
may best beneﬁt Africa under its peculiar circumstances.
1.1

Jus ﬁca on for responsible AI in Africa’s agriculture andfood systems

Globally, agriculture and food produc on will con nue to be aﬀectedby two driving forces namely; the increase in popula on, par cularly
in Africa, and; climate change, which will heighten the challenge of
feeding more people.
Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

3To overcome these two challenges, Africa must pursue the path of asustainable food security trajectory, which involves a mix of diﬀerent
approaches with the applica on of STI being paramount (UNCTAD,
2017). Agriculture in Africa has a massive social and economic
footprint. More than 60% of the popula on of SSA are smallholder
farmers, and about 23% of SSA’s GDP comes from agriculture (Goedde
et. al., 2019). Yet, Africa’s full agricultural poten al to feed itself andindustrialize the sector remains untapped.
According to Goedde et. al., (2019), Africa could produce two to threemes more cereals and grains, which would add 20% more cereals
and grains to the current worldwide 2.6 billion tons of output. Similar
increases could be seen in the produc on of hor culture crops andlivestock. S ll, these remain unrealized because the agriculture andfood sector have not been digi zed amidst other social, economic, andpoli cal constraints. Regarding supply chain issues, the con nent’s
retail moderniza on is s ll in the early stages of development, andmore than 90% of African commerce occurs in informal markets or at
the micro-retail level (Nieuwoudt, 2019).
The overarching agricultural challenge for science in Africa is how thepersis ng low produc vity in the farming systems can be transformed.
Among the main challenges are a lack of coherent and conducive
policies; poor incen ves; poor access to input and output markets;
predominant rain-fed agriculture; inadequate agricultural researchand development (R&D) spending; heavily degraded and depletedsoils; problema c land tenure systems; poor mechaniza on; pests,
diseases, and weeds; and climate change (Forum for Agricultural
Research in Africa [FARA], 2014).
Agriculture is the backbone of Africa’s economy and accounts forthe majority of livelihoods across the con nent, yet the sector isunderdeveloped. The global popula on is expected to reach morethan 9 billion by 2050, which will require an increase in agricultural
4

Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

produc on by 70% to fulﬁl the demand. Only about 10% of thisincreased produc on may come from unused lands and the rest
should be fulﬁlled by current produc on intensiﬁca on and STI (EliChukwu, 2019 and UNCTAD, 2017). Over 870 million people suﬀer
from chronic undernourishment, 27% of which are in Africa alone(World Hunger Educa on Service, 2013).
A growing global popula on, par cularly in Africa, exacerbates
this challenge. In this context, the use of the latest technologicalsolu ons to make farming more eﬃcient remains a great necessity.
It, therefore, follows that the solu ons to these key challenges areneeded quickly and answers can be obtained through the applica onof STI which AI can have a profound impact on the future of AFS in
Africa (UNCTAD, 2017).
1.2

Purpose and Objec ves of the Research

The overall purpose of the study is to examine the place of responsible
ar ﬁcial intelligence (AI) in agriculture and food systems (AFS) whileiden fying the accompanying challenges and opportuni es in its
development, deployment, and scaling in Africa. Most speciﬁcally,
the research study aims to:i) Review the theore cal underpinnings and principles aroundresponsible AIii) Examine the ra onale for the applica on of responsible AI in
AFS;iii) Iden fy the speciﬁc areas in AFS where AI can be applied toboost food and nutri on security in Africa;iv) Examine the challenges for eﬀec ve development, deployment,
and scaling of AI in African AFS;v) Examine the opportuni es that AI presents in ensuring
adequate food and nutri on security in Africa; andvi) Proﬀer recommenda ons for eﬀec ve development,
deployment, and scaling of responsible AI in AFS in Africa.
Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

5

2. Methodology
This review focuses on the challenges and opportuni es in the
responsible development, deployment, and use of AI in Africa’s
agriculture and foods system. Africa’s con nent is home to over 137
billion people, with a Gross Domes c Product (GDP) of 270 billion asof 2021 (AFDB, 2021).
The data collected was mainly qualita ve and partly quan ta ve.
The sources were from 100 relevant literature published between
1996 and 2022; informa on was analysed according to importanceconcerning the study area and subject.
Other sources of data included AI webinars like the 1st Ar ﬁcial
Intelligence for Development (AI4D) innova on Hubs learning forum
organized by the Interna onal Development Research Centre (IDRC)
and the Swedish Interna onal Development Agency (Sida) on 28th
January 2022 and Advancing Data Jus ce in AI organized by Centre
for Intellectual Property and Informa on Technology Law (CIPIT)
workshop on February 23rd 2022. The informa on aggregated isshown in the succeeding sec ons.

6

Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

3. Theore cal underpinnings andprinciples around responsible
Ar ﬁcial Intelligence
3.1 Theore cal background to taking responsibility in AI4AFS
Responsible AI implementa on in AFS is embedded in the designof products, services, and the a tude of designers and funders.
The stakeholders in responsible AI in Agriculture and food systems
include AI designers and AI developers, data scien sts, procurementoﬃcers or specialists, front-end staﬀ that will use or works with the
AI system, legal/compliance oﬃcers, and management.
3.1.1 What is Ar ﬁcial Intelligence
Ar ﬁcial Intelligence (AI), which is the capacity of a machine toperform cogni ve func ons associated with human minds, such as
perceiving, reasoning, learning, interac ng with the environment,
solving problems, and even exercising crea vity (Manyika et. al.,2017), stands out as one of the emerging technologies with a great
poten al to transform the AFS and ensure that all aspects of foodsecurity including food availability, access, u liza on, and stability
are achieved even for small-scale farm enterprises in Africa.
3.1.2 What is responsible Ar ﬁcial Intelligence
Responsible AI is the prac ce of designing, developing, and deploying
Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

7

AI that is lawful, ethical, robust, societal, and environmentallyfriendly, with good inten ons to empower people. Responsible AI(RAI) is the only way to mi gate AI risks.
3.1.3 Responsible Design
Responsible design involves ensuring that the development processes
during the project consider the ethical and societal implica ons of AI
as it integrates and replaces tradi onal systems and social structures(Zhu et al., 2022). It means integra ng ethical reasoning ability as
part of the behaviour of the ar ﬁcial autonomous systems.
It also implies that the behaviour of the systems must be in tandemwith what is or it is replacing to avoid any user conﬂicts which involve
reorienta on.
3.1.4 Responsibility of the Designer/Grantee
This involves a measure of research integrity of the researcher
and manufacturers, cer ﬁca ons, and mechanisms. The designer
ensures all the ethical considera ons in line with the responsible AI
core principles are diligently adhered to and followed. The designer
understands that he/she is accountable for the designs.
3.1.5 Responsibility of the AI Stakeholders
The stakeholders should be responsible for ensuring the projects
implemented by the grantees follow all the laid down guidelines andprinciples of responsible AI. These involve:i) Innova ng Responsibly: Drive responsible AI innova ons
in AFS by pu ng the core principles of responsible AI
into prac ce by taking a people-centered approach to AI
research, development, and deployment. To achieve this, weembrace diverse perspec ves, con nuous learning, and agile
responsiveness as AI4AFS technology evolves (Zhu et al., 2022).
8

Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni esii) Empowering others: there is a need to cul vate a responsible
AI-ready culture throughout their research and put principles
into place from implementa on to governance with prac ces,
tools, and technologies built on mul disciplinary research,shared learning, and leading innova on. These will be achieved
through capacity-building workshops on responsible AI in
agriculture and food systems (Zhu et al., 2022).
iii) Fostering posi ve impact: There should be a commitment
to ensuring AI technology has a las ng posi ve impact on
everyone by helping to shape public policy, contribu ng toindustry working groups, and empowering those working toaddress society’s most signiﬁcant challenges.
3.2 The principles of responsible AI
The core principles guiding the implementa on of a responsible
AI4AFS according to ITECHLAW (2021) on the global responsible AI
policy framework are shown in ﬁgure 1. Understanding these core
principles is central to the ethical success of this intended AI project.
These core principles are elaborated on in the preceding paragraphs.

AI andIntellectual
Property

PrivacyOpen Data
and FairCompe onEthicalPurpose andSocietal
BeneﬁtAccountability

Responsible
AI Principles

Transparency
andExplainability

Fairness and
NonDiscrimina on
Safety andReliability

Fig 1: Responsible AI core Principles
Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

9

3.2.1 Ethical Purpose and Societal Beneﬁt
Establishments that develop, deploy or use AI systems and any
na onal laws that control such use should require the purposesof such applica on to be iden ﬁed and ensure that such purposes
are consistent with the overall moral purposes of beneﬁcenceand non-maleﬁcence, as well as the other principles of the Policy
Framework for Responsible AI (Powling & Hammer, 2019). It implies
that organiza ons/individuals that develop, deploy, or use AI
systems should do so in a manner compa ble with human agency
and respect for fundamental human rights (including freedom fromdiscrimina on) (Dormehi, 2019).
Any organiza on/ individuals that develop, deploy, or use AI systems
should monitor the implementa on of such AI systems and act tomi gate against consequences of such AI systems (whether intended
or unintended) that are inconsistent with the ethical purposes of
beneﬁcence and non-maleﬁcence, as well as the other principles for
Responsible AI set out in the country or regional guiding policies.
Finally, all that develop, deploy or use AI systems should assess thesocial, poli cal, and environmental implica ons of such development,
deployment, and use in the context of a structured Responsible
AI Impact Assessment that evaluates the risk of harm and, as thecase may be, proposes mi ga on strategies in rela on to such risks(Feldstein, 2019).
3.2.2 Accountability
In all instances, humans should remain accountable for the acts
and omissions of AI systems. All organiza ons or individuals thatdevelop, deploy, or use AI systems shall designate an individual orindividuals who are accountable for the organiza on’s compliancewith those principles. The iden ty of the individual(s) designated by
10 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni esthe organiza on to oversee the organiza on’s compliance with theprinciples shall be made known upon request (Villaronga & Golia,2019).
All organiza ons/ individuals that develop, deploy or use AI systems
shall implement policies and prac ces to give eﬀect to the principlesof the Policy Framework for Responsible AI or other adopted
principles (including analogous principles that may be developed fora speciﬁc industry), including:i. establishing processes to determine whether, when, and how
to implement a “Responsible AI Impact Assessment” process;ii. establishing and implemen ng “Responsible AI by Design”
principles;iii. establishing procedures to receive and respond to complaints
and inquiries;
iv. training staﬀ and communica ng to staﬀ informa on about theorganiza on’s policies and prac ces; andv. developing informa on to explain the organiza on’s policies
and procedures (Villaronga & Golia, 2019).
3.2.3 Transparency and Explainability
Organiza ons that develop, deploy or use AI systems and any na onal
laws that regulate such use shall ensure that, to the extent reasonablegiven the circumstances and state of the art of the technology, such
use is transparent and that the decision outcomes of the AI systemare explainable.
Transparency is an obliga on for organiza ons that use AI in decisionmaking processes to provide informa on regarding a) the fact thatan organiza on is using an AI system in a decision-making process;b) the intended purpose(s) of the AI system and how the AI system
will and can be used; (c) the types of data sets that are used by theAI system; and (d) meaningful informa on about the logic involved(Keller, 2020).
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11Explainability is an obliga on for organiza ons that use AI in decisionmaking processes to provide accurate informa on in humanly
understandable terms explaining how a decision/outcome wasreached by an AI system (Metz, 2019). Transparency and Explainability
aim to preserve the public’s trust in AI systems and provide suﬃcient
informa on to help ensure meaningful accountability of an AI system’s
developers, deployers, and users, and to demonstrate whether thedecisions made by an AI system are fair and impar al.
The Transparency and Explainability principles support the
Accountability principle, the Fairness and Non-Discrimina on
principle, the Safety and Reliability principle, and the Privacy, Lawful
Use, and Consent principles. In areas of AI design, Organiza onsthat develop AI systems should ensure that the system logic andarchitecture serve to facilitate transparency and explainability
requirements. In so far as is reasonably prac cable and considering
the state of the art at the me, such systems should aim to be designed
from the most fundamental level upwards to promote transparency
and explainability by design. Where there is a choice between systemarchitectures that are less or more opaque, the more transparent
op on should be preferred (Loi and Christen, 2019).
3.2.4 Fairness and Non-Discrimina on
Decisions based on AI systems should be fair and non-discriminatory,
judged against the same standards as decision-making processes
conducted en rely by humans (European Commission, 2019). The
use of AI systems by organiza ons that develop, deploy or use AI
systems and Governments should not serve to exempt or a enuate
the need for fairness, although it may mean refocussing applicableconcepts, standards and rules to accommodate AI.
Users of AI systems and persons subject to their decisions must havean eﬀec ve way to seek remedy in discriminatory or unfair situa ons
generated by biased or erroneous AI systems, whether used by
12 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni esorganiza ons that develop, deploy or use AI systems or governments,
and to obtain redress for any harm (China Ministry of Science andTechnology, 2019).
AI systems can perpetuate and exacerbate bias and have a broadsocial and economic impact on society. Addressing fairness in AI use
requires a holis c approach. In par cular, it requires:i. the close engagement of technical experts from AI-relatedﬁelds with sta s cians and researchers from the social
sciences; andii.

a combined engagement between governments, organiza onsthat develop, deploy or use AI systems, and the public at large.

3.2.5 Safety and Reliability
Organiza ons that develop, deploy, or use AI systems and any na onal
laws regula ng such use shall adopt design regimes and standards
ensuring high safety and reliability of AI systems while limi ng theexposure of developers and deployers, on the other hand.
All developing, deploying, or using AI systems should recall that
ethical and moral principles are not globally uniform but may be
impacted by geographical, religious, or social considera ons andtradi ons. To be accepted, AI systems might have to be adjustable inorder to meet the local standards in which they will be used. Consider
whether all possible occurrences should be pre-decided in a way toensure the consistent behaviour of the AI system, the impact of thison the aggrega on of consequences, and the moral appropriatenessof “weighing the unweighable” such as life vs. life (Gardner et al.,2021).
Regula ng agencies should be required, and organiza ons should
test AI systems thoroughly to ensure that they reliably adhere, in
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13

opera on, to the underpinning ethical and moral principles and havebeen trained with data that are curated and are as ‘error-free’ as
prac cable, given the circumstances. All are encouraged to adjust
regulatory regimes and/or promote industry self-regulatory regimes
to allow market-entry of AI systems to reasonably reﬂect the posi ve
exposure that may result from the public opera on of such AI systems(Buyers and Barty, 2021). Special regimes for intermediary andlimited admissions to enable tes ng and reﬁning of the AI system’s
opera on can help expedite the comple on of the AI system andimprove its safety and reliability (Gardner et al., 2021).
In order to ensure and maintain public trust in absolute humancontrol, governments should consider implemen ng rules that ensurecomprehensive and transparent inves ga on of such adverse andunan cipated outcomes of AI systems that have occurred throughtheir usage, in par cular, if these outcomes have lethal or dangerous
consequences for the humans using such systems.
3.2.6 Open Data and Fair Compeon

Organiza ons that develop, deploy or use AI systems and any
na onal laws that regulate such use shall promote (a) open accessto datasets that could be used in the development of AI systems and(b) open-source frameworks and so ware for AI systems. AI systems
must be developed and deployed on a “compliance by design” basis
in rela on to compe on/an trust law. Organiza ons that develop
AI systems are normally en tled to commercialize such systems as
they wish (European Commission, 2020).
However, governments should at the very least advocate accessibility
through open source or other similar licensing arrangements to thoseinnova ve AI systems which may be of par cular societal beneﬁt or
advance the “state of the art” in the ﬁeld via, for example, targeted
incen ve schemes.
14 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni esOrganiza ons that elect not to release their AI systems as open-source
so ware are encouraged nevertheless to license the System on acommercial basis. To the extent that an AI system can be sub-divided
into various cons tuent parts with general u lity and applica on
in other AI use-cases, organiza ons that elect not to license the AI
system as a whole (whether on an open source or commercial basis)are encouraged to license as many of such re-usable components as
is possible.
3.2.7 Privacy
Organiza ons that develop, deploy or use AI systems and any
na onal laws that regulate such use shall endeavour to ensurethat AI systems are compliant with privacy norms and regula ons,
taking into account the unique characteris cs of AI systems, andthe evolu on of standards on privacy. There is an inherent anddeveloping conﬂict between the increasing use of AI systems to
manage private data, especially personal data; and the increasing
regulatory protec on aﬀorded interna onally to personal and otherprivate data (Powling & Hammer, 2019). Governments that regulate
the privacy implica ons of AI systems should do so in a manner that
acknowledges the speciﬁc characteris cs of AI and that does not
unduly s ﬂe AI innova on.
Organiza ons that develop, deploy and use AI systems should analyse
their current processes to iden fy whether they need to be updated
or amended in any way to ensure that respect for privacy is a central
considera on (Powling & Hammer, 2019).
AI systems create challenges speciﬁcally in rela on to the prac cali esof mee ng requirements under several na onal legisla ve regimes,
such as consent and anonymiza on of data. Accordingly, organiza onsthat develop, deploy or use AI systems and any na onal laws
regula ng such use shall provide alterna ve lawful bases for thecollec on and processing of personal data by AI systems.
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15

These organiza ons should consider implemen ng opera onal
safeguards to protect privacy by designing principles tailored to thespeciﬁc features of deployed AI systems (Feldstein, 2019).
Addi onally, they should appoint an AI Ethics Oﬃcer, in a role similar
to Data Protec on Oﬃcers under the GDPR, but with a speciﬁc remit
to consider the ethics and regulatory compliance of their use of AI.
Although there are challenges from a privacy perspec ve from theuse of AI, in turn, the advent of AI technologies could also be used tohelp organiza ons comply with privacy obliga ons (Feldstein, 2019).
3.2.8 AI and Intellectual Property
Organiza ons that develop, deploy or use AI systems should takenecessary steps to protect the rights in the resul ng works throughappropriate and directed applica on of exis ng intellectual property
rights laws. Governments should inves gate how AI-authored works
may be further protected without crea ng any new IP right at thisstage. Organiza ons must therefore be allowed to protect rights inworks resul ng from the use of AI, whether AI-created works or AIenabled works (WIPO, 2020).
3.3 European Union Perspec ve on Principles of Responsible AI
As a general guiding statement European Commission (European
Commission, 2019); a trustworthy AI development, deployment, anduse must be lawful (respec ng all applicable laws and regula ons),
ethical (respec ng ethical principles and values), and robust (bothfrom a technical perspec ve while taking into considera on its social
environment).
All applicants should fully understand the guiding principles of
responsible or trustworthy AI development, deployment, andu liza on as found out in a set of 7 essen al requirements that AI
16 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni essystems should meet in order to be deemed trustworthy. A speciﬁc
assessment list aims to help verify the applica on of each of thecri cal requirements according to European Commission (2019).
Some of the key requirements or guiding principles are:Human agency and oversight: AI systems should empower humanbeings, allowing them to make informed decisions and fostering theirfundamental rights. At the same me, proper oversight mechanisms
need to be ensured, which can be achieved through human-in-theloop, human-on-the-loop, and human-in-command approaches.
Technical Robustness and safety: AI systems need to be resilientand secure. They need to be safe, ensuring a fallback plan in casesomething goes wrong, as well as being accurate, reliable, andreproducible. That is the only way to ensure uninten onal harm is
minimized and prevented.
Privacy and data governance: besides ensuring full respect for
privacy and data protec on, adequate data governance mechanisms
must also be ensured, taking into account the quality and integrity ofthe data, and ensuring legi mized access to data.
Transparency: the data, system, and AI business models shouldbe transparent. Traceability mechanisms can help achieve this.
Moreover, AI systems and their decisions should be explained in amanner adapted to the stakeholder concerned. Humans need to
be aware that they are interac ng with an AI system, and must be
informed of the system’s capabili es and limita ons.
Diversity, non-discrimina on and fairness: Unfair bias must be
avoided, as it could have mul ple nega ve implica ons, from themarginaliza on of vulnerable groups to the exacerba on of prejudice
and discrimina on. To promote diversity, AI systems should beaccessible to all, regardless of diﬀerences in physical and social
Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es17abili es, and involve relevant stakeholders throughout their en relife cycle.
Societal and environmental well-being: AI systems should beneﬁt
all human beings, including future genera ons. It must hence beensured that they are sustainable and environmentally friendly.
Moreover, they should take into account the environment, including
other living beings, and their social and societal impact should be
carefully considered.
Accountability: Mechanisms should be put in place to ensure
responsibility and accountability for AI systems and their outcomes(Buyers and Barty (2021). Auditability, which enables the assessmentof algorithms, data, and design processes plays a key role therein,
especially in cri cal applica ons. Moreover, adequate and accessible
redress should be ensured.

18 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

4. Ra onale for responsible AI in
Africa’s Agriculture and Food
Systems
An essen al goal of responsible AI is to reduce the risk that a minorchange in an input’s weight will dras cally change the output of amachine learning model. The issue of responsible AI arises due tothe autonomous nature of AI systems. There is need to adhere to aclear set of values that are core to successful AI implementa on bydelivering solu ons with integrity, respec ng individuals, and always
being mindful of the social impact of products and services.
In reality, responsible AI will involve products and services that do notcause an end to the world or, in this case, limit or reduce agriculturaland food systems growth, but s mulates a sustainable increase in
the product while maintaining integrity, respect, and posi ve social
impacts (Balagué, 2021). An example is the automa c agro-productsor ng machine that uses high electromagne c waves.
The autonomous system should cause no harm to the end-user,
meet interna onal standards, support gender ethics, and not cause
any damage to the environment. Fairness and equity are mostly
misunderstood. Responsible AI ensures jus ce and equity throughsafe prac ces, high awareness, the ability to replace, maintaining
privacy principles without bias, and ensuring human dignity.

Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
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19Some of the advantages of responsible AI in agricultural and foodsystem research and development include the following:a) It beneﬁts the public good and minimizes unintended
consequences, especially those infringing on individual rights
and liber es.
b) Adop ng a “responsible AI” framework is an urgent moral
impera ve due to rapid innova on across all sectors of society.
There is a high risk of poten al societal and individual harmbecause very few people truly understand the technology andbecause the technology may have such a signiﬁcant impact onmany.
c) Takes a holis c approach to development and execu on at alllevels, inten onally engaging with diverse stakeholders andacknowledging that industry and government stakeholders arelooking for prac cal guideposts for responsible conduct.
d) It follows emerging global consensus and models around ethicalAI best prac ces.
e) Places the no on of human “accountability” at the heart of theframework (making organiza ons that develop, deploy or use
AI systems accountable for the harm caused by AI).
f) Rejects the no on of gran ng legal personality to AI systems.
g) Commits to avoid bias and discrimina on through regularly
tested and evaluated systems and prac ces and the inclusionof diverse stakeholders.
To be considered responsible, AI mandates the use of shared code
repositories, approved model architectures, supported variables,
20 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni esand established bias tes ng methods to determine validity, which ispart of an enterprise-wide development standard.
All AI system tes ng should be built at the grassroots level using
resources and technology stability criteria for ac ve machine learningmodels to ensure that AI programming works as intended. Promotes
transparency and explainability of AI systems to promote humanity.
Iden ﬁes and explains when and how opera ons involve humans or
AI-driven nonhumans.
Ar ﬁcial intelligence is complex, high risk, and quickly embedding
itself into our everyday lives, o en without people realizing it. No
universal guidelines for responsible AI exist yet, hence the need for
responsibility.
The ques on is, who takes responsibility when something goes
wrong? Therefore, responsible AI is a governance framework that
documents how a speciﬁc organiza on addresses the challenges
around AI from both an ethical and legal point of view (Begishev &Khisamova, 2021). Resolving ambiguity about where responsibilitylies if something goes wrong is essen al for responsible AI ini a ves.
Ar ﬁcial Intelligence is beneﬁcial to human endeavours but can beharmfully used if not checked. Hence the need for organiza ons,
individuals, and society to take responsibility (Paliwal et al., 2022).
These are six fundamental reasons why AI used in our lives must be
responsible:a) Because AI is being embedded into nearly every profession
and industry worldwide in ways that will change how we as
humans and organiza ons connect, work, play, and learn—inother words, our daily experiences.
b) Technology has a signiﬁcant capacity to disrupt and distort,
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21

poten ally beneﬁ ng and/or harming humankind and therights, protec ons, and values we all currently enjoy.
c) AI is complex and understood by few people, crea ng a serious
imbalance of comprehension and inadequate permissiongiving by ordinary people whose lives are o en unknowingly
being changed.
d) AI is already being explored and applied for malicious purposesthat threaten our safety, security, and human rights.
e) The risks of bias, discrimina on, decep on and privacy
infringement are high due in part to inadequate engagement
by diverse stakeholders in the development, applica on, andaccountability of AI systems and products.
No universal standards of responsible AI exist, so many AI providers
and users are not opera ng within a best-prac ces framework that
holds themselves accountable for the reliance, fairness, and impactof this transforma ve technology. Responsible AI involves otheraspects which are gender inclusion and support and carbon footprint
tracking which ensure the environmental friendliness of all ac vi es.
The next subsec on will examine the ra onale for responsible gendersupport and carbon footprint report.
4.1 Ra onale for Gender Support and Inclusion
4.1.1 Overview of Gender Support and Inclusion
Gender refers to the socially constructed characteris cs of women,men, girls, and boys. This includes norms, behaviours, and roles
associated with being a woman, man, girl, or boy, and rela onships
with each other. As a social construct, gender varies from society to

22 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni essociety and can change over me. Gender refers to psychological,
social, and cultural factors that shape a tudes, behaviours,
stereotypes, technologies, and knowledge. Gender norms refer tospoken and unspoken rules in the family, workplace, ins tu on, or
global culture that inﬂuence individuals (Prates et al., 2019).
Gender iden ty refers to how individuals and groups perceive andpresent themselves within speciﬁc cultures. Gender rela ons refer topower rela ons between individuals with diﬀerent gender roles andiden es (Nomura, 2017). Gender-speciﬁc repor ng is s ll limited ina range of scien ﬁc disciplines (Scanes et al., 2021). In most science,and engineering output, certain norms are gender-biased (Potluri etal., 2017). A review of experimental ocean acidiﬁca on studies inmarine science showed that only 3.9% of studies sta s cally assessed
sex-based diﬀerences (Buolamwini and Gebru, 2018). In comparison,
only 10.5% of studies accounted for possible sex eﬀects by assessing
females and males independently (Khramtsova et al., 2019).
Analysing experimental results by sex and gender is cri cal for
improving accuracy and avoiding misinterpreta on of data. Scien sts
have erroneously assumed that females should be excluded fromexperiments because of the variable nature of the data caused by
the reproduc ve cycle (Beery 2018). Research has shown that males
exhibit equal or more signiﬁcant variability than females for speciﬁc
traits owing to ﬂuctua ons in testosterone levels and other factors,
such as animal group caging (Li et al., 2017). An o en neglectedbut crucial component of engineering is understanding the broadersocial impacts of the technology being developed and ensuring thatthe technology enhances social equity and equality by beneﬁ ng
diverse popula ons (Li et al., 2017).
Human bias and stereotypes can be perpetuated and even ampliﬁed
when researchers fail to consider how human preferences andassump ons may consciously or unconsciously be built into science
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23

or technology. Gender norms, ethnicity, and other biological andsocial factors are shaped by science and technology in a robust
cultural feedback loop (Cripps et al., 2016). Hence, gender inclusion
is a concept that transcends mere equality. It is the no on that allservices, opportuni es, and establishments are open to all people
and that male and female stereotypes do not deﬁne societal roles
and expecta ons.
Gender inclusion and support aid in the removal of barriers totechnology adop on and use. Since most technology beneﬁciaries
are women, gender inclusion and support will enhance par cipa on,thus removing any perceived bias, and increasing the willingness to
produce, adopt, and disseminate technology.
Current suites show that technology interven on most mes displaces
women, thus crea ng high unemployment rates among women. In
terms of research and development, women are underrepresented in
Ar ﬁcial Intelligence and account for only 26% of data and AI posi ons
in the workforce (Heise et al., 2019). Therefore, a robust programmeof inclusion and support in AI development and deployment is a
moral responsibility for all stakeholders. Increasing representa on
and diversity in AI development is crucial to developing and deploying
responsible AI products of value to all.
4.1.2. Designing safer and acceptable products
When products are designed based on the male norm, there is arisk that women and people of smaller stature will be harmed.
Motor vehicle safety systems provide one such example. Because
male drivers have historically been overrepresented in traﬃc data,
seatbelts and airbags have been designed and evaluated focusing on
the typical male occupant with respect to anthropometric size, injury
tolerance, and mechanical response of the aﬀected body region(Sugimoto et al., 2019).
24 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
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When na onal automo ve crash data from the United States wereanalysed by sex between 1998 and 2008, data revealed that the odds
for a belt-restrained female driver to sustain severe injuries were
47% higher than those for a belt-restrained male driver involvedin a comparable crash, a er controlling for weight and body mass
(Sugimoto et al., 2019).
The subsequent introduc on of a virtual female car crash dummyallowed mathema cal simula ons to account for the eﬀect ofaccelera on on sex-speciﬁc biomechanics, highligh ng the needto add a medium-sized female dummy model to regulatory safety
tes ng (Shah et al., 2014). Beyond automo ve safety systems, theimportance of anthropometric characteris cs, such as the carrying
angle of the elbow or the shape and size of the human knee, can beused to guide sex-speciﬁc design for ar ﬁcial joints, limb prostheses,
and occupa onal protec ve gear (Prates et al., 2019).
4.1.3 Reducing Gender Bias in AI
Alarming examples of algorithmic bias are well documented.
For example, Google Translate defaults to male pronouns whentransla ng gender-neutral language related to science, technology,
engineering, and mathema cs (STEM) ﬁelds. When photographs
depict a man in the kitchen, automated image cap oning algorithms
systema cally misiden fy the individual as a woman (Parker et al.,2021). As AI becomes increasingly ubiquitous in everyday lives, suchbias, if uncorrected, can amplify social inequi es. Understanding
how gender operates within the algorithm context helps researchers
make conscious decisions about how their work func ons in society.
Figure 2 is the dra pathway to gender inclusion and science andengineering research support.

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25

Will the phenomenon or product under study
aﬀect organisms with biological sex?

No

Yes
Iden fy adequate numbers of allsexes for inclusion in experiments
Analyse by sex
Does a diﬀence exist when data
are disaggregated by sex?

No

Yes
Determine the source of the sex diﬀerence

Environmental

Gene cHormonal

Anthropometric

Experimenterpar cipant interac on

Report ﬁndings by sex, explain underlying mechanism
Report when sex diﬀerences do not exist

Source: Tannenbaum et al., (2019)
Fig. 2: Sex analysis and repor ng in science and engineering.

4.1.4 Comba ng Stereotypes
Analysing gender in so ware systems is one issue; conﬁguring
gender in hardware, such as social robots, is the focus of thissec on. Un l recently, robots were largely conﬁned to factories.
Most people never see or interact with these robots; they do not
look, sound or behave like humans. But engineers are increasingly
designing robots to assist humans as service robots in hospitals,
elder care facili es, classrooms, homes, airports, and hotels. The
ﬁeld of social human–robot interac on examines, among otherthings, when and how ‘gendering’ robots, virtual agents, or chatbots
might enhance usability while, at the same me, considering when
26 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni esand how to avoid oversimpliﬁca ons that may reinforce poten ally
harmful gender stereotypes (Buch et al., 2019). Machines are, in
principle, genderless. Gender, however, is a core social category inhuman impression forma on that is readily applied to nonhumanen es. Thus, users may consciously or unconsciously regard gendermachines as a func on of anthropomorphizing them, even when
designers intend to create gender-neutral devices. Gender repor ng
in science and engineering should follow the pathway shown in
Figure 3 (Tannenbaum et al., 2019).
Will the phenomenon or productunder study involve humans?

No

Yes
Construct dataset that includes genders relevant to study;
birth sex should not be used as a proxy for gender
Develop qualita ve or quan ta ve methods that capture
gender construc ons appropriate for the cultural context
Analyse by gender
Does diﬀences and overlaps exist whendata are disaggregated by gender?

No

Yes
Determine the sources of gender
diﬀerences and overlaps

Gender
Norms

GenderIden ty

GenderRela ons

Consider op ons tomi gate gender bias andimplement solu ons

Experimenterpar cipant
interac on

Productpar cipant
interac on

Interac on
with otherfactors

Report ﬁndings by
gender, explain
underlying mechanisms

Report when gender diﬀerences
do not impact research
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27

The two ﬁgures (Fig 2 and 3) show the decision tree represen ng
a cogni ve process for analysing sex. A ‘no’ indicates no further
analysis is necessary. A ‘yes’ suggests the next step that should beconsidered (Tannenbaum et al., 2019).
4.1.5 Gender and Science Policy
Policies are drivers of discovery and innova on that can enable sex andgender analysis in science and technology. To push forward rigorous
gender analysis, interlocking policies need to be implemented bythree pillars of academic research: funding agencies, peer-reviewed
journals, and universi es. The key decision tree for gender science
policy is shown in Figure 3 (Bahamonde et al., 2016). Governmentled funding agencies have taken the lead by asking applicants to
explain how sex and gender analysis is relevant to their proposed
research, or to explain that it is not.

Policies
Fundingagencies

Policies
Universi es

Evaluators

Peer-reviewed
journalsResearchers

Development of methods of sex and gender analysis

Greater rigour, reproducibility, inclusion and transparency in research

Source: Bahamonde et al., (2016).
Fig. 4: Three pillars of science and engineering infrastructure.

28 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
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In summary, the following gender gaps are found in AI systems;• Use of logos or symbols• Lack of gender analysis and repor ng (gender inclusion
sta s cs)
• Lack of Gender inclusion plan (experts, recipients, andcomposi on), and• Lack of Capacity building inclusion plan.
4.2 The ra onale for Carbon Footprint Repor ng
Climate change has been occurring since the Industrial Revolu on in
the 1820s (Fursin, 2020). Anthropogenic ac vi es that solely rely on
fossil fuels have exacerbated the rate of Greenhouse gases (GHGs)
and the greenhouse eﬀect on the earth in general. There is increasing
evidence that early and rapid reduc ons in GHGs emissions areneeded to avoid the signiﬁcant impacts of climate change. Moreover,
the Stern report on the Economics of Climate Change provided
evidence that “the beneﬁts of strong and early ac on far outweighthe economic costs of not ac ng” (Miao et al., 2019).
There is a need to reduce the overall carbon emission by tracking
carbon footprints le for each ac vity, process, and way of life.
There are several ways to reduce one’s greenhouse gas footprint:
choosing more energy-eﬃcient ea ng habits, using more energyeﬃcient household appliances, increasing fuel-eﬃcient cars, andsaving electricity (Gahne et al., 2020). Although the presence ofintangible assets complicates calcula ng carbon footprint, consistent
data collec on and exis ng methodologies can aid in ge ng the
projected carbon footprints of both individuals and organiza ons(Loyarte-Lopez et al., 2020)
Responsible carbon footprint repor ng will entail recording all
ac vi es during the project’s en re life cycle to determine theequivalent CO2 emission for all ac vi es. The project life cycle
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29

includes ac vi es in developing, deploying, and MEL process in using
AI in AFS. A er the advent of deep learning (DL), the use of compu ng
power (now computers) has grown exponen ally, doubling every 3.4
months (Amodei and Hernandez 2018), as specialized hardware fortraining large AI models has become the focus of the research ﬁeld
(Hooker 2020).
The increase in power consump on associated with training larger
models and the widespread adop on of AI has been par ally mi gatedby improvements in hardware eﬃciency (Ahmed and Wahed 2020;
Wheeldon et al., 2020). However, depending on where and how
energy is extracted, stored, and distributed, the prolifera on ofcomputa onally intensive AI research could signiﬁcantly nega vely
impact the environment (Lacoste et al., 2019).
The carbon footprint represents the GHG emissions of a device or
ac vity expressed in CO2 equivalent (CO2eq) (Crawford and Joler
2018; Malmodin and Lunden, 2018). The carbon footprint es mateincludes informa on on the carbon emission intensity of electricity
or energy consump on and the eﬀorts to oﬀset the carbon emissionsof the various actors involved in the ac vi es (Ma ew et al., 2008).
Data centers may not be a big deal when repor ng carbon emissions
due to the plethora of artefacts and ac ons embedded in some formof AI and its mul -layered manufacturing processes (Russel 2019).
Data collec on and storage, material produc on and transporta on,
and AI (ML) model training and inference are cri cal elements ofAI’s carbon footprint repor ng. Therefore, these factors, ac vi es,
and processes are all considered in responsible carbon emissions
repor ng. Masanet et al. (2020) argue that to assess AI ac vi es’energy consump on and carbon footprint, it is essen al to dis nguishbetween the two main computa onal phases at the heart of themethods (AlJarrah et al., 2015).

30 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

Recently, several approaches to monitoring and es ma ng GHG
emissions from AI research ac vi es have been proposed. These
include repor ng ﬂoa ng point opera ons (Lacoste et al., 2019;
Schwartz et al., 2019; Henderson et al., 2020), hardware type andhardware load, or “processor mul plied by speed” and computa onme” (Thompson et al., 2020, 10), the data centre used when training
the model, as well as the power sources that feed the electrical
network (Schwartz et al., 2019; Anthony et al., 2020), number ofexperiments required when building a model (Schwartz et al., 2019;
Strubell et al., 2019), and the length of me a model is trained,
because of emission intensity carbon/carbon can change during theday (Anthony et al., 2020).
Of these approaches, two recent eﬀorts stand out for theirgeneraliza on and/or ease of use, namely Henderson et al., (2020)“Experiment-impact-tracker” and Lacoste et al., (2019) Machines
calculate machine learning emissions.
The ﬁrst approach rests on a comprehensive framework available
on GitHub (Henderson et al. 2020), specifying the relevant data tocollect during and a er model training phases to assess the related
GHG emissions as follows:a) Central processing unit (CPU) and graphics processing unit
(GPU) hardware informa on;b) Experiment start and end mes;c) The energy grid region the experiment is being run in (based on
IP address);d) The average carbon/emission intensity in the energy grid region;e) CPU- and GPU-package power draw;f) Per-process u liza on of CPUs and GPUs;g) GPU performance states;h) Memory usage;i) The real- me CPU frequency (in Hz);j) Real- me carbon intensity.
k) Disk write speed.
Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

31

Responsible AI carbon footprint repor ng will entail providing
informa on on the items listed above, which many authors or
researchers avoid or are not aware of. Using Lacoste et al.’s carbon
impact calculator, there is a need to provide cloud provider
informa on like Microso Azure and the base of the cloud provider- Microso Azure, US -West.
One available tool is the code carbon tool, a lightweight so ware
package that integrates into the python codebase to es mate theCO2 produced to execute the code, and how you can lessen youremissions. The selec on of cloud providers is important in themodel training and deployment. Some cloud providers are locatedin some regions of the world that is not eco-friendly with respect tothe equivalent emission from such providers. The selec on of cloudproviders with low equivalent CO2 emissions would be preferred tothose with high equivalent CO2 emissions.
4.3 Ethical considera on in non-model training-based ac vi es
Other AI development and deployment ac vi es require responsible
behaviour in line with climate mi ga on ac vi es. Alterna ve useof non-high emi ng CO2 transporta on pathways like walking,paperless informa on communica on, virtual workshops andtraining, alterna ve power systems (Solar power systems), andbuilding designs with high natural lightening should be consideredin the selec on of mee ng places to avoid high power consump on.

32 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

5. Problem and Poten al areas ofAgriculture and food system for AI
applica on.
Many problems are facing the AFS sector in Africa that require urgent
interven ons to meet SDG 2 by 2030. This sec on discusses the
problem areas where AI innova ons can be deployed to transform
AFS in Africa. Ben-Ayed and Hanana (2021) iden ﬁed four problem
categories where AI can be applied in AFS to include: preproduc on,
produc on, processing, and distribu on. In the same vein, the
UNCTAD (2017) and FAO (2019) align with the four dimensions offood security: availability, access, use/u liza on, and stability toiden fy the key problem areas where AI can be applied in AFS. Forease of administra on and management, we adopt the UNCTAD and
FAO classiﬁca on to analyse these problem areas.
5.1 Availability
Availability addresses whether or not food is actually or poten allyphysically present, including aspects of produc on, food reserves,
markets and transporta on, and wild foods.
•

Low agricultural produc on and produc vity: Companies
and people in African countries are s ll u lizing rudimentaryand obsolete equipment, tools, and outdated technologies intheir produc on systems thereby leading to low agricultural
Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

33

produc vity, wastage of raw materials and resources, andgenera on of excessive wastes. Agricultural produc on andproduc vity in Africa remain lower than in the rest of the world(Bjornlund et al., 2020). This is a ributed to factors inherent
to Africa and its people, such as climate, soil quality, use of
crude implements, low-value addi on, and pests and diseases.
Breeding ac vi es by classical methods take years to establish
new crop varie es. Farming prac ces and extension service
delivery are s ll rudimentary.
Machine failure can lead to wastage of work-in-process inventory
and loss of produc vity. Produc vity in AFS in much of Africa haslong been a concern, both because of the low levels of land andlabour produc vity across much of the con nent, and because
produc vity increases have been slow. This concern has grownwith the resurgence of interest in agricultural growth in Africa
seen since the 2003 Maputo Declara on (Wiggins, 2015).
Interest in agricultural produc vity coincides with discussions
about overall economic growth across many African countries,
where the welcome news of renewed growth has o en been
tempered by observa ons that economies are growing, but nottransforming. Growth has been largely in primary produc onwith only small changes to the structure of economies andwith correspondingly weak development of manufacturing andhigh-value services. The 2014 African Transforma on Report(ACET, 2014) sees agriculture as an example of slow produc vity
growth, hence the need for an urgent interven on to turn thede of decreasing produc vity to increasing produc vity.
•

Soil degrada on: Gene cally improved crop varie es might
not increase yields if constraints such as low soil fer lity are
not overcome. Fer le soils play a pivotal role in sustaining
agricultural produc vity and thus food security. The focus on

34 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni esinnova ons and technological developments is more on crops
and ﬁgh ng pests and diseases and less on sustainable soil
management prac ces (UNCTAD, 2017). However, healthy
plants grow on healthy soils that are less aﬀected by pests anddiseases. Soil degrada on resul ng from erosion and poor landuse lead to loss of soil fer lity and low agriculture produc vity.
This complex phenomenon also has impacts on the ecosystemservices such as pollina on.
Climate change also impacts nega vely on the soil proper escausing rapid moisture and nutrient loss. In most SSA, soil
degrada on poten ally undermines eﬀorts towards sustainableagricultural produc on and so poses a major threat to the
future of agriculture (Sullivan, 2004). Ever since mankind started
agriculture, soil erosion has been the single largest threat toproduc vity (Sunday et. al., 2012).
Poor soil management and rudimentary farming methods havehighly accelerated soil degrada on hence aﬀec ng the healthof crops and ul mately leading to low yields.
•

Challenges in insect pest and diseases detec on: Insect
pest infesta on and diseases are among the most alarming
problems in the African agricultural system and have led toheavy economic losses (Ngumbi, 2017). There is increasingpressure from re-emerging and emergent pests and diseases
in crop and livestock produc on across Africa. Such emerging
pressures are likely to become more problema c with weather
variability and climate change. Over decades, researchers have
tried to mi gate these menaces by developing computerized
systems that could iden fy ac ve pests and diseases and thus
suggest control measures on me.

Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

35

The use of chemicals has also been prominent over the years
although it has been blamed for the rising number of cancers(Rifai, 2017). According to Bannerjee et al., (2018), signiﬁcant
exper se and experience are required to detect an ailing plant
and to take necessary steps for its recovery.
•

Diﬃcul es in livestock management: Feed shortage, limited
knowledge by farmers in livestock produc on, poor gene c
poten al of indigenous livestock breeds, pests and diseases,
vagaries of weather, and land shortage are the main constraints
aﬀec ng livestock produc on in Africa. Despite the enormous
poten al that livestock produc on holds as a source of food,income, foreign exchange, hides and skin, manure, draught
power, and risk reduc on in mes of crop failure.
Many african farmers and their families s ll suﬀer from nutri onal
deﬁciencies that livestock can quickly provide despite having
livestock. This is partly due to poor management prac ces that
reduce availability, hence the need to adopt modern livestock
management techniques to increase produc vity. With theglobal popula on rising and fewer farmers available to care forlivestock, each farm needs to increase produc vity with limited
resources.

5.2 AccessThis area explores whether or not households have suﬃcient accessto quality, quan ty, and diversity of nutri ous foods.
•
Supply chain ineﬃciencies: Africa’s food supply chains arevulnerable to many disturbances and disrup ons. These include
bio c, abio c, and ins tu onal risk factors. The emergenceof the COVID-19 pandemic, which has not only interrupted
but also tempered with food supply chains, has compoundedthese vulnerabili es. According to FAO (2020), there is not a
36 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni esshortage of agricultural commodi es across the world but
rather a bo leneck of access and logis cs to reach consumers.
With over 60% of the African con nent’s popula on in rural
areas and dependent on smallholder farming, the risk to foodsupply chains, market access, and nutri on especially during
the COVID-19 pandemic is high. There has been lockdowns andrestric on of movements of persons and commodi es within
and among countries. Again, the non-tariﬀ barriers restric ng
the movement of essen al items within Africa demo vate thetenets of the African Con nental Free Trade Area (AfCTA).
Africa’s long supply chains create delays and produce o en gets
spoiled or loses its value. It is es mated that 30 to 50% of fresh
produce is lost through poor handling and limited cold chain
facili es (Nieuwoudt, 2019). This leads to increased prices,passed on to urban centres’ consumers who consume 80%of the food produced. The many intermediaries and limited
technology, create poor visibility in the supply chain, andfarmers rarely know the price of their products before the sale.
5.3 Use/U liza on
It analyses whether or not households are maximizing theconsump on of adequate nutri on and energy determined byknowledge and habits as well as the ability of the human body totake food and convert it.
•

Inadequacies in food processing and safety: Food processing is asigniﬁcant driver of local economies, crea ng supplier linkages
for millions of small-scale farmers and helping elevate rural
incomes. One of Africa’s greatest challenges in AFS development
is the inability to process their primary agricultural products
to get the full premiums in price, create jobs and sustain
their economies. Yet, the few and growing local processors
Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es37

o en have diﬃcul es producing high-quality, aﬀordable,
and nutri ous products that meet food safety standards andregulatory requirements due to a lack of technical and businessknowledge, investment, and technology. Intra-African fooddemand is projected to increase by 178% by 2050 and African
diets are changing with more demand for processed products
and meats as a complement to tradi onal staple crops such asmaize, sorghum, cassava and pulses (Gross, 2020).
•

Poor nutri on and increased incidences of malnutri on:
According to the State of food security and nutri on report,
2020, beyond hunger, a growing number of people have had
to reduce the quan ty and quality of the food they consume(FAO et al., 2020). Two billion people, or 25.9% of the global
popula on, experienced hunger or did not have regular accessto nutri ous and suﬃcient food in 2019. This situa on could
deteriorate further especially with the menacing COVID-19
situa on if immediate ac ons are not taken.
The alarming trends in food and nutri on insecurity contributeto increasing the risk of child malnutri on, as food insecurity
aﬀects diet quality, including the quality of children’s andwomen’s diets, and people’s health in diﬀerent ways. It is not
surprising that the burden of child malnutri on remains a threataround the world. In 2019, 21.3% (144 million) of children
under 5 years of age were es mated to be stunted, 6.9% (47million) were wasted and 5.6% (38.3 million) were overweight,
while at least 340 million children suﬀered from micronutrientdeﬁciencies.
Projec ons for 2030, even without considering a poten al
global recession, serve as an added warning that the current
level of eﬀort is not anywhere near enough to end malnutri on

38 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

in the next decade unless there is signiﬁcant transforma vechange in the AFS.
5.4 Stability
Food stability covers household’s food security at all mes. Stability
issues refer to short-term, medium to long-term instabili es that canbe caused by clima c, economic, social, and poli cal factors.
•

Poor weather monitoring, forecas ng and disaster predic onpreparedness: Africa is among the most impacted by erra cweather and climate change disasters. The capacity to collect,analyse and use climate data has been a major challenge over
the years as governments use obsolete equipment and have
low technical capacity for this purpose.
The challenge of weather forecas ng at the temporal andspa al levels in most African countries is severely aﬀec ng
the predic ve capacity of countries leading to their inability
to adequately prepare for extreme weather events.
There are several cri cal gaps in the process of climate
informa on genera on, processing, and dissemina on (World
Meteorological Organiza on, 2014).
The precise impacts of climate change on AFS vary spa ally, but
two general predic ons are greater variability in agricultural
produc on and possibly a decline in crop produc vity (Schlenker
and Lobell, 2010).

•

The unpredictability of crop and livestock yields: Many African
farmers are unable to accurately predict yields of their crops
and livestock and are therefore not in a posi on to know how
produce will be appor oned for processing, sale, or storage.
Yield varia ons from season to season have led to various
associated problems such as price ﬂuctua ons, post-harvest
Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

39

losses, gluts, and panic sales. The amount of food lost each year
due to postharvest losses and wastage is enough to feed thetotal number of undernourished people globally. In SSA alone,which unfortunately is home to over 230 million people suﬀeringfrom chronic undernourishment, 30-50% of produc on is lostat various points in the value chain (Deloi e and Touche, 2015).
•

Poor use of evidence and data for policy decision-making:African governments have rarely used robust data and research
evidence in policy decision-making due to inadequate capacity
at individual, ins tu onal and systemic levels; poor research
infrastructure; lack of poli cal goodwill, and ineﬀec ve linkages
among research, industry, and government among others.
African countries have been very proac ve in developing
policies based on societal needs but the greatest challenge isthat most of these policies are not based on robust research
evidence and data hence implementa on usually becomesdiﬃcult (Ozor et al., 2014).

5.5

Poten al Areas of Ar ﬁcial Intelligence Applica on in
Agriculture and Food System in Africa

The poten al areas of ar ﬁcial intelligence applica on are discussed
in this sec on according to the problem areas iden ﬁed under thefour dimensions of AFS already described in sec ons 5.1 to sec on
5.4 namely; availability, access, u liza on, and stability.
5.5.1 Poten als of AI in food availability
AI can provide innova ve solu ons to improve produc on systems
and hence produc vity. AI can be applied in precision farming
and predic ve analy cs to increase produc on and produc vity.
AI applica ons and tools have been developed to help farmers
40 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni esreduce inaccuracies and control farm prac ces by guiding water
management, type of crop to be grown, op mum plan ng, crop
monitoring, weeding, pest and disease resistance and control, mely
harves ng, nutri on management, and extension service delivery
among others (Talaviya et. al., 2020).
AI has been used in plant breeding for op mal produc on andforecas ng of crop yields under variable and changing climates. AI
can help analyze historical reasons for failures and combine themwith real- me data such as images from the produc on line to predict
upcoming failures of machines. This increases produc vity and more
eﬃcient u liza on of power and reduces wastage in produc on
systems.
AI algorithms using satellite images can determine the quality ofsoils in a par cular area and the type of crops that can do well insuch areas hence leading to op mal produc vity. AI can support soil
fer lity nutrients mapping, and restora on, monitoring using imaging
through drones, and generate informa on to guide food waste
management such as recycling. The data captured through imaging
by AI-powered devices can support high throughput phenotyping inresearch.
AI-based tools are used for pests and diseases detec on, monitoring,and management through image processing which at the same me
provide big data on pest distribu on and incidence at the landscape,country, and regional levels. AI algorithms can detect the presence ofpests and diseases using satellite images and send alerts to farmers
for the mely implementa on of mi ga on measures. It will bepossible to develop a Low Al tude Remote Sensing (LARS) for therecogni on of early pest infesta on.
AI and computer vision allow farmers to achieve this. They enable
farmers to monitor their animals in real- me and results are forwarded
Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

41

to farmers’ mobile phones. They also assume remote control of the
farm’s feeding, milking, and cleaning systems. They alert farmers on
abnormal behaviour spo ed within their livestock and their herd’s
overall well-being including sani zing the herd’s pens whenevernecessary to minimize the risk of infec ons. Uganda for instance has
embraced AI and ML to detect livestock diseases two days before
they manifest, connect farmers to veterinary oﬃcers remotely andmonitor animal movement to avert the . The innova on, dubbed
Jaguza Luganda, cons tutes a chip with a sensor that is connected
to a radio-frequency iden ﬁca on (RFID) reader, and users’ mobile
phones or computers (Koigi, 2019).
5.5.2 Poten als of AI in Food Access
Nonetheless, AI systems have the poten al to mi gate some ofthese vulnerabili es across supply chains, and thereby improve thestate of food security in Africa. The COVID-19 pandemic for example
accelerated the applica on of technologies such as Food apps, drone
and robot delivery technologies as new ways to get informa on andfood to the consumer through AI technologies. UberEATS is now
incorpora ng AI to make recommenda ons for restaurants andmenu items and looking into the use of drones for their deliveries.
AI allows companies to predict consumer trends and pa erns thereby
helping them stay compe ve within the market by adap ng to
diﬀerent popular waves of various trends and making predic ons
about the market.
E-commerce pla orms such as Twiga Foods have also been developed
and used to market goods and services. They make use of disrup ve
technologies such as AI, IoT, and Blockchain to automate theirdistribu on and market processes.

42 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

5.5.3 Poten als of AI in food Use/U liza on
AI directly impacts food processing and safety in ﬁve diﬀerent ways
namely: sor ng packages and products, food safety compliance,
maintaining cleanliness, developing products, and helping customers
with decision making (Sharma, 2019). AI can maximize output andreduce waste through innova ve sensor-based sor ng machines,
detec ng and removing any types of foreign materials, reac ng tochanges in moisture levels, colours, smells, and tastes of foods, etc.
AI will help inform decisions about ‘cleaning’ to improve sanita on
and reduce the spread of pathogens and toxins in foods. It helps toreduce energy use and waste by advising against transpor ng foodthat may not be used at its des na on.
AI can predict the ingredients from photos in a consumer’s foodjournal. It will help to be er capture food consump on, determine
the nutrients and molecular structure of that food, and predict
health outcomes of those dietary choices. Changes in consumer
preferences are crea ng opportuni es for AI in foods. An example
is a growing demand for plant-based alterna ves to meat protein as
the world moves towards precision nutri on. Consumer acceptable
taste and texture quali es have been achieved through AI.
5.5.4 Poten als of AI in food Stability
AI technologies could play a major role in ﬁxing this problem. AI canenhance weather data collec on and predic on and hence provide
ample me for response. AI can enable farmers to understand theever-changing weather condi ons and be able to precisely plan forfarm enterprises along the value chains thereby reducing losses fromthe vagaries of weather.
Forest oﬃcials can use ML to iden fy which crop species aremost resistant to natural disasters such as droughts, hurricanes,
Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es43

ﬂoods, forest ﬁres, etc. and make recommenda ons for breeding
programmes. IoT sensors can be a ached to various trees and be
used to alert authori es in case of a wildﬁre. This ensures stability in
the AFS. Yield predic on in crops and livestock is very beneﬁcial formarke ng strategies and cost es ma on. ML is an important decision
support tool for crop yield predic on, including suppor ng decisions
on what crops to grow and what to do during the growing seasonof the crops to ensure maximum yields (van Klompenburg et al.,2020). Farmers use drones for livestock surveillance for monitoring
pregnancies (Ben-Ayed, 2021).
Besides aiding in the collec on of data, analyses, and use in decisionmaking, AI can also be used for regulatory purposes. Under thetheory of the limited ra onality model, public policymakers havediﬀerent limita ons for the construc on of public policies, especiallythose that require a high level of complexity due to the large numberof variables that they present (Shaﬀer, 2017).
For this reason, some research has been carried out using AI tools suchas expert systems, case-based reasoning, ar ﬁcial neural networks,
agent-based models, cellular automata, evolu onary algorithms,
and agrarian public policy variables to improve the decision-making
process to help public policymakers (Sanchez, 2020).
AI can also be used in understanding the impact of previous policies andpredict the performance of new ones. ML can help analyze previous
policy ac ons automa cally and at scale by improving computa onal
text analysis. Such applica ons will enable governments to eﬀec vely
leverage AI for the development and stability of the AFS.

44 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

6. Challenges to AI applica on in
Africa’s Agriculture and Food System
There are many challenges aﬀec ng the use of AI in agriculture andfood systems; these are discussed in this sec on.
6.1 Lack of diverse datasets speciﬁc to localized issues
The lack of publicly available datasets contextualized in Africa andAfrica’s agriculture to train algorithms has hampered the use of
precision agriculture tools on the con nent. The dataset contains
images used to train the algorithm. When the algorithm is exposed
to image processing, it can recognize pa erns in the image.
The ability of algorithms to recognize pa erns enables the detec onof plant diseases. However, image processing requires high-resolu on
images for algorithms to manipulate and store informa on. Highquality images and no-cost factors limit access to datasets and limit
algorithm training. In addi on, published datasets do not meet thedemand for specialized datasets that are speciﬁc to local issues.
Therefore, the lack of contextualiza on makes it more diﬃcult for thealgorithm to detect, limi ng the usefulness of datasets in precision
agriculture.
6.2 Privacy concerns
There are privacy concerns arising from the data and images captured
by satellites and drones to train the algorithm. In most countries,
Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es45

privacy breaches from exis ng data protec on legisla on are barriers
to the use of drones and satellites in smart agriculture. These laws
make it diﬃcult to violate the right to privacy and consent without
legal jus ﬁca on.
Approval for the use of drones and satellites is subject to the farmers’ability to prove that their right to privacy has not been violated.
Satellite-based data helps track large farm problems, and drones
support opera onal monitoring. These drones also rely heavily on
training datasets that require copyright-free images.
Copyright laws limit data access for AI, unless content owners assign
a crea ve commons license to use the images, access is restrictedwithout consent.
6.3 Low digital literacy
Farmers may not know how to access or use the available AI
applica ons or how to use these technologies to support precision
agriculture. Technology uptake is usually limited by the level of
educa on and knowledge of the farmer, which aﬀects the ability
to apply the technology at the farmer level. This limita on leads tothe failure of sustainable agriculture. Sustainable agriculture means
mee ng exis ng food needs without compromising environmental
health and economic equity.
By integra ng AI-enabled technology into agriculture, farmers canassess crop sustainability (Walker, 2019). Therefore, if farmers do
not know how to use these technologies, they miss the beneﬁts
of forecas ng and evalua on that help promote agricultural
sustainability. In addi on, the lack of digital literacy leads to theadop on of wrong technology and hence failure of results (UNEP,
2021).

46 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

6.4 Gender gaps in AI in Africa
With the advent of technology, the gap between men and women
is widening, if not worsening. With new tools, for example, a biased
algorithmic iden ﬁca on method for mislabelling, that leads to thealgorithm misclassiﬁca on of women in a par cular profession,
ﬂawed keyword searches in informa on and communica ontechnology (ICT), and sexist terms.
The persistence of inherent stereotypes in Ar ﬁcial Intelligence (AI)including AI assistants with female voices and representa on of AI
in media on a sexist scale has called into ques on the assump onof neutrality of algorithms and the poten al for harm in the future.
While much has been said about the global AI gender gap, the African
AI landscape lies largely unexamined in its contribu on to the gender
gap in AI.
In a report of an experimental study of female representa ves inAfrican AI, based on data collected on the workforce composi onof African-based AI companies and projects; the gap between men
and women in AI in Africa stands at 71-29% compared to the global
gender gap of women in AI which stands at 78-22% as of 2018 (Heiseet al., 2019).
The propor on of female par cipa on per industry diﬀers from
corporate services, healthcare, and agriculture having the smallest
gender gaps and entertainment and environmental conserva on
having the widest.
6.5 Environmental concerns
Greenhouse gases absorb radiant energy in the infrared range toraise the earth’s temperature and enable the greenhouse eﬀect(Pham et al., 2018). Although some emissions are natural, the rate
Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es47

of produc on has increased because of anthropogenic ac vi es.
The primary emi ers of GHGs are fossil fuel use in electricity, heat,transporta on, and using chloroﬂuorocarbon (CFC) products. The
most common GHGs are carbon dioxide (CO2), methane (CH4),
nitrous oxide (N2O), and many ﬂuorinated gases (Radford et al.,2018). Carbon footprint is the numerical quan ty of GHGs gases thata single en ty emits.
The calcula ons can be computed ranging from a single person to
the en re world (Rangan et al., 2020). The sixth annual assessment
report by Intergovernmental Panel on Climate Change (IPCC)
presents key scien ﬁc ﬁndings linking the increase in anthropogenic
GHGs emissions to current climate change (IPCC, 2021). Achieving
the 1.5 °C global temperatures rela ve to pre-industrial levels would
require an immediate reduc on in the emissions of GHGs (Rangan etal., 2020; IPCC, 2021).
Ar ﬁcial intelligence training is an energy-intensive process. New
es mates indicate that training a single AI will produce 284 tonnes of
CO2 emissions. This is ﬁve mes the life me emissions of an averagecar (Coleman et al., 2020).
AI can also streamline logis cs, reduce the materials needed tomake things, and otherwise reduce carbon emissions. For example,
according to a BCG study, AI could reduce global greenhouse gas
emissions by 5% to 10% by 2030, equivalent to a 2.6 to 5.3 gigaton
reduc on (Cook, 2019). AI (in the sense of both training models
and applica ons) can consume enormous amounts of energy andgenerate GHGs emissions (García Mar n et al., 2019; Cai et al., 2020).
Therefore, systema cally and accurately measuring the carbon
footprint of AI is key to responsible AI implementa on.

48 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

7. Opportuni es in Ar ﬁcial
Intelligence for Africa’s Agriculture
and Food Systems
Several opportuni es exist in developing, deploying, and using AI
in Africa’s agricultural and food systems. Some of the challenges
iden ﬁed are the lack of capacity, data, right regulatory policies,
gender gaps, environmental concerns, and the overall enabling
environment, which aﬀects the successful implementa on of AI in
AFS. These challenges provide an opportunity to develop and deploy
homegrown ar ﬁcial intelligence for Africa’s agriculture and foodsystems.
The following sec ons will now focus on the opportunity areas forleapfrogging agriculture and food produc on in Africa using AI. The
capacity to develop, deploy, and scale-up AI involves various areas;
these areas are grouped as tangible resources (data, technology,
basic resources), human capacity (technical skills, business skills),
and intangible resources (inter-departmental synergy, organiza onalchange).
7.1 Availability of data
Based on a recently published study by the MIT Sloan Management
Review, data are considered by managers as one of the key enablers
in leveraging the poten al of AI (Ransbotham et al., 2018). Whileorganiza ons have tradi onally focused on structured data in order
Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es49

to guide business decisions, today’s organiza ons capture a large
diversity of data stemming from mul ple sources and in diﬀerent
formats (Kers ng and Meyer, 2018). In fact, the availability of highquality data is considered cri cal, as it is used to train AI algorithms.
A recent study by Ransbotham et al. (2018), found that pioneeringorganiza ons in AI follow a common understanding within their
management teams which regards data as a corporate asset. The
convergence of big data with AI has emerged as one of the mostimportant developments and is shaping how ﬁrms drive businessvalue from their data resources (Bean, 2017).
When it comes to developing AI applica ons that can deliver value,
the quality of the data that are fed into such algorithms is of great
importance. Since AI systems require massive training data sets,
and applica ons eﬀec vely “learn” from available informa on in amanner similar to the way humans do, there is a high requirement
for large amounts of high-quality data. Hence the opportunity fordata scien sts to be trained to acquire relevant skills in building databanks for Africa’s AI development and deployment.
Universi es and various ins tu ons can reframe their curriculum toincorporate the teaching and storage of big data unique to Africa forthe deployment of AI. Providing such a data bank will ensure that AI
in Agriculture and food systems are responsible.
7.2 Availability of technology
AI technologies also put pressure on organiza ons to invest intechnologies that can quickly process data and run complex algorithms.
Common approaches include the use of GPU-intensive clusters andusing parallel compu ng techniques to deal with the processing
power required (Nurvitadhi et al., 2017). Many organiza ons are also
adop ng cloud-based solu ons to deal with the large cost associatedwith AI infrastructure, while a new market for integrated cloud
50 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

services that allow complex AI methods to be applied through simple
API calls has gained prevalence over the last years (Del Sole, 2018).
There are a lot of tech-investment opportuni es for Africa to develop
homegrown technologies that support the use of AI in agriculture.
This essen ally translates to infrastructure investments being madethrough the whole pipeline from ingest to inference, from storage,transfer through high bandwidth networks, to processing power. The
technological infrastructure is also highly dependent on the type oftechniques that are used, which means that organiza ons can end
up having to invest in several diﬀerent suppor ng technologies.
7.3 Improved infrastructure
Apart from the opportunity in data and the technologicalinfrastructure to support AI, organiza ons and governments need
to be able to provide me and ﬁnancial resources to allow suchini a ves to deliver expected outcomes. As most organiza ons are
just now experimen ng with AI, the vast majority of ini a ves willneed some me to mature before being released and yielding value(Ransbotham et al., 2018). Adding to me requirements, anotherimportant aspect that organiza ons must invest in is providing
adequate ﬁnancial resources to allow AI applica ons to develop.
In a 2017 study by McKinsey, the majority of respondents reported
that less than one-tenth of their digital technology spending was on AI
ini a ves (Chui and Malhorta, 2018). Hence there are opportuni es
to leverage external donors to fund research in AI for Agriculture andfood systems.
7.4

Increase in technical skills

When referring to technical AI skills, we mean those necessary to deal
with the implementa on and realiza on of AI algorithms, manage theinfrastructure to support such ini a ves and introduce and ensure AI
Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es51

applica ons adhere to goals. More speciﬁcally, algorithm developers
are necessary in order to u lize the latest AI research and transform
it into repeatable processes through mathema cal formulas that canbe implemented through hardware and so ware (Specor and Ma,2019). It has been suggested that most careers in technical aspectsof AI will require individuals with a strong background in sta s cs,
probability, predic ons, calculus, algebra, Bayesian algorithms, andlogic. In addi on, a good background in programming, logic, data
structures, language processing, and cogni ve learning theory has
been highlighted as an essen al technical AI skill (Lesgold, 2019).
A recent ar cle in the MIT Sloan Management Review presents
three key roles that will emerge as technical proﬁles in the age of AI:
trainers, explainers, and sustainers (Wison et al., 2017). Trainers areconcerned with teaching AI systems how they should perform, andinclude tasks of helping service chatbots, for instance, iden fy thecomplexi es and subtle es of human communica on. Explainers
bridge the gap between technologists and business managers byproviding clarity regarding the inner workings of AI systems to nontechnical audiences.
Finally, sustainers ensure that AI systems are opera ng as expected andthat any unan cipated consequences are addressed appropriately.
Each of these three roles includes a list of more detailed job func onsthat are already becoming cri cal for contemporary organiza ons.
While these skills are currently scarce in the market, it is argued
that they will gradually become more common, as higher educa on
and online training courses are emerging, making this resource acommodity across ﬁrms over me (Danyluk and Buck, 2019).
Therefore, there is a big opportunity for the private sector, academia,
and government to invest in training trainers. The job opportuni escreated are a source of joy for those trained, especially for thegrowing youth popula on.

52 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni esIncorpora ng powerful AI tools into the day-to-day work of companies
will require individuals who have a thorough understanding oftechnology and business strategy. Con nued progress in AI willbuild on predic ve analy cs and machine learning capabili es, andprofessionals who know methods for gathering extensive data andusing it to plan will have a leg up. These analy cs-savvy individuals are
only becoming more crucial to organiza ons as they use quan ta ve
informa on to ﬁnd business intelligence and func on with greater
agility.
Data-driven skills are vital for management analyst, logis cian, orcomputer and informa on systems manager roles. All these posi ons
and more will have an important part to play in the increasing
reliance on the speed and eﬃciency of AI-assisted decision-making
and implementa on (Zhuang et al., 2017).
Professionals interested in seizing AI-driven business opportuni esshould develop a solid grounding in big-data tools and strategies.
Working toward a business analy cs master’s degree could preparethem to lead the way in implemen ng new technology to meet anorganiza onal need.
Online courses at Villanova University provide an educa on thatemphasizes both the latest in technology and best prac ces forbusiness success. This will assist students to discover how to build
data models, perform structured analysis, and use analy cal data
mining and op miza on methods. With knowledge of how analy cs
empower more innova ve business strategies, graduates are wellposi oned to push forward the next steps of AI applica ons within
their organiza ons (Zhuang et al., 2017).
7.5

Remote agricultural opera ons

AI can help farmers reduce the cost of carrying out and monitoring
farm work for large-scale farming. AI-supported smart tractors andResponsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es53

robo cs(“agribots”) can be deployed to perform certain func ons,e.g., harves ng large volumes of crops, possibly at a higher speed
and volume than human labour would (Suiz-Rubio and Rovira-Mas,
2020). In livestock farming, drones mounted with cameras or sensors
can gather informa on on the number of animals, unusual livestock
movements, and animal health.
7.6

Weather forecas ng

AI may use the data from past weather events to predict future
weather (Dewi e et al., 2021). Weather determines the best me forplan ng, fer lizing, spraying, irriga ng, and harves ng crops (Walker2019).
Hence, forecas ng weather can allow farmers to decide whento carry out agricultural processes to maximize their crop yields.
Predic ng rainfall, for example, can inform farmers’ decisions
regarding irriga on which can prevent them from was ng limited
water resources.
7.7

Enabling policies and ins tu ons

All stakeholders in AI implementa on value chain have an excellent
opportunity to develop and deploy policies and the right ins tu ons
for responsible ar ﬁcial intelligence in agriculture and food systems.
These policies will enable proper gender inclusion and support,
environmental protec on, privacy, and intellectual property
protec on, and improved STEM uptake.
STI policies should be modiﬁed to provide the needed tracked training
in AI. Policymakers have an opportunity to push legisla on on data
jus ce and sector-focused policies.

54 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es7.8

Increased youth and gender support

Responsible AI will assist in promo ng youth and gender diversity
and inclusion. Responsible AI in agriculture and food systems will
mean increased support for women’s par cipa on in STEM as it isthe driver of AI growth. It will give female scien sts and AI leaders inbusiness and society more chances, thus highligh ng opportuni es
for women in AI and removing the nega ve stereotypes about
females not belonging to STEM.
The new genera on of female scien sts is inspired, and talented
women who are a racted to a workforce that exhibits genderinclusivity. It will create mentorship opportuni es with training
programmes, thus building the needed capacity and capability.
Young people are o en more ready and eager to master these newtechnologies and apply them to agriculture to increase produc vityand solve challenges (FAO, 2020). At the same me, thesetechnologies can help demonstrate to youth how agriculture can bea viable and proﬁtable business opportunity, increasing the desire tochoose agriculture-related career paths, in lieu of alterna ves youth
might otherwise be seeking.
Mo vated by the promises of digital tools for the modernisa on of
the agro-food sector despite their limita ons and by the business
opportuni es that their use could generate, many young African
so ware developers and entrepreneurs have joined the digital
agricultural space. However, most of them face serious challenges
to grow their businesses and oﬀering sustainable value to theagricultural sector.
The responsible deployment of AI in Agriculture will solve andempower these youths.

Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

55

8. Conclusion
The ability to enhance the performance and sustainability ofagricultural food produc on systems globally depends on developinga deeper understanding of the linkages between innova on inputs
and outputs and diﬀusion pathways in the sector.
AI can play a key role in helping us understand these diﬀusion pathways
in the agricultural sector and the crucial process of connec ng
African farmers to new markets and new sources of knowledge fromother sectors. It is only then will it be possible to fully leverage thepoten al of agricultural innova on, reverse persistently low levels ofagricultural produc vity, and ensure a sustainable global food supply.
In this regard, AI is an inven on in the methods of inven on thatcan fundamentally increase the produc vity of scien ﬁc R&D andgenerate important spill-overs for the sectors using that knowledge
Ar ﬁcial Intelligence is a viable tool to leapfrog Africa’s Agriculture
and food produc on to feed millions, improve Africa’s economy, andovercome the eﬀects of climate change if applied responsibly.
Responsible AI use in Africa’s agriculture requires cri cal legisla on,
capacity building, removal of gender bias, ethical standards forprac ce, environmentally friendly implementa on, and AI-friendly
policies. Indeed, ar ﬁcial intelligence is vital as farmers ba le theeﬀects of climate change with improved crop yield and weather
forecas ng.

56 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni esAll stakeholders, the academia, government, private sectors, andnon-governmental agencies, must synergise and put up a clear
responsible AI policy and plans. The universi es should develop
innova ve curricula focused on AI development and deployment.
Donor funds meant for sustainable development and deployment ofresponsible AI should be used judiciously in achieving the goals andobjec ves of such funding; this will assist in a rac ng more funding
for AI innova ons.
The success of responsible ar ﬁcial intelligence in agriculture and foodsystems depends on building the required capacity and capability.
Training of more youths in STEM programs, including women, willassist in developing the needed skills that are currently unavailable.
A data bank of all AI experts and an integra on of experts fromsocial science, agriculture, and engineering will posi vely aﬀect theapplica on of responsible AI in agriculture and food systems.
Finally, the responsible development and deployment of ar ﬁcial
intelligence in agriculture and food systems in Africa is the right andviable op on to leapfrog agricultural and food produc on in Africa.

Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

57

9. Recommenda ons for eﬀec ve
development, deployment, andscaling of responsible AI in AFS
The review informs the following recommenda ons ofresponsible ar ﬁcial for Africa’s agriculture and food systems. The
recommenda ons are classiﬁed into four (4) areas, namely: ﬁnancial
support, capacity to develop and deploy AI in agriculture and foodsystems, infrastructure, ins tu onal synergy, and strengthening STI
policyRecommenda on 1: Strengthening the capacity to develop, deploy,and upscale AI in agriculture and food systems
There should be urgent investment in developing the requisite skills fordeveloping and deploying ar ﬁcial intelligence for Africa’s agriculture
and food systems among all stakeholders in the agricultural sector.
This involves building capacity in the educa onal sector throughskill-based curriculum change to adapt to the immediate need inpartnership with relevant private and public sector actors. Training
and capacity building for AI developers are important in successful
AI implementa on in Africa.
Currently, Africa lacks the requisite capacity to develop homegrown
AI for Africa’s Agriculture. This is why the current program by IDRC
and Sida is a step in the right direc on in developing the capacity tocreate and deploy AI in Africa’s agriculture and food systems through58 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni esthe AI4D Africa. The forma on of AI labs and sponsorship of PhDs in
AI are capacity-building ac ons that will ensure the deployment ofresponsible AI in Africa’s agriculture and food systems. There should
be an ac ve drive in data acquisi on relevant to Africa’s environment
to ensure just applica on of data in line with the six core pillars ofdata jus ce to increase the capacity to deploy AI in agriculture andfood systems. African governments should establish an inclusive andprotected data bank with crop, animal, soil, weather, and market
informa on.
Eﬃcient energy supply systems are important in developing arobust AI system. Member countries in Africa should support theestablishment of eﬃcient and sustainable energy supply to enhance
technological development. Hence, it is recommended that there is aneed to build the capacity of AI designers and developers to acquire
the technical skills required for AI deployment.
Addi onally, capacity in the area of informa on on Africa’s agriculturallandscape to inform responsible AI development should be built as arepository data bank accessible but not abused.
Recommenda on 2: Strengthening Ins tu onal synergy andcollabora on
Responsible AI deployment needs the collabora on of relevant
ins tu ons; academia, private AI ﬁrms, farmers, government, andprimarily private and public organiza ons. Such synergy will help indeveloping func onal teams with a mix of skills and perspec ves inAfrica. These func onal teams with strong analy cal and technicalexperts will ensure that all ini a ves on responsible development,
deployment, and scale-up of AI in AFS are successfully implemented.
Interdisciplinary teams are the backbone for introducing anddeveloping robust algorithms responsibly. Such teams can develop
and deploy standardized AI designs, enhancing the deployment ofuser-friendly designs with a high adop on rate.
Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
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59

There should be an ac ve drive in data acquisi on relevant to Africa’s
environment to ensure the just applica on of data in line with the six
core pillars of data jus ce.
Recommenda on 3: Increased funding of science, technology andinnova on
Increased investments in agricultural R&D at the con nent andna onal level will support STI. The constantly changing ecological,
environmental, and biodiversity contexts require con nuous researchand development to produce inputs and disseminate knowledge thatmaximizes agricultural yields while safeguarding the environment.
China’s government-sponsored R&D, which increased by 5.5 per centannually between 1995 and 2000 and 15 per cent a er 2000, wasconsidered key to adop ng advanced technologies by poor farmers(UNCTAD, 2015).
Globally, it has been es mated by FAO, IFAD and WFP that eradica ng
hunger by 2030 will require an addi onal $267 billion annually (FAO
et al., 2015). Based on es mates of the United Na ons Environment
Programme green economy models, 0.16 per cent of global GDP
devoted to sustainable agriculture per year ($198 billion between
2011 and 2050) could provide signiﬁcant returns (UNCTAD, 2015).
Hence, member na ons should increase funding or ﬁnancial
support for STI R&D, which will directly aﬀect AI development in the
agricultural sector to enable an increase in food produc on.
Recommenda on 4: Strengthening the STI pro-AI policies in Africa
Policies can be powerful tools for African governments to promotetechnological development by encouraging innova on andinvestment. At the same me, as leading countries have shown,
60 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

government engagement and experimenta on with nascenttechnology can also be a powerful signal of trust and support for
local companies.
According to African stakeholders, low government engagement,
par cularly at the policy level, has been a hindrance, and a stronger
focus will encourage early adop on of AI. African governments shouldtake a proac ve approach and implement AI-friendly regula ons,
policies, and ini a ves.
There are several areas relevant to the development of AI and robust
digital economies where policy-makers should focus, according to
Pillay (2020):•

Data privacy and security — A data privacy and security
framework that individuals can trust encourages and empowers
them to use AI-based solu ons that require their data to work.
Data privacy and security laws should aim to protect users’ data
without restric ng moving data across borders.
In dra ing these laws, African regulators should learn
from interna onal best prac ces, which includes avoiding
burdensome requirements that would foreclose the beneﬁts ofAI and put African companies at a disadvantage.

•

Cybersecurity — African governments should adopt
cybersecurity laws that provide for meaningful deterrence,
incen vise investment, clarify legal responsibili es, and createeﬀec ve and reasonable enforcement mechanisms. Addi onally,
authori es should help users understand and properly manage
the risks inherent in using AI technology.

•

Digital strategies and cloud adop on ini a ves — Governments
should develop na onal digital strategies and policies that foster
widespread cloud adop on to democra se the use of advanced
technologies.
Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es61•Intellectual property — Intellectual property laws that provide
for clear protec on and enforcement against misappropria on
and infringement of technological developments, including
proprietary algorithms, are indispensable to promote con nued
innova on and advancement in AI.

•

Procurement policies — Public procurement regula ons should
enable the use of AI solu ons to provide public services. By
inves ng in public sector innova on, African governments will
demonstrate their trust in AI and support the growth of local
developers.

•

Industry-led standards and interna onal harmonisa on ofrules — IT organisa ons worldwide are developing interna onal
standards to ensure data portability, interoperability, and asmooth data ﬂow. African governments should remain abreastof developments in this area and seek to adopt interna onal
standards and harmonisa on rules as they become applicable.

Hence, Africa must develop the right policy and ins tu onal
landscape to create the enabling environment for the development,
deployment, and use of responsible AI in Africa’s Agriculture and food.
Pro-AI policies drive robust digital infrastructure in higher educa on
ins tu ons, e-learning pla orms, hardware, and broadband.

62 Responsible Ar ﬁcial Intelligence for Africa’s Agriculture and Food Systems:
Challenges and Opportuni es

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