ARTIFICIAL INTELLIGENCE & RESPONSIBLE
BUSINESS CONDUCTOver the last decade, rapid advancements in artificial intelligence (AI) and machine learning haveopened up new opportunities for productivity, economic development, and advancements invarious sectors, from agriculture to healthcare. While current and future AI applications have thepotential to advance responsible business, they can also pose risks to human rights, theenvironment and other important elements of responsible business conduct as addressed in theOECD Guidelines for multinational enterprises.
For example, AI can unlock significant improvements in occupational health and safety throughautomation of dangerous tasks. The use of AI in smart grids, smart cities and connected devices
can help reduce greenhouse gas emissions and aid in the adaptation to climate change.
On the other hand, the use of AI in hiring, law enforcement, lending, and other fields could leadto discriminatory outcomes through the reliance on inappropriately biased data or algorithms.
Relying on and collecting increasing amounts of personal data, there is a risk of AI adversely
impacting privacy. When used in autonomous weapons systems, AI could lead to impacts on thehuman right to life, personal security, and due process.
This background paper provides an overview of the different types of AI applications, the ways
in which humans and AI can interact, and potential adverse human rights and societal impacts
that AI technology may introduce.

Definition and Overview
The OECD defines an Artificial Intelligence (AI) System as amachine-based system that can, for a given set of humandefined objectives, make predictions, recommendations,or decisions influencing real or virtual environments.1
When applied, AI has seven different use cases, also known
as patterns, that cancoexist in parallel within the same AI system.2
1.
Hyper-personalisation uses AI to develop a profile ofeach individual, and then having that profile learn andadapt over time for a wide variety of purposes including
1 OECD, “Recommendation of the Council on Artificial Intelligence” (2019) Available at:https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449
2 Source of diagram: Cognilytica, “The Seven Patterns of AI”(2019) Available at: https://www.cognilytica.com/2019/04/04/the-seven-patternsof-ai/

displaying relevant content in online search, recommending relevant products, providing
personalized recommendations and so on. Hyper-personalisation is often used intargeted marketing, for example, Netflix suggesting other shows and movies on its
platform based on relevant content the user already watched.3 Another example ofhyper-personalisation is in finance, with the movement away from traditional financial
credit scoring systems, which work by “bucketing” people into credit worthiness scoresbased on an individual’s past data to a hyper-personalised 1:1 credit scoring system using
algorithms that look at the credit histories of individuals with similar characteristics inorder to produce targeted scores for an individual user.
2. Conversation and human interaction use cases involve machines and humans interactingwith each other through conversational content across a variety of methods including
voice, text, and image forms. For example, applications such as Wysa, Joyable andTalkspace use chatbots to provide users with automated mental health care and conduct
mood and intent analysis. Other examples include digital assistants such as Siri and Alexa.
3. Applications using pattern and anomaly detection identify patterns in the data and higher
order connections between data points to see if they fit an existing pattern or if they are
an outlier or anomaly. For example, this type of AI is used in law enforcement to detectfinancial fraud or money laundering, flagging purchases in unusual amounts or locations.4
4. Recognition uses machine learning and other cognitive approaches to identify anddetermine objects or other data points within image, video, audio, text, or other primarily
unstructured data. This type of AI is used in facial recognition to verify someone’s identity,
for example in border control, to monitor the movement of people or to unlock a smart
phone. It can also be used in healthcare, in combination with anomaly detection, toreview pictures of skin conditions and provide potential diagnoses.
5. Goal Driven Systems use machine learning and other cognitive approaches to give
computer systems the ability to learn through trial and error. The primary objective of
these systems is to find the optimal solution to a problem. Use cases include bidding andreal time auctions. This type of AI has also been tested in games, for example when IBM’s
Deep Blue computer beat world champion Gary Kasparov in chess in 1997.5
6. Predictive analytics and decision support are used to understand how past or existing
conditions or behaviours can help predict future outcomes to help humans make better
decisions. An example of this is the application of an AI system to weather forecasting,using data to assess past forecasts and improve predictions over time. With thesealgorithms, humans still make the final decisions about what to do with the AI system’s
3 Forbes, “The Seven Patterns of AI” (2019) Available at: https://www.forbes.com/sites/cognitiveworld/2019/09/17/the-seven-patterns-of-ai/#2e384e2c12d0
4 Forbes, “AI Is Predicting The Future Of Online Fraud Detection” (2019) Available at:https://www.forbes.com/sites/louiscolumbus/2019/08/01/ai-is-predicting-the-future-of-online-fraud-detection/#736abeac74f5
5 Scientific American, “20 Years after Deep Blue: How AI Has Advanced Since Conquering Chess” (2017) Available at:https://www.scientificamerican.com/article/20-years-after-deep-blue-how-ai-has-advanced-since-conquering-chess/2

predictions. This is called augmented intelligence, as opposed to other forms of AI which
are fully autonomous.
7. Autonomous systems are physical and virtual software and hardware systems that areable to accomplish a task, reach a goal, interact with their surroundings, and achieve an
objective with varying degrees of human involvement; they require machine learning
capability that can without further human interaction i receive information about the realor virtual environment , and predict the future behaviour of all elements in the system,and plan for how to deal with those changes. An example of this is autonomous vehicles.

Human Interactions with AI
Humans interact with AI enabled technologies at different levels. Even where an AI system isdesigned to operate autonomously there is a role for humans in the responsible development ofthe system:

We can understand the different levels of human involvement with the example of an AI
application that helps doctors more efficiently diagnose patients. If the app provides augmented
intelligence, it may use hyper-personalisation, predictive analytics and decision support AI
patterns to inform the doctor on the “best” diagnosis, using the patient’s symptoms and medical
history for data, as well as verified medical sources. The app would suggest the most statisticallylikely diagnoses. Based on the doctor’s feedback on correct and incorrect diagnoses the AI system
would learn to improve, becoming more accurate over time. Due to the doctor’s contribution to
how the app will synthesise the data moving forward, the human is in in the loop.
On the other hand, if the app was designed to use autonomous intelligence, it could continuously
review the patient’s profile and intake other new relevant information, adapting along the way
to improve the accuracy of its diagnosis without the help of a doctor. Apart from the design of
the app, humans are not in the loop in this process.

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AI Actor Landscape
The ability of an AI system to augment human decision-making and to make changes
autonomously that impact how an end user interacts with an AI enabled product requires new
approaches to performing human rights due diligence around AI technologies. Different from astandard physical product, where the relationship between the makers, supply chain, retailers
and consumer is linear, there is significant overlap and exchange in the AI landscape between
developers, vendors and end-users. Indeed, there remains a question of assigning responsibility
to different AI actors who either develop, sell, or deploy different technologies. The OECD
defines AI actors as “those who play an active role in the AI system lifecycle, includingorganisations and individuals that deploy or operate AI”.

Source: OECD (2019) AI in Society: AI system lifecycle as defined and approved by the OECD Expert Group on AI (AIGO) in February 2019Landscape of AI Developers, Vendors, and Users

DevelopersDevelopers

Create AI Software, APIS, Component Parts,and/or Enabled Hardware

Data Miners& Providers

Vendors

Developersthat are also
Vendors

Vendors

Sell AI Software, Component Parts, and/or
Enabled Hardware; Includes Open Source Platforms
that facilitate AI Integration solutions

End Users

High Risk
End Users

Governments

Military

Law
Enforcement

PrivateSectorCloud
ComputingPower

Civil Society

Civil Society

PrivateSectorGovernments

Law
Enforcement

Military

An example landscape of different AI actors across an AI system value chain provided as an illustration. It may not include all actors in the valuechain.

4Developers
AI products are created by developers, through the following process:6
1. Ideation: Identifying a problem and priorities for the AI
2. Data Gathering: Selecting the appropriate data set for the AI to learn from
3. Method Selection: Selected the method for teaching the AI what to do with the data
4. Performance Testing: Testing the AI’s ability to perform the task it was taught
While due diligence should cover all stages of the product lifecycle, companies have the greatestamount of leverage during the product development of AI technologies. By applying a “humanrights by design strategy,” developers can mitigate potential risks of technologies at every stepof development. Developer due diligence could include asking questions such as: Who will likely use the product and for what purpose?
 Is there the potential for misuse, poor handling or lack of enforcement of respective rules
and standards?
 Is there a chance that vulnerable groups will be especially impacted by the use of thetechnology?
Vendors
Once a product is developed, it is sold by vendors to end-users, who deploy and operate thetechnology. It is the responsibility of the vendor to conduct RBC due diligence at the point of saleon the risks associated with the use of the product. Importantly, most AI developers also sell theirown products, for example, Microsoft, Apple, IBM, Amazon, and Google.
These companies may be contracted by governments or militaries directly to create specificproducts, and as such, take on extended responsibilities of due diligence. Other companies, suchas Cisco, develop AI products that are distributed by its partners or third-party retailers.
Vendor due diligence could include asking questions such as: Was the product designed and assembled according to RBC standards?
 Is the product being sold directly to the end-user or to another distributor?
 Does the product come with an end-user agreement or training on AI limitations?
End Users
End users can be anyone, ranging from a government, to a government contractor to anothercompany, to an NGO to a member of civil society. When end users are government agencies orgovernment contractors, particularly militaries or private military and security companies, theypresent higher risks of the technology being used for harm and require more stringent due
diligence.

6 Adapted from Data Robot, (2019)“Machine Learning Life Cycle” Available at: https://datarobot.com/wiki/machine-learning-life-cycle/ andBrook, Adrien (2019) Towards Data Science, “10 Steps to Create Your Very Own Corporate AI Project” Available at:https://towardsdatascience.com/10-steps-to-your-very-own-corporate-a-i-project-ced3949faf7f5

For many AI technologies that are licensed to end users, developers have the ability to monitor
the product, creating opportunities for human rights due diligence directly between thedeveloper and the end user. For example, developers and vendors can limit licensing renewals
with end users. End user due diligence could include asking questions such as: Does the product have a dual-use that is harmful?
 Was the product accompanied by guidance or training on its limitations?
 Has the product been altered in any way that may increase its potential RBC risks throughresale channels?

AI and RBC
RBC includes all interfaces between business and society with human rights playing a particularly
important role in the context of AI. The use of AI has the potential to be affiliated or linked withvarious human rights harms, including the following:

Societal harm stemming from the use of AI can be the result of a business or government’s own
activities or can be directly linked to its business operations. As an element of due diligence there6are opportunities for risk mitigation at all stages of AI development. The table below outlines
different types of harm:
TYPE OF HARM

EXAMPLESOPPORTUNITIES FOR RISKMITIGATIONOPEN QUESTIONS

Purposeful“harm by design”

Deepfake video designed toharm
anindividual’sreputation and right toprivacy.

Investments in detection
technology

How can harmful deepfakes be
prevented without curtailing thefreedom of expression?

Harmcaused
byinherent “side effects”

Biased training data leading
to discrimination.
Social media algorithm
promoting hate speech orfalse information.

Risk review during productdevelopment

What checks and balances arenecessary to reduce risk of bias
transfer or new biases in AI
systems?
How should business AI actors
provide greater transparency on
bias or hate speech risks? Whatis the role of governments?

Harm caused by failure
rates

False positives by facialrecognition
inlaw
enforcement

Product development
Customer human rights due
diligence

How can the risks of harm caused
by AI failure be mitigated?
What tools and systems areneeded to ensure appropriate
remediation and ensure “lessonslearnt”?

Harmcausedbyintentional misuse

Political
disinformation
using data / AI systems

Customer and user due
diligence

How should business AI actors
provide greater transparency on
use of AI to spread disinformationor other harms? What is the roleof governments?

Harmcaused
security breach

Hackers taking control ofautonomous vehicles

Product development
Public policy

What is the appropriate level ofcare to ensure security?

byIncreased user transparency

Collaborative Solutions
In response to the way AI is reshaping society, a number of companies, governments, and civilsociety actors are putting in place risk mitigation measures. The most common approach isdeveloping and implementing principles for ethical and rights respecting AI. Companies such as
Google, Workday, TeliaSonera have issued ethical AI principles. Organisations such as the OECD
have put forward guidance, while civil society groups have put forward principles including theToronto Declaration and IEEE Principles. A landscape of ethical and rights based AI guiding
principles that have come out since 2016 is mapped in the chart on the next page.

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To implement ethical AI principles, companies have taken steps including:Conducting human rights impact assessments on emerging technologies
Driving collaboration and dialogue through industry and multi-stakeholder platforms
Putting in place governance structures, such as internal review committees
Advocating for public policy aimed at mitigating human rights risks related to AI

Intel and Microsoft are among the companies that have conducted human rights impact
assessments on emerging technologies. For ongoing risk review, Microsoft has created aninternal governance structure to identify and review risks, known as the AETHER Committee.
Collaborative platforms such as the Partnership on AI have emerged to connect companies withexternal stakeholders, and bridge gaps to implement rights respecting mitigation at all stages ofthe product lifecycle, spanning from development to deployment and including both public andprivate sector actors.
Landscape of AI Ethical & Rights Based Principles*Private Sector

Actor TypeMultiStakeholder

InterGovernmentalGovernment

Civil Society2016

2017

2018

2019

*Adapted from Harvard
Principled Artificial Intelligence

The OECD Directorate for Science, Technology, and Innovation (STI), with the Committee for
Digital Economy Policy, supports governments by measuring and analysing the economic andsocial impacts of AI technologies and applications.

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Further Reading
1. OECD (2019) “Artificial Intelligence in Society”Available at: http://www.oecd.org/goingdigital/artificial-intelligence-in-society-eedfee77-en.htm
2. OECD (2019) “Recommendation of the Council on Artificial Intelligence” :https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449
3. BSR (2018), “Artificial Intelligence: A Rights-Based Blueprint for Business:
https://www.bsr.org/reports/BSR-Artificial-Intelligence-A-Rights-Based-Blueprint-for-BusinessPaper-01.pdf
4. Data & Society, (2018) “Governing Artificial Intelligence: Upholding Human Rights and Dignity” :https://datasociety.net/wpcontent/uploads/2018/10/DataSociety_Governing_Artificial_Intellige
nce_Upholding_Human_Rights.pdf
5. AI for Good Global Summit (2017), “AI For Good Global Summit Report,”: Available at:https://www.itu.int/en/ITUT/AI/Documents/Report/AI_for_Good_Global_Summit_Report_2017.pdf
6. Ada Lovelace Institute (2019), “Ethical and Societal Implications of Algorithms, Data, and AI: aroadmap
forresearch”
Availableat:https://www.nuffieldfoundation.org/sites/default/files/files/Ethical-and-Societal-Implicationsof-Data-and-AI-report-Nuffield-Foundat.pdf
7. Cognilytica(2018),“WhatisArtificiaIIntelligence?”
Availableat:https://www.cognilytica.com/2018/10/08/white-paper-what-is-artificial-intelligence-forconsumer-technology-association/This paper was prepared by the OECD Centre for Responsible Business Conduct in the Directorate for Financial and Enterprise
Affairs in collaboration with Article One Advisors. It was produced with support from the Government of the Netherlands in order
to inform discussions at the OECD workshop on Responsible Business Conduct and Digitalisation taking place on 4 November 2019.
The authors are solely responsible for any remaining errors.
This document is published under the responsibility of the Secretary-General of the OECD. The opinions expressed and arguments
employed herein do not necessarily reflect the official views of OECD member countries. This document, as well as any data andmap included herein, are without prejudice to the status of or sovereignty over any territory, to the delimitation of international
frontiers and boundaries and to the name of any territory, city or area.
© OECD 20199