DISCUSSION PAPER SERIES

IZA DP No. 14021 

Public Procurement and Innovation for 

Human-Centered Artifcial Intelligence 

Wim Naudé 

Nicola Dimitri 

JANUARY 2021 

Electronic copy available at: https://ssrn.com/abstract=3762891 
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DISCUSSION PAPER SERIES

ISSN: 2365-9793

IZA DP No. 14021 

Public Procurement and Innovation for 

Human-Centered Artifcial Intelligence 

Wim Naudé 

University College Cork, RWTH Aachen University and IZA 

Nicola Dimitri 

University of Siena 

JANUARY 2021 

Electronic copy available at: https://ssrn.com/abstract=3762891 
ABSTRACT

IZA DP No. 14021 JANUARY 2021

Public Procurement and Innovation for 

Human-Centered Artifcial Intelligence 

The possible negative consequences of Artificial Intelligence (AI) have given rise to calls for 

public policy to ensure that it is safe, and to prevent improper use and misuse. Humancentered AI (HCAI) draws on ethical principles and puts forth actionable guidelines in this 

regard. So far however, these have lacked strong incentives for adherence. In this paper 

we contribute to the debate on HCAI by arguing that public procurement and innovation 

(PPaI) can be used to incentivize HCAI. We dissect the literature on PPaI and HCAI and 

provide a simple theoretical model to show that procurement of innovative AI solutions 

underpinned by ethical considerations can provide the incentives that scholars have called 

for. Our argument in favor of PPaI for HCAI is also an argument for the more innovative 

use of public procurement, and is consistent with calls for mission-oriented and challengeled innovation policies. Our paper also contributes to the emerging literature on public 

entrepreneurship, given that PPaI for HCAI can advance the transformation of society, but 

only under uncertaint. 

JEL Classification: H57, D02, O38, O32 

Keywords: artificial intelligence, data, innovation, public procurement, 

ethics 

Corresponding author: 

Wim Naudé 

Technology and Innovation Management (TIM) 

RWTH Aachen University 

Kackertstraße 7 

52072 Aachen 

Germany 

E-mail: naude@time.rwth-aachen.de 

Electronic copy available at: https://ssrn.com/abstract=3762891 
1 Introduction

Artificial intelligence (AI) is1 the “simulation of human intelligence processes by machines, especially computer systems.” These processes include learning, reasoning and self-correction.

Although the term Artificial Intelligence (AI) was first used in 1956, when attempts to build

AI was based on computational logic, modern AI is based on a statistical-probabilistic approach and is specialized in narrow domains (Moor, 2006; Naud ́e, 2021). It is also known as

narrow AI, to distinguish it from the not-yet-existing Artificial General Intelligence (AGI)

which would be indistinguishable from human intelligence (Stanford University, 2016). The

use of Machine Learning (ML) and in particular Deep Learning (DL) based on big data

has been particularly successful in generating technologies that have become as good and,

in many cases, better than humans at pattern recognition, prediction and natural language

processing (NLP). As a result, AI models are now routinely and widely used to provide

services and products such as online search engines, chatbots and virtual assistants, recommender systems, reputation systems, news curation and aggregation, hyper-personalization

of marketing, translation, credit scoring, predictive policing and spam filters, amongst others. Progress has also been made in developing autonomous vehicles and medical diagnostic

tools.

Given the ubiquity and growth in data for ML, allowing for AI models to improve their

performance over time, and the wide range of terrains where pattern recognition prediction

and NLP are required, it is clear why there has been high expectations put on AI, and this is

why it has even been described as a new general-purpose technology (Trajtenberg, 2018). The

high expectations of AI are reflected not only in the declarations of scientists and government

officials, but also in the growing investments and research in the field of AI specifically, and

data science more generally. With these expectations have however also come warnings and

concerns about the possible long-term existential threats of AI (Bostrom, 2014) as well as the

shorter-term negative consequences. The latter include intrusive surveillance2 and erosion

of privacy, AI weapons, job losses due to automation, higher inequality, discrimination and

biased policy making (Frey and Osborne, 2017; Korinek and Stiglitz, 2017; Feldstein, 2019;

Russel et al., 2015). The GitHub site “Awful AI” contains a repository of some of the

negative consequences of AI.3

1See www.Whatis.com; Note however that there is no single universal definition of AI (Van de Gevel and

Noussair, 2013).

2As Smith and Neupane (2018, p.25) point out, “AI algorithms supercharge surveillance by processing

data faster than previously possible and detecting patterns too subtle for human analysts to uncover.”

3For Awful AI, see https://github.com/daviddao/awful-ai.

1

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These negative consequences of AI have given rise to calls for proper AI governance4 (Dafoe,

2018) to ensure that AI-based applications are safe, and that the development and diffusion

of AI do not suffer from improper use and misuse. What would count as improper use and

misuse, and safety-risks such as that could be caused by accidents or unintended consequences

of AI systems, would be determined by the extent to which AI systems are human-centered.

Human-centered AI (HCAI) draws on ethical principles and puts forth actionable guidelines

for reducing the risks mentioned (Shneiderman, 2020). As such, HCAI is concerned with

Ethical AI5 and Responsible AI.6

In recent years there have been a growing number of initiatives to elaborate on Ethical and

Responsible AI, see e.g. Boddington (2017), Etzioni and Etzioni (2017), Floridi (2018) and

Yu et al. (2018). These include the Asilomar AI Principles of the Institute of the Future of

Life7 (2017), the European Union’s April 2019 Ethics Guidelines for Trustworthy AI (EC,

2019), the OECD’s May 2019 Principles on Artificial Intelligence (OECD, 2019), the G-20’s

June 2019 Human-Centered AI Principles (G-20, 2019) and the Institute of Electrical and

Electronics Engineers’ Ethically Aligned Design Principles (IEEE, 2019). In addition to these

cross-country and multinational initiatives, the European Union Agency for Fundamental

Rights (FRA) has documented more than 290 AI policy initiatives in individual EU Member

States between 2016 and 2020.8

A shortcoming of these is that they tend to lack strong incentives for developers and users of

AI to adhere to them (Askell et al., 2019; Calo, 2017; Hagendorff, 2020). To incentivize the

ethics for HCAI, Eitel-Porter (2020, p.1) argues for “strong, mandated governance controls

including tools for managing processes and creating associated audit trails.” Floridi et al.

(2018, p.704) argues that governments should “incentivise financially the inclusion of ethical,

legal and social considerations in AI research projects.”

In this paper we contribute to the debate on HCAI by arguing that public procurement and

innovation is a potentially relevant tool with which to incentivize HCAI. We provide a simple

4“The field of AI governance studies how humanity can best navigate the transition to advanced AI

systems, focusing on the political, economic, military, governance, and ethical dimensions” (Dafoe, 2018,

p.5). In 2016, an open letter by an eminent group of scientists ignited the field of AI governance by calling

for “expanded research aimed at ensuring that increasingly capable AI systems are robust and beneficial:

our AI systems must do what we want them to do” - see https://futureoflife.org/ai-open-letter/. 5It is also sometime still referred to as “Machine Ethics”, which has been defined as being “concerned

with ensuring that the behavior of machines toward human users, and perhaps other machines as well, is

ethically acceptable” (Anderson and Anderson, 2007, p.15)

6Responsible AI has been described as AI systems that “have an acceptably low risk of harming their

users or society and, ideally, to increase their likelihood of being socially beneficial” (Askell et al., 2019, p.2).

7See https://futureoflife.org/ai-principles/

8See https://tinyurl.com/y8juagop.

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theoretical model to show that such procurement of innovative AI solutions underpinned by

ethical considerations, can provide both the tools and audit trails as well as the financial

incentives that scholars such as Eitel-Porter (2020) and Floridi et al. (2018) have called for.

With the concept public procurement and innovation (PPaI) we encompass three dimensions

of the relationship between public procurement and innovation (see Obwegeser and M ̈uller

(2018, p.5)). The first is public procurement for innovation (PPfI), which deals with the

question, how can public procurement drive innovation? The second is public procurement

of innovation (PPoI), which deals with the question, how can public services be innovated?

And the third is innovative public procurement (IPP) which deals with the question, how

can public institutions procure innovatively? Our paper is relevant for all three of these

dimensions of innovation and public procurement although we will lay more stress on the

first, namely PPfI, and in particular one of its relatively new tools in the EU, the precommercial procurement of innovation (PCP).

Our argument in favor of public procurement and innovation (PPaI) to advance HCAI is

also an argument for the more innovative use of public procurement. Most often in the

past, public procurement has been used ex post to support new innovations by creating a

demand for the product (being a customer for new products). This is also the way in which

Lin (2020, p.26) conceives of the relationship between public procurement for innovation

support. However, this is a rather restricted view: we argue in this paper that through PPfI

of innovation, governments can also ex ante support research and development of goods

and services that do not yet exist. This potential instrument for steering HCAI has been

neglected in both the AI as well as innovation literature.9 Miller and Lehoux (2020, p.2)

quoting from Uyarra et al. (2017, p.828) confirms that the underlying mechanisms of how

public procurement of innovation can shape the incentives of private agents in innovation is

still “under-theorized.” Our paper contributes by addressing this neglect.

Furthermore, our argument for PPaI as a policy towards HCAI is consistent with the calls for

mission-oriented and challenge-led innovation policies, as made for instance by Mazzucato

(2013, 2018). Mazzucato (2020, p.101) describe challenge-led policies as “policies that use

investment and innovation to solve difficult problems.” Certainly, the issue of HCAI is a

challenge and a difficult problem. It is pre-eminently a challenge facing humanity and requires

challenge-led innovation policies. PPfI and PPoI can fulfil this purpose, as it “uses public

9For instance, Bloom et al. (2019) provides an innovation policy toolkit, discussing eight policy tools:

direct R&D grants, R&D tax credits, patent boxes, skilled immigration, supporting universities’ research,

competition policy, intellectual property rights, and mission-oriented policies. They do not discuss the role

of public procurement of innovation.

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procurement strategically to address a need which cannot be met by conventional solutions”

(Lenderink et al., 2019, p.7).

Finally, our paper also contributes to the emerging literature of public entrepreneurship

(see e.g., Hayter et al. (2018)), in that the use of public procurement of innovation for

HCAI is an example of public entrepreneurship as it entails innovation, it aims to contribute

towards transformation of society, and it is subject to uncertainty. These three elements

– innovation, transformation and uncertainty, are according to Hayter et al. (2018, p.676)

what characterises public entrepreneurship.

The rest of the paper will proceed as follows. In sections 2 and 3 we review the relevant

literature dealing respectively with human-centered AI and innovation policy. In section 4 we

provide a simple theoretical model wherein we show how public procurement of innovation

can incentivize the development of HCAI. Section 5 concludes.

2 Human-Centered Artificial Intelligence

We start by reviewing the state of the literature on HCAI, in particular the role of ethical

principles and actionable guidelines. This literature is part of the broader emerging literature

on AI governance.

The downsides of AI, some of which was mentioned in the introduction, include intrusive

surveillance and erosion of privacy, AI weapons, cybercrime, fake news and misinformation,

job losses and tax losses due to automation, higher inequality, discrimination and biased

policy making (Smith and Neupane, 2018). These are due to the misuse, accidents as well

as systemic risks attached to the use of AI. It is clear that these downsides or risks posed

by AI are of both an intentional nature (as in cybercrime, fake news and AI weapons) or

unintentional nature (as in discrimination, inequality and accidents). In this paper we are

largely concerned with the unintentional harm that AI can cause, and which can be more

broadly analyzed as being a “systemic” or “accident” AI risks (Dafoe, 2018).

Systemic AI risk refers to “the risks of undesired outcomes - some of which may be very

traditional - that can emerge from a system of competing and cooperating agents and can

be amplified by novel forms of AI. For example, AI could increase the risk of inadvertent

nuclear war, not because of an accident or misuse, but because of how AI could rapidly shift

crucial strategic parameters, before we are able to build up compensating understandings,

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norms, and institutions” (Dafoe, 2018, p.28).

One aspect of systemic risk is that AI models can unintentionally result in biased decisions

and recommendations due to being based on biased data (or data with gaps and absences)

as well as design bias, where the values of the designer influences the model design and

functioning. AI models also suffer, due to the nature of ML from lack of transparency

and accountability resulting in the problem that decisions and outcomes from AI models

very often cannot be easily explained. This is also known as the “black-box problem”

(Castelvecchi, 2016; Nguyen et al., 2014). With bias and lack of explainability, “inequality

in application” is a consequence: AI does not benefit everyone equality or fairly (Calo,

2017). Moreover, these shortcomings “disproportionately affect groups that are already

disadvantaged by factors such as race, gender and socio-economic back-ground” (Crawford

and Calo, 2016, p.312).

As far as accident risks are concerned, an AI accident occurs if “a human designer had in mind

a certain (perhaps informally specified) objective or task, but the system that was designed

and deployed for that task produced harmful and unexpected results” (Amodei et al., 2016,

p.2). “Normal” accidents from AI should be expected. According to Maas (2018, p.4) it

even “appears plausible that many AI applications may be even more susceptible to normal

accidents than past ‘textbook’ case technologies such as nuclear power or aviation.” There

are at least three remedies for systemic and accident risks due to AI that have been given

attention in the literature, and that forms part of the challenge of establishing a HCAI.

A first remedy is regulations and laws - to outlaw and police misuse – such as for instance

through data privacy laws, combating cybercrime and other clearly malicious uses of AI

– but also to lay down regulations for improving the safety of AI and limiting accidents.

This remedy may be necessary but not sufficient. As Askell et al. (2019, p.7) remarks, the

difficulties are that “AI regulation seems particularly tricky to get right, as it would require

a detailed understanding of the technology on the part of regulators” and that “regulation

that is reactive and slow may also be insufficient to deal with the challenges raised by AI

systems.”

A second remedy is to improve the safety of AI, and hence limit accidents and unintentional

consequences, through for instance addressing technical issues in design, monitoring and

operation (Maas, 2018; Leike et al., 2017) and by subjecting AI systems to regular monitoring

and redesign (van de Poel, 2020). The problem is that issues of AI technical safety are

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complex,10 and moreover as Leike et al. (2017, p.1) pointed out, “This nascent field of AI

safety still lacks a general consensus on its research problems.”

Thirdly, AI risks – both system and accident risks - can be addressed through the adoption

and adherence to ethical principles and guidelines for the development and use of AI – for

Ethical and Responsible AI. Such principles are needed for combatting the misuse and underuse of AI, for instance by making clear when the use of AI would constitute a fair or unfair

usage, as well as for informing the technical safety of AI, for instance in identifying questions

or problems that the design of an AI should incorporate.11 The requirement of fairness has

led to the concept of “Fair AI,” a subset of Ethical AI, which refers to refers to “systems

that both quantify bias and mitigate discrimination against subgroups” (Feuerriegel et al.,

2020, p.379). HCAI extends beyond merely reducing misuse and negative side-effects and

ensuring fairness. HCAI need to be able to contribute to “a more diverse, fair, and equitable

society” (AI and Inclusion Symposium, 2017, p.2) and encourage and foster the diffusion and

actual use of AI for promotion of societal development (Vinuesa et al., 2020). Thus, ethical

AI will increase trust, and increased use of AI. Safe and ethical AI will also help firms12 to

avoid costly mistakes and so facilitate the uptake and diffusion of AI (Baker-Brunnbauer,

2020). Thus, HCAI approaches aim to establish the societal and commercial requisites for

the greater use of AI (Floridi et al., 2018).

Eitel-Porter (2020, p.3) argue that most proposals for HCAI have similar pillars, namely

fairness,13 accountability, transparency, explainability and privacy. And according to Floridi

et al. (2018, p.689) the ethical principles14 proposed by the AI4People initiative are of beneficence, non-maleficence, autonomy, justice and explicability. In June 2019 the G-20 proposed

five Human-Centered AI Principles (G-20, 2019). These principles require AI to be consistent with (i) inclusive growth, sustainable development and well-being, (ii) human- centered

values and fairness, (iii) transparency and explainability, (iv) robustness, security and safety,

and (v) accountability (G-20, 2019, p.4).

10Everitt et al. (2018) considers the safety challenges involved over the long-term in the case of an AGI.

In this paper we are only concerned with the shorter and medium risks arising from narrow AI.

11Leike et al. (2017) poses eight questions or problems that need to be answered during the design of AI

to ensure that it will be safe for humans, for example “How do we ensure that an agent behaves robustly

when its test environment differs from the training environment?”

12It is no surprise that a growing number of multinational firms have been coming up with their own

ethical AI principles. For example, Baker-Brunnbauer (2020) relates the case of German automotive company

Continental who in June 2020 “announced its intention to develop a code of ethics for its internal development

and usage of AI that is based on the EC Trustworthy AI guideline.”

13See Feuerriegel et al. (2020, pp.381-382) for a discussion of mathematical notions of fairness in AI.

14These are similar, with the exception of explicability, to principles of bioethics (Floridi et al., 2018).

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Although the establishment of widely accepted and agreed on pillars and principles for ethical

and responsible AI is a necessary and important milestone for the development and use of

HCAI, it has a problem. The problem with this third remedy is, as we already stated

in the introduction, that these principles and ethical guidelines lack strong incentives for

developers and users of AI to adhere to them (Askell et al., 2019; Calo, 2017; Hagendorff,

2020). According to Hagendorff (2020, p.99) “Do those ethical guidelines have an actual

impact on human decision-making in the field of AI and machine learning? The short

answer is: No, most often not.” He concludes, from a review of the field of AI ethics that

AI ethics “lacks mechanisms to reinforce its own normative claims.” Calo (2017, p.6-7)

concurred with this assessment, noting that “. . . even assuming moral consensus, ethics lacks

a hard enforcement mechanism. A handful of companies dominate the emerging AI industry.

They are going to prefer ethical standards over binding rules for the obvious reason that no

tangible penalties attach to changing or disregarding ethics should the necessity arise. ” In

fact, AI developers and users may under highly competitive conditions face the incentive to

“underinvest in ensuring their systems are safe” (Askell et al., 2019, p.1). They may “skimp”

on AI ethics also when lured by the potential winner-takes-all effects of eventually inventing

an AGI, as Armstrong et al. (2016) emphasized.

To incentivize the ethics for HCAI, Eitel-Porter (2020, p.1) argues for a strong, mandated

governance controls including tools for managing processes and creating associated audit

trails.” Floridi et al. (2018, p.704) argues that governments should “incentivise financially

the inclusion of ethical, legal and social considerations in AI research projects.” This then,

is a key challenge facing HCAI: how to incentivize adherence to ethical principles? In the

rest of the paper, we argue that the nature of this challenge is such that public procurement

and innovation is very relevant.

In the next section, we argue that the literature on innovation policy suggests that public

procurement can make an important contribution towards steering society towards its goals,

in this case, the goal of a HCAI.

3 Innovation Policy and Artificial Intelligence

In the previous section we came to the conclusion that it is unlikely that HCAI will be

forthcoming automatically from the market. This requires government intervention, and in

particular, through steering the incentives for the private sector to develop and disseminate

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AI models that conform to the ethical and responsibility requirement of HCAI. For this,

innovation policy will be required. In this section we discuss how directional innovation policies may be appropriate and effective, what the contribution of one specific innovation policy

– innovation procurement – may be, and finally how public procurement and innovation, and

specifically public procurement for innovation may be utilised.

3.1 Directional innovation policies and AI

According to Edler and Fagerberg (2017, p.4) “Innovation is understood as the introduction

of new solutions in response to problems, challenges, or opportunities that arise in the social

and/or economic environment.” According to Bloom et al. (2019, pp.167-168) the empirical

evidence from the literature suggests that social returns to innovation “are much higher than

private returns, which provides a justification for government-supported innovation policy.”

More generally, an innovation such as AI generates both positive and negative externalities

and markets do not adequately capture these in market prices. As such there is, as Korinek

(2019, p.4) points out “no theoretical reason to believe that the free market will direct

innovative efforts to the most socially desirable innovations. . . . the market may thus guide

innovation in the wrong direction.” AI innovation will thus not automatically result in HCAI.

The implication is that HCAI is a challenge for innovation policies. Innovation policies are

the “public interventions to support the generation and diffusion of innovation, whereby an

innovation is understood as the transformation of an invention into marketable products

and services, the development of new business processes and methods of organization, and

the absorption, adaptation and dissemination of novel technologies and know-how” (WTO,

2020, p.24).

There are many innovation policy tools aimed at the “generation and diffusion of innovation,”

from both the supply and demand side.15 Tools such as R&D subsidies and tax credits have

typically been described as supply-side tools (Edler and Fagerberg, 2017), whist regulations

and public procurement have been described as demand-side tools (Aschhoff and Sofka,

2009). In recent years there has been subtle shift towards the increasing use of demand-side

tools, in particular as these are seen as being potentially useful for industrial policy purposes

and to facilitate economic restructuring (Crespi and Guarascio, 2019; Edler and Fagerberg,

2017). This growing use is part of what is termed the “directional turn” in innovation policy

– not to encourage innovation for the sake of innovation, but for addressing a pressing societal

15See e.g. Edler and Fagerberg (2017) who presents and discuss 15 major innovation policy instruments.

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need or challenge (Miller and Lehoux, 2020).

Present concerns with directional innovation and industrial policies are based on recognition

that government intervention was in the past important for the development of important

technologies, such as “jet engines, radar, nuclear power, the Global Positioning System

(GPS), and the internet” (Bloom et al., 2019, p.166). Clemens and Rogers (2020) studied

how government procurement influenced innovation in prosthetics during the U.S. Civil

War and World War I, conclude that the nature of government procurement can influence

significantly whether innovation will be on costs or quality – which is a finding with much

relevance for the challenge of HCAI which is primarily a challenge relating to the quality

of the technology. Another oft-quoted example of government steering the development of

new technologies is of the USA’s efforts during the Second World War, which started with

the creation of the National Defense Research Committee (NDRC) in 1940. These efforts

resulted in many impactful new technologies and their diffusion during and after the war,

including radar, mass-produced penicillin, radio communications, and pesticides such as

DDT (Gross and Sampat, 2020). After the Second World War, three notable innovations

where government incentives were important, included the creation of the internet through

the USA’s DARPA-program,16 the creation of modern biomedical research through funding

grants in the UK, and the creation of Google’s search engine algorithm by a government

grant (Lenderink et al., 2019). Interesting discussions of the specific role of government

procurement in the establishment of the computer industry, the semi-conductor industry,

and the US commercial aircraft industry is contained in (Geroski, 1990).

Azoulay et al. (2018, pp.69-70) discusses the USA’s DARPA-program as a catalyst for many

modern-day technologies, not only the internet but also such as the personal computer, lasers

and Microsoft Windows. DARPA’s approach may be argued to be eminently suitable to the

challenge facing the development and diffusion of HCAI. Fouse et al. (2020) discuss how

DARPA, through its various initiatives, had an “outsized” influence on the development of

AI, and continues to do so. For instance, DARPA is at the time of writing reported to be

“investing more than US $2 billion in AI through its initiative, AI Next” (Fouse et al., 2020,

p.4).

As discussed by Azoulay et al. (2018) DARPA’s projects typically are focused on generating

new technological solutions where three characteristics of the technology and markets overlap,

namely 1) there has to be a societal challenge; 2) the technology must be new and on the

16DARPA is the acronym for the USA’s Department of Defense’s “Defense Advanced Research Projects

Agency” which was launched in 1958 and first called the Advanced Research Projects Agency (ARPA)

(Azoulay et al., 2018).

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beginning of the technology S-curve, and 3) there are significant frictions in the markets

for ideas and technology that will hinder a spontaneous market solution. In the case of

HCAI, all three these conditions are present, as it presents a significant societal challenge,

the technology is new (in particular the alignment with human values) and the high degree

to which the technology, being data-based, are subject to knowledge spillover and data

network effects. These three conditions are also amenable to be influenced through public

procurement for and of innovation as we will discuss in greater detail in the next subsection. For instance, public procurement can effectively stimulate from the demand-side the

production of socially desirable technologies (Miller and Lehoux, 2020); it is better suited to

technologies that are still early in the product-life cycle (such as AI) (Geroski, 1990); and it

overcomes knowledge spillover externalities by creating new markets and networks.

In the next sub-section, we discuss the emerging literature on public procurement and innovation.

3.2 Public procurement and innovation

Public procurement refers to “the direct purchase of goods and services by the public sector” (Crespi and Guarascio, 2019, p.783). The potential impact of public procurement is

enormous. Gerdon and Molinari (2020) document for instance that there are more than

250,000 public authorities in the EU alone, who spent more than EUR 2 trillion annually on

procurement.

Public procurement in general, has long since been a tool to promote innovation (and industrialization). Most often in the past, it has been used ex post to support new innovations

by creating a demand for the product (being a customer for new products). This is also

the way in which Lin (2020, p.26) conceives of the relationship between public procurement

for innovation support. However, this is a rather restricted view: through the more targeted approaches of public procurement for and of innovation, governments can also ex ante

support research and development of goods and services that do not yet exist – for present

purposes, HCAI solutions. In this government can fulfil the function of a lead user role in

innovation, as for instance argued by von Hippel (1976), and it can also create new networks

and assist in the diffusion of innovations for instance by helping SMEs to adopt and use AI17

17In the case where the public sector procures a new innovation for its own use, it is termed intrinsic

procurement and when it is procured for use outside of the government, it is termed extrinsic procurement

(Czarnitzki et al., 2020).

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Czarnitzki et al. (2020). There has been a growing interest in recent years by governments to

support innovation through public procurement – the WTO (2020, p.67) documents that 81

percent of OECD countries have adopted initiatives to stimulate innovation through public

procurement.

Public procurement and innovation (PPaI) are, as we explained in the introduction, a term

encompassing three modes of innovation from the perspective of public procurement. As

discussed by Obwegeser and M ̈uller (2018, p.5), the first is public procurement for innovation (PPfI), which deals with the question, how can public procurement drive innovation?

This refers broadly to all public procurement initiatives aimed at innovation. There are

many types and modalities of public procurement for innovation depending on focus, use,

output, and interaction with suppliers (see Lenderink et al. (2019)). A full discussion of the

perturbations and typologies of procurement of and for innovation falls outside the scope of

this paper – the reader is referred to Lenderink et al. (2019) for an extensive overview.

For the present purposes though, public procurement for innovation includes procurement

of products and services that do not yet exist. In the European Union the explicit use

of public procurement in order to procure such products or services is relatively recent

(Czarnitzki et al., 2020). Edler and Georghiou (2007) relate how the public procurement for

innovation, after having been neglected in the EU, got onto the EU’s agenda around 2003.

This eventually resulted in the European Commission’s Handbook on Public Procurement for

Innovation in 2007. One of the innovations in public procurement that the EU introduced

herein was the legal instrument of Pre-Commercial Procurement of innovation (PCP). PCP

“concerns the procurement of R&D services prior to commercialisation, where new solutions

for a specific social need or challenge are developed in competition with risk-benefit sharing

between the public organisation and potential suppliers” (Lenderink et al., 2019, p.8). PCP

is “a competitive and selective” instrument, as firms have to compete and come up with the

best solution on their own (Aschhoff and Sofka, 2009, p.1236), and it “emphasizes the need

for new development of a solution to solve a specific requirement” (Obwegeser and M ̈uller,

2018, p.11).

The second mode of innovation from the perspective of public procurement is public procurement of innovation (PPoI). This deals with the question, how can public services be

innovated? For instance, government departments such as health, education and energy, to

name but a few, could increase efficiency through adopting AI solutions, for instance AI

diagnostic tools for hospitals and energy monitoring systems for reducing emissions from

government buildings. A third mode of innovation from the perspective of public procure11

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ment is innovative public procurement (IPP). It deals with the question how can public

institutions procure innovatively? The demands of steering societal outcomes through public procurement, including to steer advanced technologies such as AI, will require that public

procurement itself be conducted in a more advanced manner – that the public sector innovate itself in how it procures innovation. This is not least due to the complexity of AI and

the need for government agencies to understand the technology, but also due to the need

to continually monitor and upgrade AI technologies, which are never static, a result of AI

models constantly learning and changing behavior as they get exposed to more data.

PPaI have been shown to be an effective tool to steer innovation.18 Czarnitzki et al. (2020)

studies the case of Germany, where the government already since 2009 allowed for public

procurement of innovation. They find from an analysis of 3410 cases, between 2010 to 2012,

that the use of this instrument by the German government “increased turnover with new

products and services in the German business sector by EUR 13 billion in 2012, which represents 0.37% of GDP. Standard procurement tenders without innovation-related components,

by contrast, show no detectable relationship” (Czarnitzki et al., 2020, p.2). Moreover, based

on calculations from German public procurement and R&D spending, they conclude that

“The quantitative potential of public procurement of innovation is about ten times larger

than the amount of public R&D subsidies distributed to the business sector” (Czarnitzki

et al., 2020, p.3). Other interesting studies of public procurement and innovation include

Miller and Lehoux (2020) who study the use of public procurement of innovation in the

Canadian healthcare sector.

We can also report examples of how government procurement is already having an impact on

the development and diffusion of AI. Simonite (2020) reports that in the USA, the Centers

for Medicare and Medicaid Services (CMS) will facilitate the dissemination of particular

AI models for use in medical diagnostics in US hospitals. Through this, the government is

paying the health service to use particular AI algorithms. Beraja et al. (2020) studies the

case of facial recognition AI in China and notes the significant role that the government’s

steering of the technology played. They argue from this that because data is central to AI,

and furthermore that “the state is a key collector of data” as well as that there are economies

of scope from sharing data across firms, that the government is well positioned to stimulate

AI innovation. Specifically, they find that “following the receipt of a government contract

to supply AI software, firms produce more software both for government and commercial

18Geroski (1990, p.189) argues that PPI can be even better than R&D subsidies and tax credits to

steer innovation, because “procurement programmes link the production of innovations to their use” while

“subsidies focus only on the production of innovations.”

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purposes when the contract provides access to more government data” (Beraja et al., 2020,

p.1).

Finally, Stojˇci ́c et al. (2020) argue that public procurement of and for innovation can help

build innovation capability, unlike R&D grants and tax credits which depends on existing

innovation capability. In the novel case of AI ethical embeddedness, the innovation capability

may not be broadly present amongst firms. Therefore, the suitability for public procurement

of and for innovation in the context of steering human-centered AI is particularly appropriate.

3.3 How can public procurement and innovation steer AI?

We have noted on a few occasions in the preceding sub-sections why public procurement

of and for innovation (PPoI, PPfI) can be relevant and appropriate for the steering of AI

towards HCAI. Having established the why of public procurement for and of innovation, we

will deal in the rest of this section with the how. We will in particular focus on the use of

PCP.

For ordering the discussing it is useful to start with the salient features of PCP, and then

indicate what elements the contracting authority should emphasize in the case of steering towards HCAI, where after we will note some additional requirements – innovations

in procurement – that may be particularly useful for the success of PCP with respect to

human-centered AI.

3.3.1 Pre-Commercial Procurement of Innovation

First, we discuss the salient features of PCP as a tool to steer HCAI. Pre-Commercial Procurement (PCP) for innovation is the very first European Union (EU) legal provision, available to the public sector, to steer and procure innovative solutions, which are not available

in the market, and that require R&D activity. As such, it is very relevant to steer innovation

towards human-centered AI. The 2007 PCP Communication was followed by additional, clarifying, documents released to specify its correct interpretation and for its implementation.

Yet the main message has been clear from the very beginning, namely that PCP can be used

to procure R&D services only, needed to develop an innovative solution which is unavailable

in the market. More specifically, PCP can be used to develop prototypes, up to few units

of the final product, to make sure the solution is as desired by the procurer. However, PCP

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cannot be used by a contracting authority (CA) to procure the needed quantity of the final

product. Indeed, to do so, at the end of PCP the CA should follow-up with a commercial

procurement, opening a competitive tendering, or some type of negotiated, procedure within

the legal framework defined by the 2014 EU Public Procurement Directive (EC, 2014). In

such procurement any eligible firm could compete for the commercial contract, and not just

those which participated to the PCP.

The underlying idea, behind the separation of the R&D phase from the commercial procurement phase, is that the European Commission (EC) did not want to restrict the commercial

procurement of potential innovative solutions only to PCP participants. As a matter of fact

if, while a PCP procedure is taking place, other firms in the market developed interesting

new solutions they should be allowed to participate in the commercial procurement of the

final product. This would be compliant with the principle of fair treatment with respect to

potential participants, with the principle of open competition, in the best interest of CA and

of the entire society.

After more than a dozen years from its introduction PCP is gradually diffusing across EU

countries, and AI based solutions are naturally being proposed within such purchasing procedures. A distinguishing feature of PCP is that the initial need is formulated directly by the

public authorities, as much as the US Department of Defence (DoD) does it with DARPA,

which implies that the solutions must satisfy technical but also ethical requirements, in compliance with the mission of the State. For this reason, AI based solutions originated within

the PCP would avoid innovations that are potentially harmful for the society. The 2007

Communication elaborates on the fact that PCP should be founded on a risk sharing principle, between companies and CA, concerning the possibility of project failure. Furthermore,

EC suggests that as an incentive to participate in the PCP, the Intellectual Property Rights

(IPR) behind the new solutions should be left with the companies invited by CA, rather

than being appropriated by CA. Though the reason behind the suggestion is clear, with

some AI based innovative solutions contracting authorities may consider keeping the IPR,

in exchange of a compensation to the companies. This could take place when the contracting authority wants to have under its own control the diffusion and continuous updating of

the new product, which in the case of ethical and responsible AI solutions that it wants to

disseminate to SMEs and update over time (as was shown to be necessary in section 2.1)

would be necessary.

Article 31 of the 2014 Public Procurement Directive the EC introduced an additional legal

provision for procuring innovative solutions, the so called Innovation Partnership (IP). Unlike

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PCP in the IP the contracting authority may not unbundle the procurement of the R&D

phase from the commercial phase, that is the procurement of the desired quantity of the final

product. As a result, currently public officers in EU have two alternative legal instruments to

procure innovative solutions, which have not been developed yet and require R&D activity:

PCP and IP.

3.3.2 Using PCP to steer innovation towards HCAI

In what follows we discuss some of the main elements of PCP and case studies to articulate

how public procurement can steer AI solutions in EU public sector. In the 2007 Communication the EC suggests a model procedure for PCP composed of three phases to implement

the PCP. Contracting Authorities (CA) are by no means mandated to follow such model,

but it can certainly be a very helpful benchmark for them. A PCP is typically preceded by

one, or more, sessions of market consultation. These sessions are publicly announced and

organized by CA to inform all the potentially interested subjects of its needs and of the

problem – e.g., the need for an AI solution that is consistent with certain ethical principles

- that will be the object of the PCP.

Then a call for manifestation of interest to participate in a PCP for an ethical AI solution

is openly announced. After having received the initial manifestation of interest CA can opt

to invite a restricted number of companies for the first phase. In such phase CA asks the

invited companies to propose an initial draft of the would-be final solution. Once received

the proposals, a subset of them will be selected for the second phase where a prototype of

the solution will be presented. Then, a subset of firms which submitted a prototype will

be invited to the third phase, where the selected companies are asked by CA to produce a

few units of the final solution, to check if it works as desired. To prevent the emergence of

dominant/unique solutions, the 2007 Communication suggests that at least two companies

should be invited to the third phase.

As said, if the solution is satisfactory, and the CA interested in procuring a solution such

as the one emerged from the PCP, then after the conclusion of the PCP the contracting

authority will have to open a follow-up procurement procedure, for products which no longer

requires R&D. For this reason, such procedure would fall under the 2014 Public Procurement

Directive, and the CA will now be able to describe precisely in the tender documentation

what it wishes to purchase. Of course, in this case, those companies which were able to

reach the third phase of the PCP will have some advantage, but no certainty to obtain the

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contract. Still in this case, since the procured solution would be innovative, according to the

EC terminology it will be called PPI, that is public procurement of an innovative solution,

which however does not require R&D activity. It is still considered innovation procurement

because it purchases an object which was not in the market yet.

The above short description should clarify how AI based innovative solutions could be driven

in the desired direction by the public sector, in all the PCP phases. From the original formulation of the problem, to the gradual screening and selection of proposals taking place

across the three phases, until PCP ends, CA can navigate the whole process towards solutions which are beneficial for the society. In this navigation process, a number of elements

are particularly relevant for human-centered AI solutions – and some of them may require

innovation in public procurement.

The first is that the CA should from the outset approach the use of PCP for an ethical,

human-centered AI solution with the intention to foster cooperation between firms and research organizations, as well as and foster multi-disciplinary cooperation. According to Askell

et al. (2019, p.1) “competition between AI companies could decrease the incentives of each

company to develop responsibly by increasing their incentives to develop faster. As a result,

if AI companies would prefer to develop AI systems with risk levels that are closer to what is

socially optimal- as we believe many do- responsible AI development can be seen as a collective action problem.” As said, the PCP benchmark is based on a procedure with sequential

selection of companies, hence in principle competitive. Yet, despite this, in using PCP, the

CA can design calls for HCAI solutions that requires and facilitates such cooperation. Askell

et al. (2019) discuss various ways in which this can be incentivized, for instance by creating

high shared gains from cooperation and penalizing non-cooperation. Finally, if the PCP

model turns out to be unsuitable for convincingly establish cooperative behaviour among

invited firms, then the Innovation Partnership could be used instead as legal instrument.

The second is that the CA should insist on ethical design approaches be followed through all

the stages of the procurement process. In this respect Crawford and Calo (2016) proposed two

broad approaches to the ethical design of AI, which may also be applied by a CA using PCP.

The first is to require developers of AI solutions to use frameworks such as value sensitive

design and responsible innovation, and the second is to require a social-systems approach

to building AI, which would entail consideration of “the social and political history of the

data” (Crawford and Calo, 2016, p.313). The latter approach would be in line with the need

for data justice as discussed below.

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The third is that the CA should promote greater openness in AI development – for instance

by requiring open scrutiny of solutions that it purchases and requiring certain degrees of

openness and outside participation in and evaluation of the solutions developed as part of the

procurement action. Bostrom (2017) concludes that over the medium-term, greater openness

in AI development would be unambiguously positive by speeding up the development and

diffusion of AI.19 He is more concerned that over the long run, the openness would discourage

innovation to the extent that the effort may be motivated by having a monopoly in the

market from a proprietary innovation. However, through PPI this long-run disincentive

effect of openness may be compensated for by government not only be reducing the costs

and risks of developing a new AI solution, but also offering a firm the chance to build its

capacity for developing and using AI, and even for a company to benefit in complementary

areas to the AI being procured (Bostrom, 2017).

Fourth, and related to the need for openness, is the requirement that the PCP call specifically

take care of the reliance of AI models on large datasets. Beraja et al. (2020) for instance

distinguish between “data-rich versus data-scarce government contracts.” We want to argue

that PCP contracts for HCAI should be of the data-rich variety. A data-rich PCP contract

should in particular address the data parity problem (Calo, 2017) which refers to the fact

that “a few large firms have disproportionate access to big data necessary to train AI models.

Mostly, small businesses do not have the scale and scope and resources to gather data and

benefit from data network economies. This could mean that machine learning applications

will bend systematically toward the goals of profit-driven companies and not society at large”

(Calo, 2017, p.19).

The data parity problem is mirrored in society as a digital divide, with data gaps, and data

absences creating and perpetuity inequality in access to the use and knowledge of AI, and

in benefiting from AI. In recent times the imperative of addressing these as a requirement

for HCAI has resulted in the notion of data justice, which goes beyond data gaps and

data absences to recognition of structural inequalities. According to Dencik et al. (2019,

p.874) “The framework of data justice broadens the terms of the debate in a way that

accounts for a host of issues that are compounded in the datafied society, as evidenced in

recent scholarship relating to democratic procedures, the entrenchment and introduction of

inequalities, discrimination and exclusion of certain groups, deteriorating working conditions,

or the dehumanisation of decision-making and interaction around sensitive issues. These

19According to Bostrom (2017, p.137) “openness would improve static efficiency, by making products

available at marginal cost (e.g., in the form of open-source software) and allowing a given level of state-ofthe-art technical capability to diffuse more quickly through the economy.”

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discussions suggest a need to position data in a way that engages more explicitly with

questions of power, politics, inclusion and interests, as well as established notions of ethics,

autonomy, trust, accountability, governance and citizenship.” The discussion of data justice

and the relevance of societal concerns about fairness, diversity and equity in the application

of potential general-purpose technologies such as AI reflects the fact that ultimately, AI is

sociotechnical in that the value that it offers “depend on not only technical hardware but

also human behavior and social institutions for their proper functioning” (van de Poel, 2020,

p.7).

Fifth, the contract authority (CA) should make sure that all documentation20 and audits

are captured, as this “is important to provide an audit trail in case of subsequent issues with

the model” (Eitel-Porter, 2020, p.4). This is relevant not only for adjudicating the quality of

the solution offered, but to support efforts to continually review the solution so as to ensure

that “ethical parameters are not breached over time” (Eitel-Porter, 2020, p.5). This is not

so much due to the ethical norms changing, but because of the nature of machine learning

(ML), which adapts and changes as it learns more (gathers more data). The important

point is that ethical AI solutions may not automatically remain ethical given the possibility

of learning, due to AI essentially being an intelligent agent (Riedl, 2019). Anderson and

Anderson (2007) refer to this feature of AI to argue that the ultimate challenge is to create

explicit ethical machines, which are able to learn and formulate “its own ethical choices

based on its own set of principles.”

Having ensured that the above five elements are followed, the CA purchasing an AI solution

would need finally to be able to verify that the prototype solution offered during stage 3

of the PCP process conforms to the specifications set out in the call – in other words, the

technical AI solution needed as well as the requirement that ethical principles are observed.

In this regard, it is important to set up adequate measures for verification. As Brundage

et al. (2020, p.1) stresses “The capacity to verify claims made by developers, on its own,

would be insufficient to ensure responsible AI development. Not all important claims admit

verification, and there is also a need for oversight agencies such as governments and standards

organizations to align developers’ incentives with the public interest.” Values21 in AI models

need to be “intended, embodied, and realized values” (van de Poel, 2020, p.5). For present

purposes, drawing on van de Poel (2020, p.9) we can state that for a procured AI solution to

embody a particular value, denoted by V , it should be the case that at least two conditions

20This should include all documentation including AI impact assessments (Gerdon and Molinari, 2020).

21See the discussion in van de Poel (2020) on the meaning of values and how to assess whether the desired

values are indeed intended, embodied and realized.

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be met: first, the AI must be designed for V , and second that in using the AI solution,

the values V would be promoted. Thus, AI models need to be designed in order to meet a

particular value, for instance human health, and second, utilising the AI model should then

indeed promote that value, e.g. human health.

What measures can be used for verification and assessing whether desired values are intended,

embodied and realized? Brundage et al. (2020) discuss: third party auditing, red teaming

exercises, the piloting of bias and safety bounties, and the sharing of information about

AI accidents. In all of these actions, governments can benefit from having independent

oversight bodies tracking the work of contractors, and subject these to risk-analyses and

public scrutiny. Tzachor et al. (2020) advocates the use of “red teaming” by oversight bodies

to stress test any AI solutions that contractors may come up with, where “red teaming is a

way of challenging the blind spots of a team by explicitly looking for flaws from an outsider

or adversarial perspective” (Tzachor et al., 2020, p.366). According to Tzachor et al. (2020)

one legacy of the COVID-19 pandemic is the realisation that circumstances can require the

application of new AI solutions under extremely short time horizons and with urgency, and

that “doing ethics with urgency” becomes important. Such doing ethics with urgency will

require according to the authors that government should be able to “rapidly conduct robust

testing and verification of systems.” (Ibid, p.366)

Even if these various measures and methods for verification and assessment is successful in

finding that the conditions for the AI solution to embody human-centered values are met, a

further problem is that as was mentioned, AI models may have unintended results. Moreover,

value may change over time. To deal with such unintended results and changes in values,

require continuous involvement and monitoring by the contracting authority. According to

van de Poel (2020) the design of technologies can never remain static, and they may need to

be redesigned at some future stage in light if possible unintended effects as value as possible

shifts in values.

At this stage, once the CA is satisfied with the final prototype of the HCAI solution from

the PCP process, it will have to ensure that the public sector more broadly has in place

complementary mechanisms for dealing with remaining safety risks posed by AI. As we

pointed out in section 2.2.1, normal accidents are one of the salient risks posed by AI. It is

unfortunately the case that the risks of accidents “cannot simply be ‘designed out” of the

technology -at least not without giving up on many of their benefits” (Maas, 2018, p.5). Maas

(2018) concludes that this requires two complementary approaches: one, that governments

ensure that disaster and accident insurance schemes are appropriate to cover AI risks, and

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second that more research be encouraged into AI safety and AI ethics - see also Anderson

and Anderson (2007).

Finally, for the PCP process to be ultimately successful in contributing to innovations in

the field of AI, the government and its contracting authorities will need to upgrade and

continually invest in its own AI expertise (Calo, 2017). With this they could even use

data science and AI techniques within the public sector to explore and test the ethical

consequences of AI. For example, Yu et al. (2018) survey various technical solutions to

support evaluation of ethical concerns in AI. These include useful resources and initiatives

on which the public sector’s scientists and advisors could draw, such as the GenEth ethical

dilemma analyser that can be used to explore ethical dilemmas; the MoralDM tool helping

to resolve ethical dilemmas through first-principles reasoning and analogical reasoning; and

crowdsourcing of self-reported preferences such as through the Moral Machine project.22

4 A Model of Public Procurement and Responsible AI

Innovation

As we outlined in section 2.3, a contracting authority (CA) in the EU could use the provisions

of the EU’s PCP instrument to procure an AI-solution that conforms to high standards of

ethical and responsible AI, as was identified in section 2.1. By issuing an explicit call for

such a solution and offering to fund the R&D costs of the successful company, the public

sector could incentivize the development and diffusion of human-centered AI (HCAI). This

incentive is important, in the absence of which companies may skimp on adequate ethical

and safety standards, as we argued in the introduction and section 2.1.

We can now formalize these ideas. First, we can note that in the absence of the public

sector’s financial incentives, companies may not take the risks to provide the socially optimal

AI solution. Let us suppose that a company X is willing, and has the competence, to develop

an AI solution which could result in a socially desirable, human-centered AI system. The

project requires resources to be invested and success in its development process is intrinsically

uncertain. More specifically, if C > 0 is the amount of money invested in the project by

company X and π(C) the success probability, with π0

(C) ≥ 0 and π00(C) ≤ 0, then typically

0 ≤ π(C) < 1 for any level of C. That is, no matter how large the company’s effort, success

will remain uncertain.

22See https://www.moralmachine.net.

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Here π(C) formalizes the technology available to develop the project and exhibit nonincreasing returns of scale.23 Suppose now that, if development is successful and the project

is commercialized that the company’s revenue would be R. Therefore, its profit would be a

random variable defined as (see Dimitri (2012)):

⎧

⎨R − C with probability π(C) Π(C, R) = (1)

⎩−C with probability 1 − π(C)

and the related expected profit is

EΠ(C, R) = Rπ(C) − C (2)

If the company maximizes (2) then the first order condition is

Rπ0

(C) − 1 = 0 (3)

and the optimal investment level C∗ given by

π0

(C∗) = 1 (4) R

as long as EΠ(C∗, R) is non-negative. Expression (4) immediately suggests that, due to the

concavity of the success probability, the larger the revenue the higher is C∗. However, there

is no guarantee that, regardless of the value of R, that the company would invest at all.

Indeed, a non-negative expected profit requires that

π0

(0) >

1 (5) R

which if not satisfied would discourage the company to undertake any investment.

�  For example, suppose π(C) = a 1 − e−bC with 0 < a < 1 and b > 0. It is immediate

23In reality, several technologies exhibit increasing returns of scale, at least for some investment levels.

However, to keep the exposition simple, with no major loss of generality, in what follows we do not consider

this possibility.

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to see that the larger are both, a and b, the higher is π(C) and so the less challenging is

the development process of the AI project. Interestingly the parameters a and b play two

different roles. Indeed while a = lim π(C), that is it defines the maximum level that success

C→∞

probability can take, b only defines how π(C) changes as C changes. This can be seen by

considering for example the elasticity of the function π(C), namely the percentage change

in π(C) due to 1% change in C, which is given by

π0

(C)C be−bCC = (6) −bC π(C) (1 − e )

As this is independent of a, (5) would become

1 ab > (7) R

which suggests that the company decides to invest if the project is sufficiently rewarding, or

technically not too difficult, or both. In this case the optimal level of investment C∗ would

be

log(abR) C∗ > (8) b

which, consistently with (7), is positive for abR > 1.

However, suppose now that (7) is not satisfied and the company decides not to invest. Failure

to develop the AI project may damage the society and so A could consider supporting

the company, however only if the ethical level of the solution is sufficiently high. Broadly

speaking, suppose that company X has to compete for funding with other companies as per

the EU’s PCP, and that the ethical level of the solution under development, summarized by

the indicator e ≥ 0, is positively related to the probability θ(e) of being funded by A. We

assume θ(e = 0) = 0 if differentiable and θ0

(e) > 0 and that lim θ(e) = 1. Finally, we now e→∞

assume that the R&D total costs for the company are given by C + ce, where ce is total cost

due to reaching the ethical level e and c > 0 is the marginal cost for doing so. Under these

assumptions, in the analysis conducted so far X undertook no, or very low, investment to

take care of the ethical features of the solution under development. Therefore, company X

needs to decide the level of both C and e, and meticulously document e as per the CA’s

requirements.

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⎪⎪

⎪⎪

For this, suppose A and X agree to write the following contract: “if the AI project is funded

by A, with probability θ(e), and successfully developed then A pays to X the sum p while

if the project fails to be developed then A pays to X the sum of q, with p > q. Moreover,

the intellectual property rights will remain with X, which will still enjoy reward R”. As we

shall see below, such admittedly simple contract will induce the company to try developing

a solution that contains consideration of the ethical requirements.

Before proceeding, it is worth noticing that conditional on A funding X the above contract

operates as a mixture of pull (p − q) and push (q) incentives. That is, although we assume

A will only pay at the end of the project, as it may be with the EU’s PCP, q are guaranteed

to X and it is as if they would be paid upfront, hence as a push incentive. For this reason

we could also imagine that should X lack resources for engaging in the process then A may

indeed pay upfront the sum q. Nonetheless, the additional sum p − q will be paid only

conditionally upon success of the project, hence as a pull incentive.

Then the company’s profit Π(C, R, p, q, c, e) is defined as the following random variable:

⎧

⎪(p + R) − C − ce with probability θ(e)π(C + ce)

⎨

Π(C, R, p, q, c, e) = q − C − ce with probability θ(e)(1 − π(C + ce) (9)

⎪⎩0 with probability1 − θ(e)

Therefore, its expected profit will be

⎧

⎨θ(e)π(C + ce)(p + R − q) + θ(e)(q − C − ce) if e > 0

EΠ(C, R, p, q, c, e) = (10)

⎩0 if e = 0

and the optimal level of investment C∗∗ chosen by the firm would now be such that

π0

(C∗∗ 1

+ ce) = (11) (R + p − q)

Condition (11) looks like (4) except that the right-hand side now has the additional term

p − q > 0 at the denominator. This basically increases X’s expected revenues providing a

stronger incentive for the company to invest resources and engage in developing a solution,

provided it is ethically acceptable to A. This will now take place if

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lim π0

(C + ce) = ab >

1 (12) (C+ce)→0 R + p − q

Condition (12) indicates that A could always find a large enough amount p−q such that (5) is

satisfied. Moreover, notice that on the left hand side of (12) we are taking lim π0

(C +ce) (C+ce)→0

because expression (10) is equal to 0 for e = 0, and so π0(0) would not even be defined. Moreover, with an acceptable estimation of π(C), authority A, from (11), could also determine

the effort (C∗∗ + ce) of X.

Considering again the previous example π(C) = a(1 − e−bC), the optimal investment level

for X would now be

log(ab(R + p − q)) C∗∗ + ce = (13) b

log(ab(R+p−q)) If C∗∗ + ce > 0 it is interesting to notice that b < p − q, namely the company will

invest less than the additional incentive term p − q, for all levels of p − q if abR < 1, that

is if the company was not investing at all before A’s intervention. However, if abR > 1 then

the inequality would be true for p − q > z, with z solving log(ab(

b

R+z)) = z.

So, the element p − q will work as a co-funding term for X to engage in the AI project. To

summarize, what is important for A to motivate X to invest more in R&D activities are not

the values of p and q separately, but rather only their difference. Indeed, this is true even if

q = 0, as long as p is large enough.

It is worth stressing that the optimal level of R&D investment obtained from (13) must

include the component ce > 0, needed by X to receive funding from A, which represents the

expenditure undertaken by X to introduce ethical features into the solution. The desirable

level of the indicator e resolves a trade-off between increasing the probability of being funded

by A, as well as of finding the innovative solution, and of increasing the costs. It would be

found by maximising (10) also with respect to e.

To summarise, the above equations describe a simple framework where public procurement

can induce HCAI solutions, by conditioning the funding of a company to the presence of

ethical features, for example in the outcome of a PCP as well as of Innovation Partnership

procedures. This use of public procurement to promote HCAI as we modelled it here, is

complementary to the approach in Naud ́e and Dimitri (2020), who model the use of public

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procurement to reduce the likelihood of arms races for AI.

The mechanism that we outlined above for incentivizing R&D for HCAI is relevant not only

for addressing the general reluctance of companies to invest in HCAI, but more generally

the fact that the concerns about technologies such as AI’s have led to increasing calls in

recent years to make “Technology Impact Assessments” (TIAs) obligatory for entrepreneurs

before implementing and investing in new technologies (Korinek, 2019). This will have

particularly negative effects however, on innovation, over and above the impact of the risks

already illustrated here. It may be worth pointing out that one reason why innovation in the

digital economy have not been declining as innovation elsewhere, is due to the fact that until

now, such innovation has been relatively unencumbered by bureaucratic influence, i.e., it has

been rather more “permissionless.” Rather than risk dis-incentivizing innovation, we want

to argue that PCP, organised for example within a framework such as the one expressed by

equations (9) and (10), can obtain the same results, but through rewarding entrepreneurs

who do perform technology impact assessments. In other words, PCP may reduce the fixed

costs which a requirement such as TIA can cause, by requesting the solution to incorporate

the requested features, possibly co-funding the engaged companies for this.

Finally, given the potential of PCP to steer HCAI as this simple model show, we can conclude

this section by pointing out that although public procurement for innovation as instrument

to steer AI is still relatively neglected, there are already a small sample of PCP AI based

undertaken in EU since 2007 when PCP was launched. It is useful to briefly mention these

projects, and to recommend further research to document the lessons learned from them.

A first example is that during the years 2015-16 a consortium of five hospitals, in different

European countries implemented an EU funded PCP project to develop a highly interoperable, AI based, telemedicine system for the cockpit of intensive care units in hospitals. The

project, called THALEA, was successfully completed and as a result the Dutch Company

called NewCompliance, which developed its solution for the operating room cockpit, was able

to attract venture capital and sell the solutions to several hospitals in EU and in the US.

Moreover, it established partnerships with some major companies in the field to improve the

solution, scaling up its business volume and size.

Among the PCP AI projects which have not been funded by the EU, a very timely and

successful project is the PCP sponsored by the Danish Market Development Fund for selfdriving-robots, based on ultraviolet light, to disinfect hospitals. The project was initially

promoted in 2014, and completed in 2017, by a group of Danish hospitals, hence much earlier

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than 2020 when the COVID-19 pandemic hit the world. As a result of the PCP project,

the Danish company Blue Ocean Robotics attracted millions of dollars in venture capital,

and sold its robots across several countries, to be used in hospitals across the world to help

disinfecting hospitals during the COVID-19 pandemic.

Another interesting example, of a non-EU funded PCP, is an AI-based smart mobility PCP

project developed in the period 2014-2016 and promoted by the Dutch Province of NorthBrabant, to try solving shock-wave traffic jams problems in the area. Traffic jams often occur

when a vehicle suddenly stops, inducing also the following vehicles to suddenly use their

breaks even more strongly, originating traffic jams. As the outcome of PCP, the company

Be-Mobile produced an application merging and analysing real time data on traffic, reported

by various sources, data on vehicles speed, and to advise drivers on which roads/lanes to

take. This innovative solution has been so successful that Be-Mobile was able to meaningfully

scale-up in terms of employers and raised funds.

5 Concluding Remarks

There are high expectations of AI as a general-purpose technology that can contribute towards economic growth, innovation and sustainable development. There are, however, also

concerns about the negative consequences and uses of AI. These negative consequences of

AI have given rise to calls for public policy to ensure that AI-based applications are safe,

and that the development and diffusion of AI does not suffer from improper use and misuse. What would count as improper use and misuse, and safety-risks such as that could

be caused by accidents or unintended consequences of AI systems, would be determined by

the extent to which AI systems are human-centered. Human-centered AI (HCAI) draws on

ethical principles and puts forth actionable guidelines for reducing the risks mentioned. As

such, HCAI is concerned with Ethical AI and Responsible AI.

In recent years there have been a growing number of proposals for Ethical and Responsible

AI. A shortcoming of these is that they lack strong incentives for developers and users of AI to

adhere to them. In this paper, we argued that public procurement and innovation (PPaI) is a

potentially relevant tool with which to incentivize HCAI. Indeed, if the governance of AI deals

with “how decisions are made about AI,” and what “institutions and arrangements could

help in this decision-making (Dafoe, 2018, 2020), then public procurement and innovation

as presented in this paper is a key element of AI governance – however an element that we

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argued is still neglected.

We provided a simple theoretical model to show that the procurement for an innovative AI

solution, underpinned by ethical considerations, can provide both the tools and audit trails

as well as the financial incentives necessary to steer AI towards HCAI. Additionally, we

highlighted five elements that the EU’s PCP instrument should specifically take into account

when aiming to steer HCAI, as well as complementary initiatives, such as supporting more

multi-disciplinary research into ethics and into making ethics computable. This could inform

further innovations in public procurement itself.

In this, we believe our paper made a number of contributions to the literature. First, our

argument and model illustrating that public procurement for innovation can promote HCAI

adds to the literature on innovation policy. This potential instrument for steering HCAI

has been neglected in both the AI as well as innovation literature. Our paper contributed

to addressing this neglect by providing arguments based on an analysis of the literature as

well as providing a theoretical model. Secondly, our paper contributed to the literature on

innovation policy by locating the role of public procurement for innovation of ethical, HCAI

within the recent literature on mission-oriented and challenge-led innovation policies. Finally,

we think our paper also contributed to the emerging literature of public entrepreneurship,

in that the use of public procurement of innovation is an example of public entrepreneurship

as it consists of innovation, it aims to contribute towards transformation of society, and it is

subject to uncertainty.

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Electronic copy available at: https://ssrn.com/abstract=3762891 
Acknowledgements

We are grateful to the participants of various workshops of the Research Unit for the Diffusion of Quality AI at Paderborn University for their comments on earlier versions of this

paper, and in particular to Thomas Gries and Margarete Redlin. The financial assistance of

the Volkswagen Stiftung, through planning grant AZ 97042 from their project on Artificial

Intelligence and the Society of the Future is gratefully acknowledged. The usual disclaimer

applies.

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Electronic copy available at: https://ssrn.com/abstract=3762891 
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