The next bit of my reseach project concerns the leveraging of procurement to achieve ‘AI regulation by contract’ (ie to ensure in the use of AI by the public sector: trustworthiness, safety, explainability, human rights compliance, legality especially in data protection terms, ethical use, etc), so I have been thinking about it for the last few weeks to build on my previous views (see here).
In this post, I summarise my further thoughts — which have been prompted by the rich submissions to the House of Commons Science and Technology Committee [ongoing] inquiry on the ‘Governance of Artificial Intelligence’.
Let’s do it via procurement
As a starting point, it is worth stressing that the (perhaps unsurprising) increasingly generalised position is that procurement has a key role to play in regulating the adoption of digital technologies (and AI in particular) by the public sector—which consolidates procurement’s gatekeeping role in this regulatory space (see here).
More precisely, the generalised view is not that procurement ought to play such a role, but that it can do so (effectively and meaningfully). ‘AI regulation by contract’ via procurement is seen as an (easily?) actionable policy and governance mechanism despite the more generalised reluctance and difficulties in regulating AI through general legislative and policy measures, and in creating adequate governance architectures (more below).
This is very clear in several submissions to the ongoing Parliamentary inquiry (above). Without seeking to be exhaustive (I have read most, but not all submissions yet), the following points have been made in written submissions (liberally grouped by topics):
Procurement as (soft) AI regulation by contract & ‘Market leadership’
‘Procurement processes can act as a form of soft regulation … Government should use its purchasing power in the market to set procurement requirements that ensure private companies developing AI for the public sector address public standards. ’ (Committee on Standards in Public Life, at [25]-[26], emphasis added).
‘For public sector AI projects, two specific strategies could be adopted [to regulate AI use]. The first … is the use of strategic procurement. This approach utilises government funding to drive change in how AI is built and implemented, which can lead to positive spill-over effects in the industry’ (Oxford Internet Institute, at 5, emphasis added).
‘Responsible AI Licences (“RAILs”) utilise the well-established mechanisms of software and technology licensing to promote self-governance within the AI sector. RAILs allow developers, researchers, and companies to publish AI innovations while specifying restrictions on the use of source code, data, and models. These restrictions can refer to high-level restrictions (e.g., prohibiting uses that would discriminate against any individual) as well as application-specific restrictions (e.g., prohibiting the use of a facial recognition system without consent) … The adoption of such licenses for AI systems funded by public procurement and publicly-funded AI research will help support a pro-innovation culture that acknowledges the unique governance challenges posed by emerging AI technologies’ (Trustworthy Autonomous Systems Hub, at 4, emphasis added).
Procurement and AI explainability
‘public bodies will need to consider explainability in the early stages of AI design and development, and during the procurement process, where requirements for transparency could be stipulated in tenders and contracts’ (Committee on Standards in Public Life, at [17], emphasis added).
‘In the absence of strong regulations, the public sector may use strategic procurement to promote equitable and transparent AI … mandating various criteria in procurement announcements and specifying design criteria, including explainability and interpretability requirements. In addition, clear documentation on the function of a proposed AI system, the data used and an explanation of how it works can help. Beyond this, an approved vendor list for AI procurement in the public sector is useful, to which vendors that agree to meet the defined transparency and explainability requirements may be added’ (Oxford Internet Institute, at 2, referring to K McBride et al (2021) ‘Towards a Systematic Understanding on the Challenges of Procuring Artificial Intelligence in the Public Sector’, emphasis added).
Procurement and AI ethics
‘For example, procurement processes should be designed so products and services that facilitate high standards are preferred and companies that prioritise ethical practices are rewarded. As part of the commissioning process, the government should set out the ethical principles expected of companies providing AI services to the public sector. Adherence to ethical standards should be given an appropriate weighting as part of the evaluation process, and companies that show a commitment to them should be scored more highly than those that do not (Committee on Standards in Public Life, at [26], emphasis added).
Procurement and algorithmic transparency
‘… unlike public bodies, the private sector is not bound by the same safeguards – such as the Public Sector Equality Duty within the Equality Act 2010 (EA) – and is able to shield itself from criticisms regarding transparency behind the veil of ‘commercial sensitivity’. In addition to considering the private company’s purpose, AI governance itself must cover the private as well as public sphere, and be regulated to the same, if not a higher standard. This could include strict procurement rules – for example that private companies need to release certain information to the end user/public, and independent auditing of AI systems’ (Liberty, at [20]).
‘… it is important that public sector agencies are duly empowered to inspect the technologies they’re procuring and are not prevented from doing so by the intellectual property rights. Public sector buyers should use their purchasing power to demand access to suppliers’ systems to test and prove their claims about, for example, accuracy and bias’ (BILETA, at 6).
Procurement and technical standards
‘Standards hold an important role in any potential regulatory regime for AI. Standards have the potential to improve transparency and explainability of AI systems to detail data provenance and improve procurement requirements’ (Ada Lovelace Institute, at 10)
‘The speed at which the technology can develop poses a challenge as it is often faster than the development of both regulation and standards. Few mature standards for autonomous systems exist and adoption of emerging standards need to be encouraged through mechanisms such as regulation and procurement, for example by including the requirement to meet certain standards in procurement specification’ (Royal Academy of Engineering, at 8).
Can procurement do it, though?
Implicit in most views about the possibility of using procurement to regulate public sector AI adoption (and to generate broader spillover effects through market-based propagation mechanisms) is an assumption that the public buyer does (or can get to) know and can (fully, or sufficiently) specify the required standards of explainability, transparency, ethical governance, and a myriad other technical requirements (on auditability, documentation, etc) for the use of AI to be in the public interest and fully legally compliant. Or, relatedly, that such standards can (and will) be developed and readily available for the public buyer to effectively refer to and incorporate them into its public contracts.
This is a BIG implicit assumption, at least in relation with non trivial/open-ended proceduralised requirements and in relation to most of the complex issues raised by (advanced) forms of AI deployment. A sobering and persuasive analysis has shown that, at least for some forms of AI (based on neural networks), ‘it appears unlikely that anyone will be able to develop standards to guide development and testing that give us sufficient confidence in the applications’ respect for health and fundamental rights. We can throw risk management systems, monitoring guidelines, and documentation requirements around all we like, but it will not change that simple fact. It may even risk giving us a false sense of confidence’ [H Pouget, ‘The EU’s AI Act Is Barreling Toward AI Standards That Do Not Exist’ (Lawfare.com, 12 Jan 2023)].
Even for less complex AI deployments, the development of standards will be contested and protracted. This not only creates a transient regulatory gap that forces public buyers to ‘figure it out’ by themselves in the meantime, but can well result in a permanent regulatory gap that leaves procurement as the only safeguard (on paper) in the process of AI adoption in the public sector. If more general and specialised processes of standard setting are unlikely to plug that gap quickly or ever, how can public buyers be expected to do otherwise?
seriously, can procurement do it?
Further, as I wrote in my own submission to the Parliamentary inquiry, ‘to effectively regulate by contract, it is at least necessary to have (i) clarity on the content of the obligations to be imposed, (ii) effective enforcement mechanisms, and (iii) public sector capacity to establish, monitor, and enforce those obligations. Given that the aim of regulation by contract would be to ensure that the public sector only adopts trustworthy AI solutions and deploys them in a way that promotes the public interest in compliance with existing standards of protection of fundamental and individual rights, exercising the expected gatekeeping role in this context requires a level of legal, ethical, and digital capability well beyond the requirements of earlier instances of regulation by contract to eg enforce labour standards’ (at [4]).
Even optimistically ignoring the issues above and adopting the presumption that standards will emerge or the public buyer will be able to (eventually) figure it out (so we park requirement (i) for now), and also assuming that the public sector will be able to develop the required level of eg digital capability (so we also park (iii), but see here)), does however not overcome other obstacles to leveraging procurement for ‘AI regulation by contract’. In particular, it does not address the issue of whether there can be effective enforcement mechanisms within the contractual relationship resulting from a procurement process to impose compliance with the required standards (of explainability, transparency, ethical use, non-discrimination, etc).
I approach this issue as the challenge of enforcing not entirely measurable contractual obligations (ie obligations to comply with a contractual standard rather than a contractual rule), and the closest parallel that comes to my mind is the issue of enforcing quality requirements in public contracts, especially in the provision of outsourced or contracted-out public services. This is an issue on which there is a rich literature (on ‘regulation by contract’ or ‘government by contract’).
Quality-related enforcement problems relate to the difficulty of using contract law remedies to address quality shortcomings (other than perhaps price reductions or contractual penalties where those are permissible) that can do little to address the quality issues in themselves. Major quality shortcomings could lead to eg contractual termination, but replacing contractors can be costly and difficult (especially in a technological setting affected by several sources of potential vendor and technology lock in). Other mechanisms, such as leveraging past performance evaluations to eg bar access to future procurements can also do too little too late to control quality within a specific contract.
An illuminating analysis of the ‘problem of quality’ concluded that the ‘structural problem here is that reliable assurance of quality in performance depends ultimately not on contract terms but on trust and non-legal relations. Relations of trust and powerful non-legal sanctions depend upon the establishment of long-term … relations … The need for a governance structure and detailed monitoring in order to achieve co-operation and quality seems to lead towards the creation of conflictual relations between government and external contractors’ [see H Collins, Regulating Contracts (OUP 1999) 314-15].
To me, this raises important questions about the extent to which procurement and public contracts more generally can effectively deliver the expected safeguards and operate as an adequate sytem of ‘AI regulation by contract’. It seems to me that price clawbacks or financial penalties, even debarment decisions, are unilkely to provide an acceptable safety net in some (or most) cases — eg high-risk uses of complex AI. Not least because procurement disputes can take a long time to settle and because the incentives will not always be there to ensure strict enforcement anyway.
More thoughts to come
It seems increasingly clear to me that the expectations around the leveraging of procurement to ‘regulate AI by contract’ need reassessing in view of its likely effectiveness. Such effectiveness is constrained by the rules on the design of tenders for the award of public contracts, as well as those public contracts, and mechanisms to resolve disputes emerging from either tenders or contracts. The effectiveness of this approach is, of course, also constrained by public sector (digital) capability and by the broader difficulties in ascertaining the appropriate approach to (standards-based) AI regulation, which cannot so easily be set aside. I will keep thinking about all this in the process of writing my monograph. If this is of interested, keep an eye on this blog fior further thougths and analysis.
