Digital procurement governance: drawing a feasibility boundary

In the current context of generalised quick adoption of digital technologies across the public sector and strategic steers to accelerate the digitalisation of public procurement, decision-makers can be captured by techno hype and the ‘policy irresistibility’ that can ensue from it (as discussed in detail here, as well as here).

To moderate those pressures and guide experimentation towards the successful deployment of digital solutions, decision-makers must reassess the realistic potential of those technologies in the specific context of procurement governance. They must also consider which enabling factors must be put in place to harness the potential of the digital technologies—which primarily relate to an enabling big data architecture (see here). Combined, the data requirements and the contextualised potential of the technologies will help decision-makers draw a feasibility boundary for digital procurement governance, which should inform their decisions.

In a new draft chapter (num 7) for my book project, I draw such a technology-informed feasibility boundary for digital procurement governance. This post provides a summary of my main findings, on which I will welcome any comments: a.sanchez-graells@bristol.ac.uk. The full draft chapter is free to download: A Sanchez-Graells, ‘Revisiting the promise: A feasibility boundary for digital procurement governance’ to be included in A Sanchez-Graells, Digital Technologies and Public Procurement. Gatekeeping and experimentation in digital public governance (OUP, forthcoming). Available at SSRN: https://ssrn.com/abstract=4232973.

Data as the main constraint

It will hardly be surprising to stress again that high quality big data is a pre-requisite for the development and deployment of digital technologies. All digital technologies of potential adoption in procurement governance are data-dependent. Therefore, without adequate data, there is no prospect of successful adoption of the technologies. The difficulties in generating an enabling procurement data architecture are detailed here.

Moreover, new data rules only regulate the capture of data for the future. This means that it will take time for big data to accumulate. Accessing historical data would be a way of building up (big) data and speeding up the development of digital solutions. Moreover, in some contexts, such as in relation with very infrequent types of procurement, or in relation to decisions concerning previous investments and acquisitions, historical data will be particularly relevant (eg to deploy green policies seeking to extend the use life of current assets through programmes of enhanced maintenance or refurbishment; see here). However, there are significant challenges linked to the creation of backward-looking digital databases, not only relating to the cost of digitisation of the information, but also to technical difficulties in ensuring the representativity and adequate labelling of pre-existing information.

An additional issue to consider is that a number of governance-relevant insights can only be extracted from a combination of procurement and other types of data. This can include sources of data on potential conflict of interest (eg family relations, or financial circumstances of individuals involved in decision-making), information on corporate activities and offerings, including detailed information on products, services and means of production (eg in relation with licensing or testing schemes), or information on levels of utilisation of public contracts and satisfaction with the outcomes by those meant to benefit from their implementation (eg users of a public service, or ‘internal’ users within the public administration).

To the extent that the outside sources of information are not digitised, or not in a way that is (easily) compatible or linkable with procurement information, some data-based procurement governance solutions will remain undeliverable. Some developments in digital procurement governance will thus be determined by progress in other policy areas. While there are initiatives to promote the availability of data in those settings (eg the EU’s Data Governance Act, the Guidelines on private sector data sharing, or the Open Data Directive), the voluntariness of many of those mechanisms raises important questions on the likely availability of data required to develop digital solutions.

Overall, there is no guarantee that the data required for the development of some (advanced) digital solutions will be available. A careful analysis of data requirements must thus be a point of concentration for any decision-maker from the very early stages of considering digitalisation projects.

Revised potential of selected digital technologies

Once (or rather, if) that major data hurdle is cleared, the possibilities realistically brought by the functionality of digital technologies need to be embedded in the procurement governance context, which results in the following feasibility boundary for the adoption of those technologies.

Robotic Process Automation (RPA)

RPA can reduce the administrative costs of managing pre-existing digitised and highly structured information in the context of entirely standardised and repetitive phases of the procurement process. RPA can reduce the time invested in gathering and cross-checking information and can thus serve as a basic element of decision-making support. However, RPA cannot increase the volume and type of information being considered (other than in cases where some available information was not being taken into consideration due to eg administrative capacity constraints), and it can hardly be successfully deployed in relation to open-ended or potentially contradictory information points. RPA will also not change or improve the processes themselves (unless they are redesigned with a view to deploying RPA).

This generates a clear feasibility boundary for RPA deployment, which will generally have as its purpose the optimisation of the time available to the procurement workforce to engage in information analysis rather than information sourcing and basic checks. While this can clearly bring operational advantages, it will hardly transform procurement governance.

Machine Learning (ML)

Developing ML solutions will pose major challenges, not only in relation to the underlying data architecture (as above), but also in relation to specific regulatory and governance requirements specific to public procurement. Where the operational management of procurement does not diverge from the equivalent function in the (less regulated) private sector, it will be possible to see the adoption or adaptation of similar ML solutions (eg in relation to category spend management). However, where there are regulatory constraints on the conduct of procurement, the development of ML solutions will be challenging.

For example, the need to ensure the openness and technical neutrality of procurement procedures will limit the possibilities of developing recommender systems other than in pre-procured closed lists or environments based on framework agreements or dynamic purchasing systems underpinned by electronic catalogues. Similarly, the intended use of the recommender system may raise significant legal issues concerning eg the exercise of discretion, which can limit their deployment to areas of information exchange or to merely suggestion-based tasks that could hardly replace current processes and procedures. Given the limited utility (or acceptability) of collective filtering recommender solutions (which is the predominant type in consumer-facing private sector uses, such as Netflix or Amazon), there are also constraints on the generality of content-based recommender systems for procurement applications, both at tenderer and at product/service level. This raises a further feasibility issue, as the functional need to develop a multiplicity of different recommenders not only reopens the issue of data sufficiency and adequacy, but also raises questions of (economic and technical) viability. Recommender systems would mostly only be susceptible of feasible adoption in highly centralised procurement settings. This could create a push for further procurement centralisation that is not neutral from a governance perspective, and that can certainly generate significant competition issues of a similar nature, but perhaps a different order of magnitude, than procurement centralisation in a less digitally advanced setting. This should be carefully considered, as the knock-on effects of the implementation of some ML solutions may only emerge down the line.

Similarly, the development and deployment of chatbots is constrained by specific regulatory issues, such as the need to deploy closed domain chatbots (as opposed to open domain chatbots, ie chatbots connected to the Internet, such as virtual assistants built into smartphones), so that the information they draw from can be controlled and quality assured in line with duties of good administration and other legal requirements concerning the provision of information within tender procedures. Chatbots are suited to types of high-volume information-based queries only. They would have limited applicability in relation to the specific characteristics of any given procurement procedure, as preparing the specific information to be used by the chatbot would be a challenge—with the added functionality of the chatbot being marginal. Chatbots could facilitate access to pre-existing and curated simple information, but their functionality would quickly hit a ceiling as the complexity of the information progressed. Chatbots would only be able to perform at a higher level if they were plugged to a knowledge base created as an expert system. But then, again, in that case their added functionality would be marginal. Ultimately, the practical space for the development of chatbots is limited to low added value information access tasks. Again, while this can clearly bring operational advantages, it will hardly transform procurement governance.

ML could facilitate the development and deployment of ‘advanced’ automated screens, or red flags, which could identify patterns of suspicious behaviour to then be assessed against the applicable rules (eg administrative and criminal law in case of corruption, or competition law, potentially including criminal law, in case of bid rigging) or policies (eg in relation to policy requirements to comply with specific targets in relation to a broad variety of goals). The trade off in this type of implementation is between the potential (accuracy) of the algorithmic screening and legal requirements on the explainability of decision-making (as discussed in detail here). Where the screens were not used solely for policy analysis, but acting on the red flag carried legal consequences (eg fines, or even criminal sanctions), the suitability of specific types of ML solutions (eg unsupervised learning solutions tantamount to a ‘black box’) would be doubtful, challenging, or altogether excluded. In any case, the development of ML screens capable of significantly improving over RPA-based automation of current screens is particularly dependent on the existence of adequate data, which is still proving an insurmountable hurdle in many an intended implementation (as above).

Distributed ledger technology (DLT) systems and smart contracts

Other procurement governance constraints limit the prospects of wholesale adoption of DLT (or blockchain) technologies, other than for relatively limited information management purposes. The public sector can hardly be expected to adopt DLT solutions that are not heavily permissioned, and that do not include significant safeguards to protect sensitive, commercially valuable, and other types of information that cannot be simply put in the public domain. This means that the public sector is only likely to implement highly centralised DLT solutions, with the public sector granting permissions to access and amend the relevant information. While this can still generate some (degrees of) tamper-evidence and permanence of the information management system, the net advantage is likely to be modest when compared to other types of secure information management systems. This can have an important bearing on decisions whether DLT solutions meet cost effectiveness or similar criteria of value for money controlling their piloting and deployment.

The value proposition of DLT solutions could increase if they enabled significant procurement automation through smart contracts. However, there are massive challenges in translating procurement procedures to a strict ‘if/when ... then’ programmable logic, smart contracts have limited capability that is not commensurate with the volumes and complexity of procurement information, and their development would only be justified in contexts where a given smart contract (ie specific programme) could be used in a high number of procurement procedures. This limits its scope of applicability to standardised and simple procurement exercises, which creates a functional overlap with some RPA solutions. Even in those settings, smart contracts would pose structural problems in terms of their irrevocability or automaticity. Moreover, they would be unable to generate off-chain effects, and this would not be easily sorted out even with the inclusion of internet of things (IoT) solutions or software oracles. This comes to largely restrict smart contracts to an information exchange mechanism, which does not significantly increase the value added by DLT plus smart contract solutions for procurement governance.

Conclusion

To conclude, there are significant and difficult to solve hurdles in generating an enabling data architecture, especially for digital technologies that require multiple sources of information or data points regarding several phases of the procurement process. Moreover, the realistic potential of most technologies primarily concerns the automation of tasks not involving data analysis of the exercise of procurement discretion, but rather relatively simple information cross-checks or exchanges. Linking back to the discussion in the earlier broader chapter (see here), the analysis above shows that a feasibility boundary emerges whereby the adoption of digital technologies for procurement governance can make contributions in relation to its information intensity, but not easily in relation to its information complexity, at least not in the short to medium term and not in the absence of a significant improvement of the required enabling data architecture. Perhaps in more direct terms, in the absence of a significant expansion in the collection and curation of data, digital technologies can allow procurement governance to do more of the same or to do it quicker, but it cannot enable better procurement driven by data insights, except in relatively narrow settings. Such settings are characterised by centralisation. Therefore, the deployment of digital technologies can be a further source of pressure towards procurement centralisation, which is not a neutral development in governance terms.

This feasibility boundary should be taken into account in considering potential use cases, as well as serve to moderate the expectations that come with the technologies and that can fuel ‘policy irresistibility’. Further, it should be stressed that those potential advantages do not come without their own additional complexities in terms of new governance risks (eg data and data systems integrity, cybersecurity, skills gaps) and requirements for their mitigation. These will be explored in the next stage of my research project.

Urgent: 'no eForms, no fun' -- getting serious about building a procurement data architecture in the EU

‘EU Member States only have about one year to make crucial decisions that will affect the procurement data architecture of the EU and the likelihood of successful adoption of digital technologies for procurement governance for years or decades to come’. Put like that, the relevance of the approaching deadline for the national implementation of new procurement eForms may grab more attention than the alternative statement that ‘in just about a year, new eForms will be mandatory for publication of procurement notices in TED’.

This latter more technical (obscure, and uninspiring?) understanding of the new eForms seems to have been dominating the approach to eForms implementation, which does not seem to have generally gained a high profile in domestic policy-making at EU Member State level despite the Publications Office’s efforts.

In this post, I reflect about the strategic importance of the eForms implementation for the digitalisation of procurement, the limited incentives for an ambitious implementation that stem from the voluntary approach of the most innovative aspects of the new eForms, and the opportunity that would be lost with a minimalistic approach to compliance with the new rules. I argue that it is urgent for EU Member States to get serious about building a procurement data architecture that facilitates the uptake of digital technologies for procurement governance across the EU, which requires an ambitious implementation of eForms beyond their minimum mandatory requirements.

eForms: some background

The EU is in the process of reforming the exchange of information about procurement procedures. This information exchange is mandated by the EU procurement rules, which regulate a variety of procurement notices with the two-fold objective of (i) fostering cross-border competition for public contracts and (ii) facilitating the oversight of procurement practices by the Member States, both in relation to the specific procedure (eg to enable access to remedies) and from a broad policy perspective (eg through the Single Market Scoreboard). In other words, this information exchange underpins the EU’s approach to procurement transparency, which mainly translates into publication of notices in the Tenders Electronic Daily (TED).

A 2019 Implementing Regulation established new standard forms for the publication of notices in the field of public procurement (eForms). The Implementing Regulation is accompanied by a detailed Implementation Handbook. The transition to eForms is about to hit a crucial milestone with the authorisation for their voluntary use from 14 November 2022, in parallel with the continued use of current forms. Following that, eForms will be mandatory and the only accepted format for publication of TED notices from 25 October 2023. There will thus have been a very long implementation period (of over four years), including an also lengthy (11-month) experimentation period about to start. This contrasts with previous revisions of the TED templates, which had given under six months’ notice (eg in 2015) or even just a 20-day implementation period (eg in 2011). This extended implementation period is reflective of the fact that the transition of eForms is not merely a matter of replacing a set of forms with another.

Indeed, eForms are not solely the new templates for the collection of information to be published in TED. eForms represent the EU’s open standard for publishing public procurement data — or, in other words, the ‘EU OCDS’ (which goes much beyond the OCDS mapping of the current TED forms). The importance of the implementation of a new data standard has been highlighted at strategic level, as this is the cornerstone of the EU’s efforts to improve the availability and quality of procurement data, which remain suboptimal (to say the least) despite continued efforts to improve the quality and (re)usability of TED data.

In that regard, the 2020 European strategy for data, emphasised that ‘Public procurement data are essential to improve transparency and accountability of public spending, fighting corruption and improving spending quality. Public procurement data is spread over several systems in the Member States, made available in different formats and is not easily possible to use for policy purposes in real-time. In many cases, the data quality needs to be improved.’ The European Commission now stresses how ‘eForms are at the core of the digital transformation of public procurement in the EU. Through the use of a common standard and terminology, they can significantly improve the quality and analysis of data’ (emphasis added).

It should thus be clear that the eForms implementation is not only about low level form-filling, but also (or primarily) about building a procurement data architecture that facilitates the uptake of digital technologies for procurement governance across the EU. Therefore, the implementation of eForms and the related data standard seeks to achieve two goals: first, to ensure the data quality (eg standardisation, machine-readability) required to facilitate its automated treatment for the purposes of publication of procurement notices mandated by EU law (ie their primary use); and, second, to build a data architecture that can facilitate the accumulation of big data so that advanced data analytics can be deployed by re-users of procurement data. This second(ary) goal is particularly relevant to our discussion. This requires some unpacking.

The importance of data for the deployment of digital technologies

It is generally accepted that quality (big) data is the primary requirement for the deployment of digital technologies to extract data-driven insights, as well as to automate menial back-office tasks. In a detailed analysis of these technologies, I stress the relevance of procurement data across technological solutions that could be deployed to improve procurement governance. In short, the outcome of robotic process automation (RPA) can only be as good as its sources of information, and adequate machine learning (ML) solutions can only be trained on high-quality big data—which thus conditions the possibility of developing recommender systems, chatbots, or algorithmic screens for procurement monitoring and oversight. Distributed Ledger Technology (DLT) systems (aka blockchain) can manage data, but cannot verify its content, accuracy, or reliability. Internet of Things (IoT) applications and software oracles can automatically capture data, which can alleviate some of the difficulties in generating an adequate data infrastructure. But this is only in relation with the observation of the ‘real world’ or in relation to digitally available information, which quality raises the same issues as other sources of data. In short, all digital technologies are data-centric or, more clearly, data-dependent.

Given the crucial relevance of data across digital technologies, it is hard to emphasise how any shortcomings in the enabling data architecture curtail the likelihood of successful adoption of digital technologies for procurement governance. With inadequate data, it may simply be impossible to develop digital solutions at all. And the development and adoption of digital solutions developed on poor or inadequate data can generate further problems—eg skewing decision-making on the basis of inadequately derived ‘data insights’. Ultimately, then, ensuring that adequate data is available to develop digital governance solutions is a challenging but unavoidable requirement in the process of procurement digitalisation. Success, or lack of it, in the creation of an enabling data architecture will determine the viability of the deployment of digital technologies more generally. From this perspective, the implementation of eForms gains clear strategic importance.

eForms Implementation: a flexible model

Implementing eForms is not an easy task. The migration towards eForms requires a complete redesign of information exchange mechanisms. eForms are designed around universal business language and involve the use of a much more structured information schema, compatible with the EU’s eProcurement Ontology, than the current TED forms. eForms are also meant to collect a larger amount of information than current TED forms, especially in relation to sub-units within a tender, such as lots, or in relation to framework agreements. eForms are meant to be flexible and regularly revised, in particular to add new fields to facilitate data capture in relation to specific EU-mandated requirements in procurement, such as in relation with the clean vehicles rules (with some changes already coming up, likely in November 2022).

From an informational point of view, the main constraint that remains despite the adoption of eForms is that their mandatory content is determined by existing obligations to report and publish tender-specific information under the current EU procurement rules, as well as to meet broader reporting requirements under international and EU law (eg the WTO GPA). This mandatory content is thus rather limited. Ultimately, eForms’ main concentration is on disseminating details of contract opportunities and capturing different aspects of decision-making by the contracting authorities. Given the process-orientedness and transactional focus of the procurement rules, most of the information to be mandatorily captured by the eForms concerns the scope and design of the tender procedure, some aspects concerning the award and formal implementation of the contract, as well as some minimal data points concerning its material outcome—primarily limited to the winning tender. As the Director-General of the Publications Office put it an eForms workshop yesterday, the new eForms will provide information on ‘who buys what, from whom and for what price’. While some of that information (especially in relation to the winning tender) will be reflective of broader market conditions, and while the accumulation of information across procurement procedures can progressively generate a broader view of (some of) the relevant markets, it is worth stressing that eForms are not designed as a tool of market intelligence.

Indeed, eForms do not capture the entirety of information generated by a procurement process and, as mentioned, their mandatory content is rather limited. eForms do include several voluntary or optional fields, and they could be adapted for some voluntary uses, such as in relation to detection of collusion in procurement, or in relation to the beneficial ownership of tenderers and subcontractors. Extensive use of voluntary fields and the development of additional fields and uses could contribute to generating data that enabled the deployment of digital technologies for the purposes of eg market intelligence, integrity checks, or other sorts of (policy-related) analysis. For example, there are voluntary fields in relation to green, social or innovation procurement, which could serve as the basis for data-driven insights into how to maximise the effects of such policy interventions. There are also voluntary fields concerning procurement challenges and disputes, which could facilitate a monitoring of eg areas requiring guidance or training. However, while the eForms are flexible, include voluntary fields, and the schema facilitates the development of additional fields, is it unclear that adequate incentives exist for adoption beyond their mandatory minimum content.

Implementation in two tiers

The fact that eForms are in part mandatory and in part voluntary will most likely result in two separate tiers of eForms implementation across the EU. Tier 1 will solely concern the collection and exchange of information mandated by EU law, that is the minimum mandatory eForm content. Tier 2 will concern the optional collection and exchange of a much larger volume of information concerning eg the entirety of tenders received, as well as qualitative information on eg specific policy goals embedded in a tender process. Of course, in the absence of coordination, a (large) degree of variation within Tier 2 can be expected. Tier 2 is potentially very important for (digital) procurement governance, but there is no guarantee that Member States will decide to implement eForms covering it.

One of the major obstacles to the broad adoption of a procurement data model so far, at least in the European Union, relates to the slow uptake of e-procurement (as discussed eg here). Without an underlying highly automated e-procurement system, the generation and capture of procurement data is a main challenge, as it is a labour-intensive process prone to input error. The entry into force of the eForms rules could serve as a further push for the completion of the transition to e-procurement—at least in relation to procurement covered by EU law (as below thresholds procurement is a voluntary potential use of eForms). However, it is also possible that low e-procurement uptake and generalised unsophisticated approaches to e-procurement (eg reduced automation) will limit the future functionality of eForms, with Member States that have so far lagged behind restricting the use of eForms to tier 1. Non life-cycle (automated) e-procurement systems may require manual inputs into the new eForms (or the databases from which they can draw information) and this implies that there is a direct cost to the implementation of each additional (voluntary) data field. Contracting authorities may not perceive the (potential) advantages of incurring those costs, or may more simply be constrained by their available budget. A collective action problem arises here, as the cost of adding more data to the eForms is to be shouldered by each public buyer, while the ensuing big data would potentially benefit everyone (especially as it will be published—although there are also possibilities to capture but not publish information that should be explored, at least to prevent excessive market transparency; but let’s park that issue for now) and perhaps in particular data re-users offering for pay added-value services.

In direct relation to this, and compounding the (dis)incentives problem, the possibility (or likelihood) of minimal implementation is compounded by the fact that, in many Member States, the operational adaptation to eForms does not directly concern public sector entities, but rather their service providers. e-procurement services providers compete for the provision of large volume, entirely standardised platform services, which are markets characterised by small operational margins. This creates incentives for a minimal adaptation of current e-sending systems and disincentives for the inclusion of added-value (data) services potentially unlikely to be used by public buyers. Some (or most) optional aspects of the eForm implementation will thus remain unused due to these market structure and dynamics, which does not clearly incentivise a race to the top (unless there is clear demand pull for it).

With some more nuance, it should be stressed that it is also possible that the adoption of eForms is uneven within a given jurisdiction where the voluntary character of parts of the eForm is kept (rather than made mandatory across the board through domestic legislation), with advanced procurement entities (eg central purchasing bodies, or large buyers) adopting tier 2 eForms, and (most) other public buyers limiting themselves to tier 1.

Ensuing data fragmentation

While this variety of approaches across the EU and within a Member State would not pose legal challenges, it would have a major effect on the utility of the eForms-generated data for the purposes of eg developing ML solutions, as the data would be fragmented, hardly representative of important aspects of procurement (markets), and could hardly be generalisable. The only consistent data would be that covered by tier 1 (ie mandatory and standardised implementation) and this would limit the potential use cases for the deployment of digital technologies—with some possibly limited to the procurement remit of the specific institutions with tier 2 implementations.

Relatedly, it should be stressed that, despite the effort to harmonise the underlying data architecture and link it to the Procurement Ontology, the Implementation Handbook makes clear that ‘eForms are not an “off the shelf” product that can be implemented only by IT developers. Instead, before developers start working, procurement policy decision-makers have to make a wide range of policy decisions on how eForms should be implemented’ in the different Member States.

This poses an additional challenge from the perspective of data quality (and consistency), as there are many fields to be tailored in the eForms implementation process that can result in significant discrepancies in the underlying understanding or methodology to determine them, in addition to the risk of potential further divergence stemming from the domestic interpretation of very similar requirements. This simply extends to the digital data world the current situation, eg in relation to diverging understandings of what is ‘recyclable’ or what is ‘social value’ and how to measure them. Whenever open-ended concepts are used, the data may be a poor source for comparative and aggregate analysis. Where there are other sources of standardisation or methodology, this issue may be minimised—eg in relation to the green public procurement criteria developed in the EU, if they are properly used. However, where there are no outside or additional sources of harmonisation, it seems that there is scope for quite a few difficult issues in trying to develop digital solutions on top of eForms data, except in relation to quantitative issues or in relation to information structured in clearly defined categories—which will mainly link back to the design of the procurement.

An opportunity about to be lost?

Overall, while the implementation of eForms could in theory build a big data architecture and facilitate the development of ML solutions, there are many challenges ahead and the generalised adoption of tier 2 eForms implementations seems unlikely, unless Member States make a positive decision in the process of national adoption. The importance of an ambitious tier 2 implementation of eForms should be assessed in light of its downstream importance for the potential deployment of digital technologies to extract data-driven insights and to automate parts of the procurement process. A minimalistic implementation of eForms would significantly constrain future possibilities of procurement digitalisation. Primarily in the specific jurisdiction, but also with spillover effects across the EU.

Therefore, a minimalistic eForms implementation approach would perpetuate (most of the) data deficit that prevents effective procurement digitalisation. It would be a short-sighted saving. Moreover, the effects of a ‘middle of the road’ approach should also be considered. A minimalistic implementation with a view to a more ambitious extension down the line could have short-term gains, but would delay the possibility of deploying digital technologies because the gains resulting from the data architecture are not immediate. In most cases, it will be necessary to wait for the accumulation of sufficiently big data. In some cases of infrequent procurement, missing data points will generate further time lags in the extraction of valuable insights. It is no exaggeration that every data point not captured carries an opportunity cost.

If Member States are serious about the digitalisation of public procurement, they will make the most of the coming year to develop tier 2 eForms implementations in their jurisdiction. They should also keep an eye on cross-border coordination. And the European Commission, both DG GROW and the Publications Office, would do well to put as much pressure on Member States as possible.

Public procurement governance as an information-intensive exercise, and the allure of digital technologies

I have just started a 12-month Mid-Career Fellowship funded by the British Academy with the purpose of writing up the monograph Digital Technologies and Public Procurement. Gatekeeping and experimentation in digital public governance (OUP, forthcoming).

In the process of writing up, I will be sharing some draft chapters and other thought pieces. I would warmly welcome feedback that can help me polish the final version. As always, please feel free to reach out: a.sanchez-graells@bristol.ac.uk.

In this first draft chapter (num 6), I explore the technological promise of digital governance and use public procurement as a case study of ‘policy irresistibility’. The main ideas in the chapter are as follows:

This Chapter takes a governance perspective to reflect on the process of horizon scanning and experimentation with digital technologies. The Chapter stresses how aspirations of digital transformation can drive policy agendas and make them vulnerable to technological hype, despite technological immaturity and in the face of evidence of the difficulty of rolling out such transformation programmes—eg regarding the still ongoing wave of transition to e-procurement. Delivering on procurement’s goals of integrity, efficiency and transparency requires facing challenges derived from the information intensity and complexity of procurement governance. Digital technologies promise to bring solutions to such informational burden and thus augment decisionmakers’ ability to deal with that complexity and with related uncertainty. The allure of the potential benefits of deploying digital technologies generates ‘policy irresistibility’ that can capture decision-making by policymakers overly exposed to the promise of technological fixes to recalcitrant governance challenges. This can in turn result in excessive experimentation with digital technologies for procurement governance in the name of transformation. The Chapter largely focuses on the EU policy framework, but the insights derived from this analysis are easily exportable.

Another draft chapter (num 7) will follow soon with more detailed analysis of the feasibility boundary for the adoption of digital technologies for procurement governance purposes. The full details of this draft chapter are as follows: A Sanchez-Graells, ‘The technological promise of digital governance: procurement as a case study of “policy irresistibility”’ to be included in A Sanchez-Graells, Digital Technologies and Public Procurement. Gatekeeping and experimentation in digital public governance (OUP, forthcoming). Available at SSRN: https://ssrn.com/abstract=4216825.

Interesting legislative proposal to make procurement of AI conditional on external checks

Procurement is progressively put in the position of regulating what types of artificial intelligence (AI) are deployed by the public sector (ie taking a gatekeeping function; see here and here). This implies that the procurement function should be able to verify that the intended AI (and its use/foreseeable misuse) will not cause harms—or, where harms are unavoidable, come up with a system to weigh, and if appropriate/possible manage, that risk. I am currently trying to understand the governance implications of this emerging gatekeeping role to assess whether procurement is best placed to carry it out.

In the context of this reflection, I found a very useful recent paper: M E Kaminski, ‘Regulating the Risks of AI’ (2023) 103 Boston University Law Review forthcoming. In addition to providing a useful critique of the treatment of AI harms as risk and of the implications in terms of the regulatory baggage that (different types of) risk regulation implies, Kaminski provides an overview of a very interesting legislative proposal: Washington State’s Bill SB 5116.

Bill SB 5116 is a proposal for new legislation ‘establishing guidelines for government procurement and use of automated decision systems in order to protect consumers, improve transparency, and create more market predictability'. The governance approach underpinning the Bill is interesting in two respects.

First, the Bill includes a ban on certain uses of AI in the public sector. As Kaminski summarises: ‘Sec. 4 of SB 5116 bans public agencies from engaging in (1) the use of an automated decision system that discriminates, (2) the use of an “automated final decision system” to “make a decision impacting the constitutional or legal rights… of any Washington resident” (3) the use of an “automated final decision system…to deploy or trigger any weapon;” (4) the installation in certain public places of equipment that enables AI-enabled profiling, (5) the use of AI-enabled profiling “to make decisions that produce legal effects or similarly significant effects concerning individuals’ (at 66, fn 398).

Second, the Bill subjects the procurement of the AI to approval by the director of the office of the chief information officer. As Kaminski clarifies: ‘The bill’s assessment process is thus more like a licensing scheme than many proposed impact assessments in that it envisions a central regulator serving a gatekeeping function (albeit probably not an intensive one, and not over private companies, which aren’t covered by the bill at all). In fact, the bill is more protective than the GDPR in that the state CIO must make the algorithmic accountability report public and invite public comment before approving it’ (at 66, references omitted).

What the Bill does, then, is to displace the gatekeeping role from the procurement function itself to the data protection regulator. It also sets the specific substantive criteria the regulator has to apply in deciding whether to authorise the procurement of the AI.

Without getting into the detail of the Washington Bill, this governance approach seems to have two main strengths over the current emerging model of procurement self-regulation of the gatekeeping role (in the EU).

First, it facilitates a standardisation of the substantive criteria to be applied in assessing the potential harms resulting from AI adoption in the public sector, with a concentration on the specific characteristics of decision-making in this context. Importantly, it creates a clear area of illegality. Some of it is in line with eg the prohibition of certain AI uses in the Draft EU AI Act (profiling), or in the GDPR (prohibition of solely automated individual-decision making, including profiling — although it may go beyond it). Moreover, such an approach would allow for an expansion of prohibited uses in the specific context of the public sector, which the EU AI Act mostly fails to tackle (see here). It would also allow for the specification of constraints applicable to the use of AI by the public sector, such as a heightened obligation to provide reasons (see M Fink & M Finck, ‘Reasoned A(I)dministration: Explanation Requirements in EU Law and the Automation of Public Administration‘ (2022) 47(3) European Law Review 376-392).

Second, it introduces an element of external (independent) verification of the assessment of potential AI harms. I think this is a crucial governance point because most proposals relying on the internal (self) assessment by the procurement team fail to consider the extent to which such approach ensures (a) adequate resourcing (eg specialism and experience in the type of assessment) and (b) sufficient objectivity in the assessment. On the second point, with procurement teams often being told to ‘just go and procure what is needed’, moving to a position of gatekeeper or controller could be too big an ask (depending on institutional aspects that require closer consideration). Moreover, this would be different from other aspects of gatekeeping that procurement has progressively been asked to carry out (also excessively, in my view: see here).

When the procurement function is asked to screen for eg potential contractors’ social or environmental compliance track record, it is usually at arms’ length from those being reviewed (and the rules on conflict of interest are there to strengthen that position). Conversely, when the procurement function is asked to screen for the likely impact on citizens and/or users of public services of an initiative promoted by the operational part of the organisation to which it belongs, things are much more complicated.

That is why some systems (like the US FAR) create elements of separation between the procurement team and those in charge of reviewing eg competition issues (by means of the competition advocate). This is a model reflected in the Washington Bill’s approach to requiring external (even if within the public administration) verification and approval of the AI impact assessment. If procurement is to become a properly functioning gatekeeper of the adoption of AI by the public sector, this regulatory approach (ie having an ‘AI Harms Controller’) seems promising. Definitely a model worth thinking about for a little longer.