This is a blog about the use of emerging technologies to boost the governance of public procurement. It used to be a blog on EU law, with a focus on free movement, public procurement and competition law issues (thus the long archive of entries about those topics). I use it to publish my thoughts and to test some ideas. All comments are personal and in no way bind any of the institutions to which I am affiliated and, particularly, the University of Bristol Law School. I hope to spur discussion and look forward to your feedback and participation.
The recording and slides for yesterday’s webinar on ‘Tech fixes for procurement problems?’ co-hosted by the University of Bristol Law School and the GW Law Government Procurement Programme are now available for catch up if you missed it.
I would like to thank once again Dean Jessica Tillipman (GW Law), Professor Sope Williams (Stellenbosch), and Eliza Niewiadomska (EBRD) for really interesting discussion, and to all participants for their questions. Comments most welcome, as always.
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.
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.
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.
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.
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).
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.
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.
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.
Magic; Stage Illusions and Scientific Diversions, Including Trick Photography (1897), written by Albert Allis Hopkins and Henry Ridgely Evan.
As the dust settles in the process of reform of UK public procurement rules, and while we await for draft legislation to be published (some time this year?), there is now a chance to further reflect on the likely effects of the deregulatory, flexibility- and discretion-based approach to be embedded in the new UK procurement system.
An issue that may not have been sufficiently highlighted, but which should be of concern, is the way in which increased flexibility and discretion will unavoidably carry higher corruption risks and reduce the effectiveness of potential anti-corruption tools, in particular those based on the implementation of digital technologies for procurement oversight [see A Sanchez-Graells, ‘Procurement Corruption and Artificial Intelligence: Between the Potential of Enabling Data Architectures and the Constraints of Due Process Requirements’ in S Williams-Elegbe & J Tillipman (eds), Routledge Handbook of Public Procurement Corruption (Routledge, forthcoming)].
This is an inescapable issue, for there is an unavoidable trade-off between flexibility, discretion and corruption (in procurement, and more generally). And this does not bode well for the future of UK procurement integrity if the experience during the pandemic is a good predictor.
The trade-off between flexibility, discretion and corruption underpins many features of procurement regulation, such as the traditional distrust of procedures involving negotiations or direct awards, which may however stifle procurement innovation and limit value for money [see eg F Decarolis et al, ‘Rules, Discretion, and Corruption in Procurement: Evidence from Italian Government Contracting’ (2021) NBER Working Paper 28209].
The trade-off also underpins many of the anti-corruption tools (eg red flags) that use discretionary elements in procurement practice as a potential proxy for corruption risk [see eg M Fazekas, L Cingolani and B Tóth, ‘Innovations in Objectively Measuring Corruption in Public Procurement’ in H K Anheier, M Haber and M A Kayser (eds) Governance Indicators: Approaches, Progress, Promise (OUP 2018) 154-180; or M Fazekas, S Nishchal and T Søreide, ‘Public procurement under and after emergencies’ in O Bandiera, E Bosio and G Spagnolo (eds), Procurement in Focus – Rules, Discretion, and Emergencies (CEPR Press 2022) 33-42].
Moreover, economists and political scientists have clearly stressed that one way of trying to strike an adequate balance between the exercise of discretion and corruption risks, without disproportionately deterring the exercise of judgement or fostering laziness or incompetence in procurement administration, is to increase oversight and monitoring, especially through auditing mechanisms based on open data (see eg Procurement in a crisis: how to mitigate the risk of corruption, collusion, abuse and incompetence).
The difficulty here is that the trade-off is inescapable and the more dimensions on which there is flexibility and discretion in a procurement system, the more difficult it will be to establish a ‘normalcy benchmark’ or ‘integrity benchmark’ from which deviations can trigger close inspection. Taking into account that there is a clear trend towards seeking to automate integrity checks on the basis of big data and machine learning techniques, this is a particularly crucial issue. In my view, there are two main sources of difficulties and limitations.
First, that discretion is impossible to code for [see S Bratus and A Shubina, Computerization, Discretion, Freedom (2015)]. This both means that discretionary decisions cannot be automated, and that it is impossible to embed compliance mechanisms (eg through the definition of clear pathways based on business process modelling within an e-procurement system, or even in blockchain and smart contract approaches: Neural blockchain technology for a new anticorruption token: towards a novel governance model) where there is the possibility of a ‘discretion override’.
The more points along the procurement process where discretion can be exercised (eg choice of procedure, design of procedure, award criteria including weakening of link to subject matter of the contract and inclusion of non(easily)measurable criteria eg on social value, displacement of advantage analysis beyond sphere of influence of contracting authority, etc) the more this difficulty matters.
Second, the more deviations there are between the new rulebook and the older one, the lower the value of existing (big) data (if any is available or useable) and of any indicators of corruption risk, as the regulatory confines of the exercise of discretion will not only have shifted, but perhaps even lead to a displacement of corruption-related exercise of discretion. For example, focusing on the choice of procedure, data on the extent to which direct awards could be a proxy for corruption may be useless in a new context where that type of corruption can morph into ‘custom-made’ design of a competitive flexible procedure—which will be both much more difficult to spot, analyse and prove.
Moreover, given the inherent fluidity of that procedure (even if there is to be a template, which is however not meant to be uncritically implemented), it will take time to build up enough data to be able to single out specific characteristics of the procedure (eg carrying out negotiations with different bidders in different ways, such as sequentially or in parallel, with or without time limits, the inclusion of any specific award criterion, etc) that can be indicative of corruption risk reliably. And that intelligence may not be forthcoming if, as feared, the level of complexity that comes with the exercise of discretion deters most contracting authorities from exercising it, which would mean that only a small number of complex procedures would be carried out every year, potentially hindering the accumulation of data capable of supporting big data analysis (or even meaningful econometrical treatment).
Overall, then, the issue I would highlight again is that there is an unavoidable trade-off between increasing flexibility and discretion, and corruption risk. And this trade-off will jeopardise automation and data-based approaches to procurement monitoring and oversight. This will be particularly relevant in the context of the design and implementation of the tools at the disposal of the proposed Procurement Review Unit (PRU). The Response to the public consultation on the Transforming Public Procurement green paper emphasised that
‘… the PRU’s main focus will be on addressing systemic or institutional breaches of the procurement regulations (i.e. breaches common across contracting authorities or regularly being made by a particular contracting authority). To deliver this service, it will primarily act on the basis of referrals from other government departments or data available from the new digital platform and will have the power to make formal recommendations aimed at addressing these unlawful breaches’ (para [48]).
Given the issues raised above, and in particular the difficulty or impossibility of automating the analysis of such data, as well as the limited indicative value and/or difficulty of creating reliable red flags in a context of heightened flexibility and discretion, quite how effective this will be is difficult to tell.
Moreover, given the floating uncertainty on what will be identified as suspicious of corruption (or legal infringement), it is also possible that the PRU (initially) operates on the basis of indicators or thresholds arbitrarily determined (much like the European Commission has traditionally arbitrarily set thresholds to consider procurement practices problematic under the Single Market Scorecard; see eg here). This could have a signalling effect that could influence decision-making at contracting authority level (eg to avoid triggering those red flags) in a way that pre-empts, limits or distorts the exercise of discretion—or that further displaces corruption-related exercise of discretion to areas not caught by the arbitrary indicators or thresholds, thus making it more difficult to detect.
Therefore, these issues can be particularly relevant in establishing both whether the balance between discretion and corruption risk is right under the new rulebook’s regulatory architecture and approach, as well as whether there are non-statutory determinants of the (lack of) exercise of discretion, other than the complexity and potential litigation and challenge risk already stressed in earlier analysis and reflections on the green paper.
Another ‘interesting’ area of development of UK procurement law and practice post-Brexit when/if it materialises.
I have started to watch the three-session series on Intelligent Automation in US Federal Procurement hosted by the GW Law Government Procurement Law Program over the last few weeks (worth watching!), as part of my research for a paper on AI and corruption in procurement. The first session in the series focuses in large part on the intelligent automation of information gathering for the purposes of what in the EU context are the processes of exclusion and qualitative selection of economic providers. And this got me thinking about how it would (or not) be possible to replicate some of the projects in an EU jurisdiction (or even at EU-wide level).
And, once again, the issue of the lack of data on which to train algorithms, as well as the lack of representative/comprehensive databases from which to automatically extract information came up. But somehow it seems like the ESPD and the underlying regulatory approach may be making things more difficult.
In the EU, automating mandatory exclusion (not necessarily to have AI adopt decisions, but to have it prepare reports capable of supporting independent decision-making by contracting authorities) would primarily be a matter of checking against databases of prior criminal convictions, which is not only difficult to do due to the absence of structured databases themselves, but also due to the diversity of legal regimes and the languages involved, as well as the pervasive problem of beneficial ownership and (dis)continuity in corporate personality.
Similarly, for discretionary exclusion, automation would primarily be based on retrieving information concerning grounds not easily or routinely captured in existing databases (eg conflicts of interest), as well as limited by increasingly constraining CJEU case law demanding case-by-case assessments by the contracting authority in ways that diminish the advantages of automating eg red flags based on decisions taken by a different contracting authority (or centralised authority).
Finally, automating qualitative selection would be almost impossible, as it is currently mostly based on the self-certification implicit in the ESPD. Here, the 2014 Public Procurement Directives tried to achieve administrative simplification not through the once only principle (which would be useful in creating databases supporting automatisation of some parts of the project, but on which a 2017 project does not seem to have provided many advances), but rather through the ‘tell us only if successful’ (or suspected) principle. This naturally diminishes the amount of information the public buyer (and the broader public sector) holds, with repeat tenderers being completely invisible for the purposes of automation so long as they are not awarded contracts.
All of this leads me to think that there is a big blind spot in the current EU approach to open procurement data as the solution/enabler of automatisation in the context of EU public procurement practice. In fact, most of the crucial (back office) functions — and especially those relating to probity and quality screenings relating to tenderers — will not be susceptible of automation until (or rather unless) different databases are created and advanced mechanisms of interconnection of national databases are created at EU level. And creating those databases will be difficult (or simply not happen in practice) for as long as the ESPD is in place, unless a parallel system of registration (based on the once only principle) is developed for the purposes of registering onto and using eProcurement platforms (which seems to also raise some issues).
So, all in all, it would seem that more than ever we need to concentrate on the baby step of creating a suitable data architecture if we want to reap the benefits of AI (and robotic process automation in particular) any time soon. As other jurisdictions are starting to move (or crawl, to keep with the metaphor), we should not be wasting our time.
Thinking about some of the issues raised in the earlier post ‘Can the robot procure for you?,’ I have now taken a close look at the European Commission’s Proposal for an Artificial Intelligence Act (AIA) to see how it approaches the use of AI in procurement procedures. It may (not) come as a surprise that the AI Act takes an extremely light-touch approach to the regulation of AI uses in procurement and simply subjects them to (yet to be developed) voluntary codes of conduct. I will detail my analysis of why this is the case in this post, as well as some reasons why I do not find it satisfactory.
Before getting to the details, it is worth stressing that this is reflective of a broader feature of the AIA: its heavy private sector orientation. When it comes to AI uses by the public sector, other than prohibiting some massive surveillance by the State (both for law enforcement and to generate a system of social scoring) and classifying as high-risk the most obvious AI uses by the law enforcement and judicial authorities (all of which are important, of course), the AIA remains silent on the use of AI in most administrative procedures, with the only exception of those concerning social benefits.
This approach could be generally justified by the limits to EU competence and, in particular, those derived from the principle of administrative self-organisation of the Member States. However, given the very broad approach taken by the Commission on the interpretation and use of Article 114 TFEU (which is the legal basis for the AIA, more below), this is not entirely consistent. It could rather be that the specific uses of AI by the public sector covered in the proposal reflect the increasingly well-known problematic uses of (biased) AI solutions in narrow aspects of public sector activity, rather than a broader reflection on the (still unknown, or still unimplemented) uses that could be problematic.
While the AIA is ‘future-proofed’ by including criteria for the inclusion of further use cases in its ‘high-risk’ category (which determines the bulk of compliance obligations), it is difficult to see how those criteria are suited to a significant expansion of the regulatory constraints to AI uses by the public sector, including in procurement. Therefore, as a broader point, I submit that the proposed AIA needs some revision to make it more suited to the potential deployment of AI by the public sector. To reflect on that, I am co-organising a webinar on ’Digitalization and AI decision-making in administrative law proceedings’, which will take place on 15 Nov 2021, 1pm UK (save the date, registration and more details here). All welcome.
Summarising the AIA is both difficult and has already been done (see eg this quick explainer of the Centre for Data Innovation, and for an accessible overview of the rationale and regulatory architecture of the AIA, this master class by Prof Christiane Wendehorst). So, I will just highlight here a few issues linked to the analysis of procurement’s position within its regulatory framework.
The AIA seeks to establish a proportionate approach to the regulation of AI deployment and use. While its primary concern is with the consolidation of the EU Single Digital Market and the avoidance of regulatory barriers to the circulation of AI solutions, its preamble also points to the need to ensure the effectiveness of EU values and, crucially, the fundamental rights in the Charter of Fundamental Rights of the EU.
Importantly for the purposes of our discussion, recital (28) AIA stresses that ‘The extent of the adverse impact caused by the AI system on the fundamental rights protected by the Charter is of particular relevance when classifying an AI system as high-risk. Those rights include ... right to an effective remedy and to a fair trial [Art 47 Charter] … [and] right to good administration {Art 41 Charter]’.
The AIA seeks to create such a proportionate approach to the regulation of AI by establishing four categories of AI uses: prohibited, high-risk, limited risk requiring transparency measures, and minimal risk. The two categories that carry regulatory constraints or compliance obligations are those concerning high-risk (Arts 8-15 AIA), and limited risk requiring transparency measures (Art 52 AIA, which also applies to some high-risk AI). Minimal risk AI uses are left unregulated, although the AIA (Art 69) seeks to promote the development of codes of conduct intended to foster voluntary compliance with the requirements applicable to high-risk AI systems.
Procurement AI practices could not be classified as prohibited uses (Art 5 AIA), except in the difficult to imagine circumstances in which they deployed subliminal techniques. It is also difficult to see how they could fall under the regime applicable to uses requiring special transparency (Art 52) because it only applies to AI systems intended to interact with natural persons, which must be ‘designed and developed in such a way that natural persons are informed that they are interacting with an AI system, unless this is obvious from the circumstances and the context of use.’ It would not be difficult for public buyers using external-facing AI solutions (eg chatbots seeking to guide tenderers through their e-submissions) to make it clear that the tenderers are interacting with an AI solution. And, even if not, the transparency obligations are rather minimal.
So, the crux of the issue rests on whether procurement-related AI uses could be classified as high-risk. This is regulated in Art 6 AIA, which cross-refers to Annex III AIA. The Annex contains a numerus clausus of high-risk AI uses, which is however susceptible of amendment under the conditions specified in Art 7 AIA. Art 6/Annex III do not contain any procurement-related AI uses. The only type of AI use linked to administrative procedures concerns ‘AI systems intended to be used by public authorities or on behalf of public authorities to evaluate the eligibility of natural persons for public assistance benefits and services, as well as to grant, reduce, revoke, or reclaim such benefits and services’ (Annex III(5)(a) AIA).
Clearly, then, procurement-related AI uses are currently left to the default category of those with minimal risk and, thus, subjected only to voluntary self-regulation via codes of conduct.
Art 7 AIA establishes the following two cumulative criteria: (a) the AI systems are intended to be used in any of the areas listed in points 1 to 8 of Annex III; and (b) the AI systems pose a risk of harm to the health and safety, or a risk of adverse impact on fundamental rights, that is, in respect of its severity and probability of occurrence, equivalent to or greater than the risk of harm or of adverse impact posed by the high-risk AI systems already referred to in Annex III.
The first hurdle in getting procurement-related AI uses included in Annex III in the future is formal and concerns the interpretation of the categories listed therein. There are only two potential options: nesting them under uses related to ‘Access to and enjoyment of essential private services and public services and benefits’, or uses related to ‘Administration of justice and democratic processes’. It could (theoretically) be possible to squeeze them in one of them (perhaps the latter easier than the former), but this is by no means straightforward and, given the existing AI uses in each of the two categories, I would personally be disinclined to engage in such broad interpretation.
Even if that hurdle was cleared, the second hurdle is also challenging. Art 7(2) AIA establishes the criteria to assess that an AI use poses a sufficient ‘risk of adverse impact on fundamental rights’. Of those criteria, there are three that in my view would make it very difficult to classify procurement-related AI uses as high-risk. Those criteria require the European Commission to consider:
(c) the extent to which the use of an AI system has already caused … adverse impact on the fundamental rights or has given rise to significant concerns in relation to the materialisation of such … adverse impact, as demonstrated by reports or documented allegations submitted to national competent authorities;
(d) the potential extent of such harm or such adverse impact, in particular in terms of its intensity and its ability to affect a plurality of persons;
(e) the extent to which potentially harmed or adversely impacted persons are dependent on the outcome produced with an AI system, in particular because for practical or legal reasons it is not reasonably possible to opt-out from that outcome;
(g) the extent to which the outcome produced with an AI system is easily reversible …;
Meeting these criteria would require for the relevant AI systems to basically be making independent or fully automated decisions (eg on award of contract, or exclusion of tenderers), so that their decisions would be seen to affect the effectiveness of Art 41 and 47 Charter rights; as well as a (practical) understanding that those decisions cannot be easily reversed. Otherwise, the regulatory threshold is so high that most likely procurement-related AI uses (screening, recommender systems, support to human decision-making (eg automated evaluation of tenders), etc) are unlikely to be considered to pose a sufficient ‘risk of adverse impact on fundamental rights’.
As mentioned above, one of the potential explanations for the almost absolute silence on the use of AI in administrative procedures in the AIA could be that the Commission considers that this aspect of AI regulation belongs to each of the Member States. If that was true, then Member States could further than the code of conduct self-regulatory approach resulting from the AIA regulatory architecture. An easy approach would be to eg legally mandate compliance with the AIA obligations for high-risk AI systems.
However, given the internal market justification of the AIA, to be honest, I have my doubts that such a regulatory intervention would withstand challenges on the basis of general EU internal market law.
The thrust of the AIA competential justification (under Art 114 TFEU, see point 2.1 of the Explanatory memorandum) is that
The primary objective of this proposal is to ensure the proper functioning of the internal market by setting harmonised rules in particular on the development, placing on the Union market and the use of products and services making use of AI technologies or provided as stand-alone AI systems. Some Member States are already considering national rules to ensure that AI is safe and is developed and used in compliance with fundamental rights obligations. This will likely lead to two main problems: i) a fragmentation of the internal market on essential elements regarding in particular the requirements for the AI products and services, their marketing, their use, the liability and the supervision by public authorities, and ii) the substantial diminishment of legal certainty for both providers and users of AI systems on how existing and new rules will apply to those systems in the Union.
All of those issues would arise if each Member State adopted its own rules constraining the use of AI for administrative procedures not covered by the AIA (either related to procurement or not), so the challenge to that decentralised approach on grounds of internal market law by eg providers of procurement-related AI solutions capable of deployment in all Member States but burdened with uneven regulatory requirements seems quite straightforward (if controversial), especially given the high level of homogeneity in public procurement regulation resulting from the 2014 Public Procurement Package. Not to mention the possibility of challenging those domestic obligation on grounds that they go further than the AIA in breach of Art 16 Charter (freedom to conduct a business), even if this could face some issues resulting from the interpretation of Art 51 thereof.
In my view, there is a case to be made for the repositioning of procurement-related AI uses within the AIA, and its logic can apply to other areas of administrative law/activity with similar market effects.
The key issue is that the development of AI solutions to support decision-making in the public sector not only concerns the rights of those directly involved or affected by those decisions, but also society at large. In the case of procurement, eg the development of biased procurement evaluation or procurement recommender systems can have negative social effects via its effects on the market (eg on value for money, to mention the most obvious) that are difficult to identify in single tender procurement decisions.
Moreover, it seems that the public administration is well-placed to comply with the requirements of the AIA for high-risk AI systems as a matter of routine procedure, and the arguments on the need to take a proportionate approach to the regulation of AI so as not to stifle innovation lose steam and barely have any punch when it comes to imposing them on the public sector user. Further, to a large extent, the AIA requirements seem to me mostly aligned with the requirements for running a proper (and challenge proof) eProcurement system, and they would also facilitate compliance with duties of good administration when specific decisions are challenged.
Therefore, on balance, I see no good reason not to expand the list in Annex III AIA to include the use of AI systems in all administrative procedures, and in particular in public procurement and in other regulatory sectors where ex post interventions to correct market distortions resulting from biased AI implementations can simply be practically impossible. I submit that this should be done before its adoption.
** This post is based on the seminar given at the Law Department of Pompeu Fabra University in Barcelona, on 7 November 2019. The slides for the seminar are available here. Please note that some of the issues have been rearranged. I am thankful to participants for the interesting discussion, and to Dr Lela Mélon and Prof Carlos Gómez Ligüerre for the kind invitation to participate in this activitity of their research group on patrimonial law. I am also grateful to Karolis Granickas for comments on an earlier draft. The standard disclaimer applies.**
The use of public procurement as a tool to further sustainability goals is not a new topic, but rather the object of a long-running discussion embedded in the broader setting of the use of procurement for the pursuit of horizontal or secondary goals—currently labelled smart or strategic procurement. The instrumentalisation of procurement for (quasi)regulatory purposes gives rise to a number of issues, such as: regulatory transfer; the distortion of the very market mechanisms on which procurement rules rely as a result of added regulatory layers and constraints; legitimacy and accountability issues; complex regulatory impact assessments; professionalisation issues; etc.
Discussions in this field are heavily influenced by normative and policy positions, which are not always clearly spelled out but still drive most of the existing disagreement. My own view is that the use of procurement for horizontal policies is not per se desirable. The simple fact that public expenditure can act as a lever/incentive to affect private (market) behaviour does not mean that it should be used for that purpose at every opportunity and/or in an unconstrained manner. Procurement should not be used in lieu of legislation or administrative regulation where it is a second-best regulatory tool. Embedding regulatory elements that can also achieve horizontal goals in the procurement process should only take place where it has clear synergies with the main goal of procurement: the efficient satisfaction of public sector needs and/or needs in the public interest. This generates a spectrum of potential uses of procurement of a different degree of desirability.
At one end, and at its least desirable, procurement can and is used as a trade barrier for economic protectionism. In my view, this should not happen. At the other end of the spectrum, at its most desirable, procurement can and is (sometimes) used in a manner that supports environmental sustainability and technical innovation. In my view, this should happen, and more than it currently does. In between these two ends, there are uses of procurement for the promotion of labour and social standards, as well as for the promotion of human rights. Controversial as this position is, in my view, the use of procurement for the pursuit of those goals should be subjected to strict proportionality analysis in order to make sure that the secondary goal does not prevent the main purpose of the efficient satisfaction of public sector needs and/or needs in the public interest.
From a normative perspective, thus, I think that there is a wide space of synergy between procurement and environmental sustainability—which goes beyond green procurement and extends to the use of procurement to support a more circular economy—and that this can be used more effectively than is currently the case, due to emerging innovative uses of digital technologies for procurement governance.
This is the topic in which I would like to concentrate, to formulate some exploratory thoughts. The following reflections are focused on the EU context, but hopefully they are of a broader relevance. I first zoom in on the strategic priorities of fostering sustainability through procurement (2) and the digitalisation of procurement (3), as well as critically assess the current state of development of digital technologies for procurement governance (4). I then look at the interaction between both strategic goals, in terms of the potential for sustainable digital procurement (5), which leads to specific discussion of the need for an enabling data architecture (6), the potential for AI and sustainable procurement (7), the potential for the implementation of blockchains for sustainable procurement (8) and the need to refocus the emerging guidelines on the procurement of digital technologies to stress their sustainability dimension (9). Some final thoughts conclude (10).
As mentioned above, the use of public procurement to promote sustainability is not a new topic. However, it has been receiving increasing attention in recent policy-making and legislative efforts (see eg this recent update)—though they are yet to translate in the level of practical change required to make a relevant contribution to pressing challenges, such as the climate emergency (for a good critique, see this recent post by Lela Mélon).
Facilitating the inclusion of sustainability-related criteria in procurement was one of the drivers for the new rules in the 2014 EU Public Procurement Package, which create a fairly flexible regulatory framework. Most remaining problems are linked to the implementation of such a framework, not its regulatory design. Cost, complexity and institutional inertia are the main obstacles to a broader uptake of sustainable procurement.
The European Commission is alive to these challenges. In its procurement strategy ‘Making Procurement work in and for Europe’ [COM(2017) 572 final; for a critical assessment, see here], the Commission stressed the need to facilitate and to promote the further uptake of strategic procurement, including sustainable procurement.
However, most of its proposals are geared towards the publication of guidance (such as the Buying Green! Handbook), standardised solutions (such as the library of EU green public procurement criteria) and the sharing of good practices (such as in this library of use cases) and training materials (eg this training toolkit). While these are potentially useful interventions, the main difficulty remains in their adoption and implementation at Member State level.
While it is difficult to have a good view of the current situation (see eg the older studies available here, and the terrible methodology used for this 2015 PWC study for the Commission), it seems indisputable that there are massive differences across EU Member States in terms of sustainability-oriented innovation in procurement.
Taking as a proxy the differences that emerge from the Eco-Innovation Scoreboard, it seems clear that this very different level of adoption of sustainability-related eco-innovation is likely reflective of the different approaches followed by the contracting authorities of the different Member States.
Such disparities create difficulties for policy design and coordination, as is acknowledged by the Commission and the limitations of its procurement strategy. The main interventions are thus dependent on Member States (and their sub-units).
Similarly to the discussion above, the bidirectional relationship between the use of procurement as a tool to foster innovation, and the adaptation of procurement processes in light of technological innovations is not a new issue. In fact, the transition to electronic procurement (eProcurement) was also one of the main drivers for the revision of the EU rules that resulted in the 2014 Public Procurement Package, as well as the flanking regulation of eInvoicing and the new rules on eForms. eProcurement (broadly understood) is thus an area where further changes will come to fruition within the next 5 years (see timeline below).
However, even a maximum implementation of the EU-level eProcurement rules would still fall short of creating a fully digitalised procurement system. There are, indeed, several aspects where current technological solutions can enable a more advanced and comprehensive eProcurement system. For example, it is possible to automate larger parts of the procurement process and to embed compliance checks (eg in solutions such as the Prozorro system developed in Ukraine). It is also possible to use the data automatically generated by the eProcurement system (or otherwise consolidated in a procurement register) to develop advanced data analytics to support procurement decision-making, monitoring, audit and the deployment of additional screens, such as on conflicts of interest or competition checks.
Progressing the national eProcurement systems to those higher levels of functionality would already represent progress beyond the mandatory eProcurement baseline in the 2014 EU Public Procurement Package and the flanking initiatives listed above; and, crucially, enabling more advanced data analytics is one of the effects sought with the new rules on eForms, which aim to significantly increase the availability of (better) procurement data for transparency purposes.
Although it is an avenue mainly explored in other jurisdictions, and currently in the US context, it is also possible to create public marketplaces akin to Amazon/eBay/etc to generate a more user-friendly interface for different types of catalogue-based eProcurement systems (see eg this recent piece by Chris Yukins).
Beyond that, the (further) digitalisation of procurement is another strategic priority for the European Commission; not only for procurement’s sake, but also in the context of the wider strategy to create an AI-friendly regulatory environment and to use procurement as a catalyst for innovations of broader application – along lines of the entrepreneurial State (Mazzucato, 2013; see here for an adapted shorter version).
Indeed, the Commission has formulated a bold(er) vision for future procurement systems based on emerging digital technologies, in which it sees a transformative potential: “New technologies provide the possibility to rethink fundamentally the way public procurement, and relevant parts of public administrations, are organised. There is a unique chance to reshape the relevant systems and achieve a digital transformation” (COM(2017) 572 fin at 11).
Even though the Commission has not been explicit, it may be worth trying to map which of the currently emerging digital technologies could be of (more direct) application to procurement governance and practice. Based on the taxonomy included in a recent OECD report (2019a, Annex C), it is possible to identify the following types and specific technologies with potential procurement application:
AI solutions
Virtual Assistants (Chat bots or Voice bots): conversational, computer-generated characters that simulate a conversation to deliver voice- or text-based information to a user via a Web, kiosk or mobile interface. A VA incorporates natural-language processing, dialogue control, domain knowledge and a visual appearance (such as photos or animation) that changes according to the content and context of the dialogue. The primary interaction methods are text-to-text, text-to-speech, speech-to-text and speech-to-speech;
Natural language processing: technology involves the ability to turn text or audio speech into encoded, structured information, based on an appropriate ontology. The structured data may be used simply to classify a document, as in “this report describes a laparoscopic cholecystectomy,” or it may be used to identify findings, procedures, medications, allergies and participants;
Machine Learning: the goal is to devise learning algorithms that do the learning automatically without human intervention or assistance;
Deep Learning: allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction;
Robotics: deals with the design, construction, operation, and use of robots, as well as computer systems for their control, sensory feedback, and information processing;
Recommender systems: subclass of information filtering system that seeks to predict the "rating" or "preference" that a user would give to an item;
Expert systems: is a computer system that emulates the decision-making ability of a human expert;
Digital platforms
Distributed ledger technology (DLT): is a consensus of replicated, shared, and synchronized digital data geographically spread across multiple sites, countries, or institutions. There is no central administrator or centralised data storage. A peer-to-peer network is required as well as consensus algorithms to ensure replication across nodes is undertaken; Blockchain is one of the most common implementation of DLT;
Smart contracts: is a computer protocol intended to digitally facilitate, verify, or enforce the negotiation or performance of a contract;
IoT Platform: platform on which to create and manage applications, to run analytics, and to store and secure your data in order to get value from the Internet of Things (IoT);
Not all technologies are equally relevant to procurement—and some of them are interrelated in a manner that requires concurrent development—but these seem to me to be those with a higher potential to support the procurement function in the future. Their development needs not take place solely, or primarily, in the context of procurement. Therefore, their assessment should be carried out in the broader setting of the adoption of digital technologies in the public sector.
The emergence of the above mentioned digital technologies is now seen as a potential solution to complex public policy problems, such as the promotion of more sustainable public procurement. Keeping track of all the potential use cases in the public sector is difficult and the hype around buzzwords such as AI, blockchain or the internet of things (IoT) generates inflated claims of potential solutions to even some of the most wicked public policy problems (eg corruption).
This is reflective of the same hype in private markets, and in particular in financial and consumer markets, where AI is supposed to revolutionise the way we live, almost beyond recognition. There also seems to be an emerging race to the top (or rather, a copy-cat effect) in policy-making circles, as more and more countries adopt AI strategies in the hope of harnessing the potential of these technologies to boost economic growth.
In my view, digital technologies are receiving excessive attention. These are immature technologies and their likely development and usefulness is difficult to grasp beyond a relatively abstract level of potentiality. As such, I think these technologies may be receiving excessive attention from policy-makers and possibly also disproportionate levels of investment (diversion).
The implementation of digital technologies in the public sector faces a number of specific difficulties—not least, around data availability and data skills, as stressed in a recent OECD report (2019b). While it is probably beyond doubt that they will have an impact on public governance and the delivery of public services, it is more likely to be incremental rather than disruptive or revolutionary. Along these lines, another recent OECD report (2019c) stresses the need to take a critical look at the potential of artificial intelligence, in particular in relation to public sector use cases.
The OECD report (2019a) mentioned above shows how, despite these general strategies and the high levels of support at the top levels of policy-making, there is limited evidence of significant developments on the ground. This is the case, in particular, regarding the implementation of digital technologies in public procurement, where the OECD documents very limited developments (see table below).
Of course, this does not mean that we will not see more and more widespread developments in the coming years, but a note of caution is necessary if we are to embrace realistic expectations about the potential for significant changes resulting from procurement digitalisation. The following sections concentrate on the speculative analysis of such potential use of digital technologies to support sustainable procurement.
Bringing together the scope for more sustainable public procurement (2), the progressive digitalisation of procurement (3), and the emergence of digital technologies susceptible of implementation in the public sector (4); the combined strategic goal (or ideal) would be to harness the potential of digital technologies to promote (more) sustainable procurement. This is a difficult exercise, surrounded by uncertainty, so the rest of this post is all speculation.
In my view, there are different ways in which digital technologies can be used for sustainability purposes. The contribution that each digital technology (DT) can make depends on its core functionality. In simple functional terms, my understanding is that:
AI is particularly apt for the massive processing of (big) data, as well as for the implementation of data-based machine learning (ML) solutions and the automation of some tasks (through so-called robotic process automation, RPA);
Blockchain is apt for the implementation of tamper-resistant/evident decentralised data management;
The internet of things (IoT) is apt to automate the generation of some data and (could be?) apt to breach the virtual/real frontier through oracle-enabled robotics
The timeline that we could expect for the development of these solutions is also highly uncertain, although there are expectations for some technologies to mature within the next four years, whereas others may still take closer to ten years.
© Gartner, Aug 2018.
Each of the core functionalities or basic strengths of these digital technologies, as well as their rate of development, will determine a higher or lower likelihood of successful implementation in the area of procurement, which is a highly information/data-sensitive area of public policy and administration. Therefore, it seems unavoidable to first look at the need to create an enabling data architecture as a priority (and pre-condition) to the deployment of any digital technologies.
The importance of the availability of good quality data in the context of digital technologies cannot be over-emphasised (see eg OECD, 2019b). This is also clear to the European Commission, as it has also included the need to improve the availability of good quality data as a strategic priority. Indeed, the Commission stressed that “Better and more accessible data on procurement should be made available as it opens a wide range of opportunities to assess better the performance of procurement policies, optimise the interaction between public procurement systems and shape future strategic decisions” (COM(2017) 572 fin at 10-11).
However, despite the launch of a set of initiatives that seek to improve the existing procurement data architecture, there are still significant difficulties in the generation of data [for discussion and further references, see A Sanchez-Graells, “Data-driven procurement governance: two well-known elephant tales” (2019) 24(4) Communications Law 157-170; idem, “Some public procurement challenges in supporting and delivering smart urban mobility: procurement data, discretion and expertise”, in M Finck, M Lamping, V Moscon & H Richter (eds), Smart Urban Mobility – Law, Regulation, and Policy, MPI Studies on Intellectual Property and Competition Law (Springer 2020) forthcoming; and idem, “EU Public Procurement Policy and the Fourth Industrial Revolution: Pushing and Pulling as One?”, Working Paper for the YEL Annual Conference 2019 ‘EU Law in the era of the Fourth Industrial Revolution’].
To be sure, there are impending advances in the availability of quality procurement data as a result of the increased uptake of the Open Contracting Data Standards (OCDS) developed by the Open Contracting Partnership (OCP); the new rules on eForms; the development of eGovernment Application Programming Interfaces (APIs); the 2019 Open Data Directive; the principles of business to government data sharing (B2G data sharing); etc. However, it seems to me that the European Commission needs to exercise clearer leadership in the development of an EU-wide procurement data architecture. There is, in particular, one measure that could be easily adopted and would make a big difference.
The 2019 Open Data Directive (Directive 2019/1024/EU, ODD) establishes a special regime for high-value datasets, which need to be available free of charge (subject to some exceptions); machine readable; provided via APIs; and provided as a bulk download, where relevant (Art 14(1) ODD). Those high-value datasets are yet to be identified by the European Commission through implementing acts aimed at specifying datasets within a list of thematic categories included in Annex I, which includes the following datasets: geospatial; Earth observation and environment; meteorological; statistics; companies and company ownership; and mobility. In my view, most relevant procurement data can clearly fit within the category of statistical information.
More importantly, the directive specifies that the ‘identification of specific high-value datasets … shall be based on the assessment of their potential to: (a) generate significant socioeconomic or environmental benefits and innovative services; (b) benefit a high number of users, in particular SMEs; (c) assist in generating revenues; and (d) be combined with other datasets’ (Art 14(2) ODD). Given the high-potential of procurement data to unlock (a), (b) and (d), as well as, potentially, generate savings analogous to (c), the inclusion of datasets of procurement information in the future list of high-value datasets for the purposes of the Open Data Directive seems like an obvious choice.
Of course, there will be issues to iron out, as not all procurement information is equally susceptible of generating those advantages and there is the unavoidable need to ensure an appropriate balance between the publication of the data and the protection of legitimate (commercial) interests, as recognised by the Directive itself (Art 2(d)(iii) ODD) [for extended discussion, see here]. However, this would be a good step in the direction of ensuring the creation of a forward-looking data architecture.
At any rate, this is not really a radical idea. At least half of the EU is already publishing some public procurement open data, and many Eastern Partnership countries publish procurement data in OCDS (eg Moldova, Ukraine, Georgia). The suggestion here would bring more order into this bottom-up development and would help Member States understand what is expected, where to get help from, etc, as well as ensure the desirable level of uniformity, interoperability and coordination in the publication of the relevant procurement data.
Beyond that, in my view, more needs to be done to also generate backward-looking databases that enable the public sector to design and implement adequate sustainability policies, eg in relation to the repair and re-use of existing assets.
Only when the adequate data architecture is in place, will it be possible to deploy advanced digital technologies. Therefore, this should be given the highest priority by policy-makers.
If/when sufficient data is available, there will be scope for the deployment of several specific implementations of artificial intelligence. It is possible to imagine the following potential uses:
Sustainability-oriented (big) data analytics: this should be relatively easy to achieve and it would simply be the deployment of big data analytics to monitor the extent to which procurement expenditure is pursuing or achieving specified sustainability goals. This could support the design and implementation of sustainability-oriented procurement policies and, where appropriate, it could generate public disclosure of that information in order to foster civic engagement and to feedback into political processes.
Development of sustainability screens/indexes: this would be a slight variation of the former and could facilitate the generation of synthetic data visualisations that reduced the burden of understanding the data analytics.
Machine Learning-supported data analysis with sustainability goals: this could aim to train algorithms to establish eg the effectiveness of sustainability-oriented procurement policies and interventions, with the aim of streamlining existing policies and to update them at a pace and level of precision that would be difficult to achieve by other means.
Sustainability-oriented procurement planning: this would entail the deployment of algorithms aimed at predictive analytics that could improve procurement planning, in particular to maximise the sustainability impact of future procurements.
Moreover, where clear rules/policies are specified, there will be scope for:
Compliance automation: it is possible to structure procurement processes and authorisations in such a way that compliance with pre-specified requirements is ensured (within the eProcurement system). This facilitates ex ante interventions that could minimise the risk of and the need for ex post contractual modifications or tender cancellations.
Recommender/expert systems: it would be possible to use machine learning to assist in the design and implementation of procurement processes in a way that supported the public buyer, in an instance of cognitive computing that could accelerate the gains that would otherwise require more significant investments in professionalisation and specialisation of the workforce.
Chatbot-enabled guidance: similarly to the two applications above, the use of procurement intelligence could underpin chatbot-enabled systems that supported the public buyers.
A further open question is whether AI could ever autonomously generate new sustainability policies. I dare not engage in such exercise in futurology…
By contrast with the potential for big data and the AI it can enable, the potential for blockchain applications in the context of procurement seems to me much more limited (for further details, see here, here and here). To put it simply, the core advantages of distributed ledger technologies/blockchain derive from their decentralised structure.
Whereas there are several different potential configurations of DLTs (see eg Rauchs et al, 2019 and Alessie et al, 2019, from where the graph is taken), the configuration of the blockchain affects its functionalities—with the highest levels of functionality being created by open and permissionless blockchains.
However, such a structure is fundamentally uninteresting to the public sector, which is unlikely to give up control over the system. This has been repeatedly stressed and confirmed in an overview of recent implementations (OECD, 2019a:16; see also OECD, 2018).
Moreover, even beyond the issue of public sector control, it should be stressed that existing open and permissionless blockchains operate on the basis of a proof-of-work (PoW) consensus mechanism, which has a very high carbon footprint (in particular in the case of Bitcoin). This also makes such systems inapt for sustainable digital procurement implementations.
Therefore, sustainable blockchain solutions (ie private & permissioned, based on proof-of-stake (PoS) or a similar consensus mechanisms), are likely to present very limited advantages for procurement implementation over advanced systems of database management—and, possibly, even more generally (see eg this interesting critical paper by Low & Mik, 2019).
Moreover, even if there was a way to work around those constraints and design a viable technical solution, that by itself would still not fix underlying procurement policy complexity, which will necessarily impose constraints on technologies that require deterministic coding, eg
Tenders on a blockchain - the proposals to use blockchain for the implementation of the tender procedure itself are very limited, in my opinion, by the difficulty in structuring all requirements on the basis of IF/THEN statements (see here).
Smart (public) contracts - the same constraints apply to smart contracts (see here and here).
Blockchain as an information exchange platform (Mélon, 2019, on file) - the proposals to use blockchain mechanisms to exchange information on best practices and tender documentation of successful projects could serve to address some of the confidentiality issues that could arise with ‘standard’ databases. However, regardless of the technical support to the exchange of information, the complexity in identifying best practices and in ensuring their replicability remains. This is evidenced by the European Commission’s Initiative for the exchange of information on the procurement of Large Infrastructure Projects (discussed here when it was announced), which has not been used at all in its first two years (as of 6 November 2019, there were no publicly-available files in the database).
A final issue to take into consideration is that the procurement of digital technologies needs to itself incorporate sustainability considerations. However, this does not seem to be the case in the context of the hype and over-excitement with the experimentation/deployment of those technologies.
Indeed, there are emerging guidelines on procurement of some digital technologies, such as AI (UK, 2019) (WEF, 2019) (see here for discussion). However, as could be expected, these guidelines are extremely technology-centric and their interaction with broader procurement policies is not necessarily straightforward.
I would argue that, in order for these technologies to enable a more sustainable procurement, sustainability considerations need to be embedded not only in their application, but may well require eg an earlier analysis of whether the life-cycle of existing solutions warrants replacement, or the long-term impacts of the implementation of digital technologies (eg in terms of life-cycle carbon footprint).
Pursuing technological development for its own sake can have significant environmental impacts that must be assessed.
This (very long…) blog post has structured some of my thoughts on the interaction of sustainability and digitalisation in the context of public procurement. By way of conclusion, I would just try to translate this into priorities for policy-making (and research). Overall, I believe that the main area of effort for policy-makers should now be in creating an enabling data architecture. Its regulation can thus focus research in the short term. In the medium-term, and as use cases become clearer in the policy-making sphere, research should be moving towards the design of digital technology-enabled solutions (for sustainable public procurement, but not only) and their regulation, governance and social impacts. The long-term is too difficult for me to foresee, as there is too much uncertainty. I can only guess that we will cross that bridge when/if we get there…
