Oracles as a sub-hype in blockchain discussions, or how my puppy helps me get to 10,000 steps a day

Photo: Rob Alcaraz/The Wall Street Journal.

The more I think about the use of blockchain solutions in the context of public procurement governance—and, more generally, of public services delivery—the more I find that the inability for blockchain technology to reliably connect to the ‘real world’ is bound to restrict any potentially useful applications to back-office functions and the procurement of strictly digital assets.

This is simply because blockchain can only generate its desirable effects of tamper-evident record-keeping and automated execution of smart contracts built on top of it to the extent that it does not require off-chain inputs. Blockchain is also structurally incapable of generating off-chain outputs by itself.

This is increasingly widely-known and is generating a sub-hype around oracles—which are devices aimed at plugging blockchains to the ‘real world’, either by feeding the blockchain with data, or by outputting data from the blockchain (as discussed eg here). In this blog post, I reflect on the minimal changes that I think the development of oracles is likely to have in the context of public procurement governance.

Why would blockchain be interesting in this context?

Generally, the potential for the use of blockchain and blockchain-enabled smart contracts to improve procurement governance is linked to the promise that it can help prevent corruption and mistakes through the automation of decision-making through the procurement process and the execution of public contracts and the immutability (rectius, tamper-evidence) of procurement records. There are two main barriers to the achievement of such improvements over current processes and governance mechanisms. One concerns transactions costs and information asymmetries (as briefly discussed here). The other concerns the massive gap between the virtual on-chain reality and the off-chain real world—which oracles are trying to bridge.

The separation between on-chain and off-chain reality is paramount to the analysis of governance issues and the impact blockchain can have. If blockchain can only displace the focus of potential corrupt or mistaken intervention—by the public buyer, or by public contractors—but not eliminate such risks, its potential contribution to a revolution of procurement governance certainly reduces in various orders of magnitude. So it is important to assess the extent to which blockchain can be complemented with other solutions (oracles) to achieve the elimination of points of entry for corrupt or mistaken activity, rather than their displacement or substitution.

Oracle’s vulnerabilities: my puppy wears my fitbit

In simple terms, oracles are data interfaces that connect a blockchain to a database or a source of data (for a taxonomy and some discussion, see here). This makes them potentially unreliable as (i) the oracle can only be as good as the data it relies on and (ii) the oracle can itself be manipulated. There are thus, two main sources of oracle vulnerability, which automatically translate into blockchain vulnerability.

First, the data can be manipulated—like when I prefer to sit and watch some TV rather than go for a run and tie my fitbit to my puppy’s collar so that, by midnight, I have still achieved my 10,000 daily steps.* Second, the oracle itself can be manipulated because it is a piece of software or hardware that can be tampered with, and perhaps in a way that is not readily evident and which uncovering requires some serious IT forensics—like getting a friend to crack fitbit’s code and add 10,000 daily steps to my database without me even needing to charge my watch.**

Unlilke when these issues concern the extent to which I lie to myself about my healthy lifestyle, these two vulnerabilities are highly problematic from a public governance perspective because, unless the data used in the interaction with the blockchain is itself automatically generated in a way that cannot be manipulated (and this starts to point at a mirror within a mirror situation, see below), the effect of implementing a blockchain plus oracle simply seems to be to displace the governance focus where controls need to be placed towards the source of the data and the devices used to collect it.

But oracles can get better! — sure, but only to deal with data

The sub-hype around oracles in blockchain discussions basically follows the same trend as the main hype around blockchain. The same way it is assumed that blockchain is bound to revolutionise everything because it will get so much better than it currently is, there are emerging arguments about the almost boundless potential for oracles to connect the real world to the blockchain in so much better ways. I do not have the engineering or futurology credentials necessary to pass judgement on this, but it seems to me plain to see that—unless we want to add an additional layer about robotics (and pretty evolved robotics at that), so that we consider blockchain+oracle+robot solutions—any and all advances will remain limited to improving the way data is generated/captured and exploited within and outside the blockchain.

So, for everything that is not data-based or data-transformable (such as the often used example of event tickets, which in the end get plugged back to a database that determines their effects in the real world)—or, in other words, where moving digital tokes around does not generate the necessary effects in the real world—even much advanced blockchain+oracle solutions are likely to remain of limited use in the context of procurement and the delivery of public services. Not because the applications are not (technically) possible, but because they generate governance problems that merely replace the current ones. And the advantage is not necessarily obvious.

How far can we displace governance problems and still reap some advantages?

What do I mean that the advantage is not necessarily obvious? Well, imagine the possibility of having a blockchain+oracle control the inventory of a given consumable, so that the oracle feeds information into the blockchain about the existing level of stock and about new deliveries made by the supplier, so that automated payments are made eg on a per available unit basis. This could be seen as a possible application to avoid the need for different ways of controlling the execution of the contract—or even for the need to procure the consumable in the first place, if a smart contract in the blockchain (the same, or a separate one) is automatically buying them on the basis of a closed system (eg a framework agreement or dynamic purchasing system based on electronic catalogues) or even in the ‘open market’ of the internet. Would this not be advantageous from a governance perspective?

Well, I think it would be a matter of degree because there would still need to be a way of ensuring that the oracle is not tampered with and that what the oracle is capturing reflects reality. There are myriad ways in which you could manipulate most systems—and, given the right economic incentives, there will always be attempts to manipulate even the most sophisticated systems we may want to put in place—so checks will always be needed. At this stage, the issue becomes one of comparing the running costs of the system. Unless the cost of the blockchain+oracle+new checks (plus the cybersecurity needed to keep them up and properly running) is lower than the cost of existing systems (including inefficiencies derived from corruption and mistakes), there is no obvious advantage and likely no public interest in the implementation of solutions based on these disruptive technologies.

Which leads me to the new governance issue that has started to worry me: the control of ‘business cases’ for the implementation of blockchain-based solutions in the context of public procurement (and public governance more generally). Given the lack of data and the difficulty in estimating some of the risks and costs of both the existing systems and any proposed new blockchain solutions, who is doing the math and on the basis of what? I guess convincingly answering this will require some more research, but I certainly have a hunch that not much robust analysis is going on…

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* I do not have a puppy, though, so I really end up doing my own running…

** I am not sure this is technically doable, but hopefully it works for the sake of the example…

Legal text analytics: some thoughts on where (I think) things stand

Researching the area of artificial intelligence and the law (AI & Law) has currently taken me to the complexities of natural language processing (NLP) applied to legal texts (aka legal text analytics). Trying to understand the extent to which AI can be used to perform automated legal analysis—or, more modestly, to support humans in performing legal analysis—requires (at least) a view of the current possibilities for AI tools to (i) extract information from legal sources (or ‘understand’ them and their relationships), (ii) assess their relevance to a given legal problem and (iii) apply the legal source to provide a legal solution to the problem (or to suggest one for human validation).

Of course, this obviates other issues such as the need for AI to be able to understand the factual situation to formulate the relevant legal problem, to assess or rank different legal solutions where available, or take into account additional aspects such as the likelihood of obtaining a remedy, etc—all of which could be tackled by fields of AI & Law different from legal text analytics. The above also ignores other aspects of ‘understanding’ documents, such as the ability for an algorithm to distinguish factual and legal issues within a legal document (ie a judgment) or to extract basic descriptive information (eg being able to create a citation based on the information in the judgment, or to cluster different types of provisions within a contract or across contracts)—some of which seems to be at hand or soon to be developed on the basis of the recently released Google ‘Document Understanding AI’ tool.

The latest issue of Artificial Intelligence and the Law luckily concentrates on ‘Natural Language Processing for Legal Texts’ and offers some help in trying to understand where things currently stand regarding issues (i) and (ii) above. In this post, I offer some reflections based on my understanding of two of the papers included in the special issue: Nanda et al (2019) and Chalkidis & Kampas (2019). I may have gotten the specific technical details wrong (although I hope not), but I think I got the functional insights.

Establishing relationships between legal sources

One of the problems that legal text analytics is trying to solve concerns establishing relationships between different legal sources—which can be a partial aspect of the need to ‘understand’ them (issue (i) above). This is the main problem discussed in Nanda et al, 'Unsupervised and supervised text similarity systems for automated identification of national implementing measures of European directives' (2019) 27(2) Artificial Intelligence and Law 199-225. In this piece of research, AI is used to establish whether a provision of a national implementing measure (NIM) transposes a specific article of an EU Directive or not. In extremely simplified terms, the researchers train different algorithms to perform text comparison. The researchers work on a closed list of 43 EU Directives and the corresponding Luxembuorgian, Irish and Italian NIMs. The following table plots their results.

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Nanda et al (2019: 208, Figure 6).

The table shows that the best AI solution developed by the researchers (the TF-IDF cosine) achieves levels of precision of around 83% for Luxembourg, 77% for Italy and 68% for Ireland. These seem like rather impressive results but a qualitative analysis of their experiment indicates that the significantly better performance for Luxembourgian transposition over Italian or Irish transposition likely results from the fact that Luxembourg tends to largely ‘copy & paste’ EU Directives into national law, whereas the Italian and Irish legislators adopt a more complex approach to the integration of EU rules into their existing legal instruments.

Moreover, it should be noted that the algorithms are working on a very specific issue, as they are only assessing the correspondence between provisions of EU and NIM instruments that were related—that is, they are operating in a closed or walled dataset that does not include NIMs that do not transpose any of the 43 chosen Directives. Once these aspects of the research design are taken into account, there are a number of unanswered questions, such as the precision that the algorithms would have if they had to compare entire NIMs against an open-ended list of EU Directives, or if they were used to screen for transposition rules. While the first issue could probably be answered simply extending the experiment, the second issue would probably require a different type of AI design.

On the whole, my impression after reading this interesting piece of research is that AI is still relatively far from a situation where it can provide reliable answers to the issue of establishing relationships across legal sources, particularly if one thinks of relatively more complex relationships than transposition within the EU context, such as development, modification or repeal of a given set of rules by other (potentially dispersed) rules.

Establishing relationships between legal problems and legal sources

A separate but related issue requires AI to identify legal sources that could be relevant to solve a specific legal problem (issue (ii) above)—that is, the relevant relationship is not across legal sources (as above), but between a legal problem or question and relevant legal sources.

This is covered in part of the literature review included in Chalkidis & Kampas, ‘Deep learning in law: early adaptation and legal word embeddings trained on large corpora‘ (2019) 27(2) Artificial Intelligence and Law 171-198 (see esp 188-194), where they discuss some of the solutions given to the task of the Competition on Legal Information Extraction/Entailment (COLIEE) from 2014 to 2017, which focused ‘on two aspects related to a binary (yes/no) question answering as follows: Phase one of the legal question answering task involves reading a question Q and extract[ing] the legal articles of the Civil Code that are relevant to the question. In phase two the systems should return a yes or no answer if the retrieved articles from phase one entail or not the question Q’.

The paper covers four different attempts at solving the task. It reports that the AI solutions developed to address the two binary questions achieved the following levels of precision: 66.67% (Morimoto et al. (2017)); 63.87% (Kim et al. (2015)); 57.6% (Do et al. (2017)); 53.8% (Nanda et al. (2017)). Once again, these results are rather impressive but some contextualisation may help to assess the extent to which this can be useful in legal practice.

The best AI solution was able to identify relevant provisions that entailed the relevant question 2 out of 3 times. However, the algorithms were once again working on a closed or walled field because they solely had to search for relevant provisions in the Civil Code. One can thus wonder whether algorithms confronted with the entirety of a legal order would be able to reach even close degrees of accuracy.

Some thoughts

Based on the current state of legal text analytics (as far as I can see it), it seems clear that AI is far from being able to perform independent/unsupervised legal analysis and provide automated solutions to legal problems (issue (iii) above) because there are still very significant shortcomings concerning issues of ‘understanding’ natural language legal texts (issue (i)) and adequately relating them to specific legal problems (issue (ii)). That should not be surprising.

However, what also seems clear is that AI is very far from being able to confront the vastness of a legal order and that, much as lawyers themselves, AI tools need to specialise and operate within the narrower boundaries of sub-domains or quite contained legal fields. When that is the case, AI can achieve much higher degrees of precision—see examples of information extraction precision above 90% in Chalkidis & Kampas (2019: 194-196) in projects concerning Chinese credit fraud judgments and Canadian immigration rules.

Therefore, the current state of legal text analytics seems to indicate that AI is (quickly?) reaching a point where algorithms can be used to extract legal information from natural language text sources within a specified legal field (which needs to be established through adequate supervision) in a way that allows it to provide fallible or incomplete lists of potentially relevant rules or materials for a given legal issue. However, this still requires legal experts to complement the relevant searches (to bridge any gaps) and to screen the proposed materials for actual relevance. In that regard, AI does hold the promise of much better results than previous expert systems and information retrieval systems and, where adequately trained, it can support and potentially improve legal research (ie cognitive computing, along the lines developed by Ashley (2017)). However, in my view, there are extremely limited prospects for ‘independent functionality’ of legaltech solutions. I would happily hear arguments to the contrary, though!

New paper: ‘Screening for Cartels’ in Public Procurement: Cheating at Solitaire to Sell Fool’s Gold?

By Andy Saxton.

I have uploaded a new paper on SSRN, where I critically assess the bid rigging screening tool published by the UK’s Competition and Markets Authority in 2017. I will be presenting it in a few weeks at the V Annual meeting of the Spanish Academic Network for Competition Law. The abstract is as follows:

Despite growing global interest in the use of algorithmic behavioural screens, big data and machine learning to detect bid rigging in procurement markets, the UK’s Competition and Markets Authority (CMA) was under no obligation to undertake a project in this area, much less to publish a bid-rigging algorithmic screening tool and make it generally available. Yet, in 2017 and under self-imposed pressure, the CMA released ‘Screening for Cartels’ (SfC) as ‘a tool to help procurers screen their tender data for signs of illegal bid-rigging activity’ and has since been trying to raise its profile internationally. There is thus a possibility that the SfC tool is not only used by UK public buyers, but also disseminated and replicated in other jurisdictions seeking to implement ‘tried and tested’ solutions to screen for cartels. This paper argues that such a legal transplant would be undesirable.

In order to substantiate this main claim, and after critically assessing the tool, the paper tracks the origins of the indicators included in the SfC tool to show that its functionality is rather limited as compared with alternative models that were put to the CMA. The paper engages with the SfC tool’s creation process to show how it is the result of poor policy-making based on the material dismissal of the recommendations of the consultants involved in its development, and that this has resulted in the mere illusion that big data and algorithmic screens are being used to detect bid rigging in the UK. The paper also shows that, as a result of the ‘distributed model’ used by the CMA, the algorithms underlying the SfC tool cannot improved through training, the publication of the SfC tool lowers the likelihood of some types of ‘easy to spot cases’ by signalling areas of ‘cartel sophistication’ that can bypass its tests and that, on the whole, the tool is simply not fit for purpose. This situation is detrimental to the public interest because reliance in a defective screening tool can create a false perception of competition for public contracts, and because it leads to immobilism that delays (or prevents) a much-needed engagement with the extant difficulties in developing a suitable algorithmic screen based on proper big data analytics. The paper concludes that competition or procurement authorities willing to adopt the SfC tool would be buying fool’s gold and that the CMA was wrong to cheat at solitaire to expedite the deployment of a faulty tool.

The full citation of the paper is: Sanchez-Graells, Albert, ‘Screening for Cartels’ in Public Procurement: Cheating at Solitaire to Sell Fool’s Gold? (May 3, 2019). Available at SSRN: https://ssrn.com/abstract=3382270