2 min and 34 sec to read, 642 words

In 2018 Sandra Wachter and Bernt Mittelstadt published a really interesting article about the role of inferences in data protection law.¹ Wachter, Sandra and Mittelstadt, Brent, A Right to Reasonable Inferences: Re-Thinking Data Protection Law in the Age of Big Data and AI (October 5, 2018). Columbia Business Law Review, 2019(2), Available at SSRN: https://ssrn.com/abstract=3248829 Their conclusion was that there is a need for a right to reasonable inferences, and that with the current data protection law, the legal standing of inferences remains uncertain.

The article is well-argued and thorough, but it hinges on the use of “inference” in a way that is tricky – especially in the view of recent discussions about how data protection law applies to large language models.

We see this clearly if we replace the word “inference” with the word “prediction”. The idea that there is a right to reasonable predictions about us is subtly, and importantly, different from the idea that there is a right to reasonable inferences – and, I would argue, probably harder to resolve.

One reason for this is that there exists shades of prediction — and we need to treat different kinds of predictions, or statements about the future, differently. Here is one possible such taxonomy that we could play around with:

Here’s a potential scale of terms, ranging from low to high certainty:

• Guess: A casual, intuitive estimate with low certainty and little to no evidentiary basis.
• Conjecture: An educated guess based on incomplete information or unproven assumptions.
• Forecast: A more formal, model-based estimate of future outcomes, often with explicit probabilistic bounds and stated assumptions.
• Prediction: A relatively high-certainty statement about the future, often based on robust evidence, well-validated models, or expert judgment.
• Prophecy: A categorical claim about the future with no uncertainty, often based on alleged divine or supernatural insight rather than evidence.

You could argue that each of these should be treated differently, not least because the difference they have in effects and impacts, and how reasonable it is to rely on them. It also seems clear that they have different economic value, and so should be thought about differently from a law and economics perspective as well. There could, for example, be a right to reasonable predictions, but no right to reasonable conjectures.

This, of course, raises the issue of how we determine what it is that a large language model is doing — and here I would argue that it stops short of forecasts in our hierarchy. Most language models operate in the realm of conjecture – which is both a strength and possible weakness. Now, you could argue that this is understating the quality of language models – but I don’t think so, not at this point in time. That may change in the future, though.

Overall, predictions will become much more important in understanding society, rights, law and other things in the future – and there is a lot of work to be done on the legal aspects of different kinds of statements about the future (as these become more accurate and we rely on them to make decisions).

The perhaps harder question here is the one that Wachter and Mittelstadt explore in the beginning of the paper — if data protection law should guarantee good decision making. It is tempting to suggest that we need data protection, or data quality, legislation to do so — but that is also a dangerous path to enter, since it suggests that decision making responsibility can be spread across a “decision value chain” — something that detracts responsibility from the decision makers. If we instead argue that there is a general responsibility to make good decisions in certain institutions, we incentivize these institutions to constantly examine the grounds on which they decide — that seems a better path.

Footnotes and references

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  Wachter, Sandra and Mittelstadt, Brent, A Right to Reasonable Inferences: Re-Thinking Data Protection Law in the Age of Big Data and AI (October 5, 2018). Columbia Business Law Review, 2019(2), Available at SSRN: https://ssrn.com/abstract=3248829