3 min and 45 sec to read, 938 words

Is there a right not be predicted? In his book Against prediction, Bernard E. Harcourt contends that the widespread use of actuarial methods in criminal justice, particularly for predicting future criminal behavior, is fundamentally flawed and potentially harmful.¹ Harcourt, B., 2000. Against Prediction. Chicago: University of Chicago Press. Harlow, Caroline Wolf. He argues that these predictive techniques, rather than reducing crime or improving justice, actually reshape our concept of just punishment and can lead to increased racial profiling and discrimination. Harcourt posits that prediction tools create a self-fulfilling prophecy: by focusing law enforcement resources on groups deemed “high-risk,” we increase the likelihood of detecting crime in these groups, which in turn reinforces the initial prediction. This cycle, he argues, exacerbates existing inequalities and distorts our understanding of crime patterns. Furthermore, Harcourt suggests that the reliance on these tools shifts the focus of the criminal justice system away from rehabilitation and towards mere risk management, potentially undermining important principles of justice and individual rights.

This would seem to suggest that in order to avoid profiling and bias, prediction should not be used in cases where such techniques can create self-reflexive phenomena like self-fulfilling prophecies. So we can imagine a right not to predicted in cases like this, but what about more generally? What if there is a chance that a prediction might influence us in a positive way? Should it still be prohibited? Or are there cases where we would in fact have a right to be predicted?

The obvious case for the latter is in medicine. Here we would argue that if there are methods that can be used to diagnose us and predict treatments that could help us, we have some right to equal access to those methods. Widespread testing is a version of this: if there is a significant improvement in survival chances for a disease, and a simple test that is reliable for that disease, we sometimes considers it a duty of the state to perform that test and ensure that we get the treatment if we needed it.

Thomas Ploug argues in “The Right Not to Be Subjected to AI Profling Based on Publicly Available Data—Privacy and the Exceptionalism of AI Profiling” that there should be a right not to be predicted, even on the basis of publicly available data.² See Ploug, T. The Right Not to Be Subjected to AI Profiling Based on Publicly Available Data—Privacy and the Exceptionalism of AI Profiling. Philos. Technol. 36, 14 (2023). https://doi.org/10.1007/s13347-023-00616-9 . He argues for a sui generis legal right not to be subjected to AI profiling based on publicly available data without explicit informed consent.³ The question of consent here is really tricky, since prediction from publicly available data may be very broad, and the subject of consent would probably be rather general. He further contends that AI profiling poses unique threats to individual autonomy, wellbeing, and social participation due to its potential for accurate predictions, increased social control, and stigmatization. The article develops three key arguments for protecting personal data: the social pressure argument, the ‘open future’ argument, and the stigmatization argument. It then explains why AI profiling is exceptional in its risks compared to other forms of data processing. The author considers reasons for and against protecting publicly available online data, arguing that online engagement has important social and democratic benefits that could be undermined by unchecked AI profiling. The article examines whether existing EU GDPR regulations adequately protect against AI profiling, concluding that they do not, and advocates for an explicit right. Ploug does acknowledge several areas requiring further research, including the justification for framing this as a right, potential exemptions, and the role of informed consent – all interesting.

It is interesting to note the focus on accurate predictions. If we believed that the predictions were not accurate⁴ When does a prediction cross that accuracy threshold? That is also an interesting question., would our view of the rights question then change? But an erroneous prediction, taken as accurate may be even more harmful to us! So what do we do then? Should we, as some argue, instead focus on the results of predictions so we can challenge them?⁵ See eg M., DUBNIAK. (2023). The right to results of data processing in the form of predictive conclusions obtained by artificial intelligence. Інформація і право, Available from: 10.37750/2616-6798.2023.4(47).291615

And should we focus on the prediction as such, or the acting on that prediction in anticipating someone? A more interesting question here seems to be if there is a right not to be anticipated, not to have someone act in such a way as to use a prediction about us to reduce our optionality. And in sometimes that will mean not sharing predictions with us – as in the case of genetic predictions.⁶ See for example: Gunnar, Duttge. (2021). The Right to Know and not to Know: Predictive Genetic Diagnosis and Non-diagnosis.. Recent results in cancer research, Available from: 10.1007/978-3-030-63749-1_6 Here, knowledge about a heightened risk to succumb to a disease may change our outlook on life, our options and impact us psychologically in such a way as to be compared to physical violence or restraint.

Any philosophy of prediction would have to incorporate this into the overall discussion to understand more clearly how predictions interact with institutions, ethics and more. While often framed as a privacy question⁷ Cast as “predictive privacy” see e.g. Rainer, Mühlhoff. (2023). Predictive privacy: Collective data protection in the context of artificial intelligence and big data. Big Data & Society, Available from: 10.1177/20539517231166886 , this may well be a much more fundamental question about the temporality of action and rights.

Footnotes and references

• 1
  Harcourt, B., 2000. Against Prediction. Chicago: University of Chicago Press. Harlow, Caroline Wolf.
• 2
  See Ploug, T. The Right Not to Be Subjected to AI Profiling Based on Publicly Available Data—Privacy and the Exceptionalism of AI Profiling. Philos. Technol. 36, 14 (2023). https://doi.org/10.1007/s13347-023-00616-9
• 3
  The question of consent here is really tricky, since prediction from publicly available data may be very broad, and the subject of consent would probably be rather general.
• 4
  When does a prediction cross that accuracy threshold? That is also an interesting question.
• 5
  See eg M., DUBNIAK. (2023). The right to results of data processing in the form of predictive conclusions obtained by artificial intelligence. Інформація і право, Available from: 10.37750/2616-6798.2023.4(47).291615
• 6
  See for example: Gunnar, Duttge. (2021). The Right to Know and not to Know: Predictive Genetic Diagnosis and Non-diagnosis.. Recent results in cancer research, Available from: 10.1007/978-3-030-63749-1_6
• 7
  Cast as “predictive privacy” see e.g. Rainer, Mühlhoff. (2023). Predictive privacy: Collective data protection in the context of artificial intelligence and big data. Big Data & Society, Available from: 10.1177/20539517231166886