Data-driven algorithms are increasingly used by penal systems across western jurisdictions to predict risks of recidivism. This chapter draws on Foucauldian analysis of the epistemic power of discourse to demonstrate how the algorithms are operating as truth or knowledge producers through the construction of risk labels that determine degrees of penal governance and control. Some proponents emphasise the technical fairness of the algorithms, highlighting their predictive accuracy. But in its exploration of the algorithms and their design configurations as well as their structural implications, this paper unravels the distinctions between a criminal justice and a social justice perspective on algorithmic fairness. It argues that whilst the former focuses on the technical, the latter emphasises broader structural consequences. These include impositions of algorithmic risk labels that operate as self-fulfilling prophesies, triggering future criminalisation and consequently undermining the perceived legitimacy of risk assessment and prediction. Through its theorisation of these issues, the chapter expands the parameters of current scholarship on the predictive algorithms applied by penal systems.

Chapter 3 examines the challenges of applying the cost–benefit analysis theory given the current legal stanrads used by courts. The cost–benefit analysis theory requires quantified costs and benefits, while the current legal system uses broad, descriptive standards to evaluate searches. The chapter notes that the current legal standards are inconsistently applied, and thus provide inadequate guidance to police who are attempting to follow these standards. The chapter also points out a dissonance between how judges apply the current standards and how lay people believe the standards should be applied. The solution is to quantify the legal standards, thus making the standards more transparent, allowing for a greater range of standards, and allowing the judges to use data from predictive algorithms as formal factors in deciding whether to allow a certain type of surveillance. This will also allow courts and policymakers to use the cost–benefit analysis theory more accurately and efficiently.

Chapter 2 focuses on the benefits side of the cost–benefit analysis equation, and notes that the rise of big data’s predictive algorithms allow law enforcement to measure the likely success rate of their surveillance with far greater precision than in the past. These predictive algorithms have the potential to revolutionize criminal investigations in many ways, by making them cheaper, more accurate, and less biased. However, the surveillance technologies must be designed in ways to ensure that they meet the Fourth Amendment’s requirement of particularized suspicion and to ensure that they do not rely on tainted data.