Algorithms in the court: does it matter which part of the judicial decision-making is automated?

Artificial intelligence plays an increasingly important role in legal disputes, influencing not only the reality outside the court but also the judicial decision-making process itself. While it is clear why judges may generally benefit from technology as a tool for reducing effort costs or increasing accuracy, the presence of technology in the judicial process may also affect the public perception of the courts. In particular, if individuals are averse to adjudication that involves a high degree of automation, particularly given fairness concerns, then judicial technology may yield lower benefits than expected. However, the degree of aversion may well depend on how technology is used, i.e., on the timing and strength of judicial reliance on algorithms. Using an exploratory survey, we investigate whether the stage in which judges turn to algorithms for assistance matters for individual beliefs about the fairness of case outcomes. Specifically, we elicit beliefs about the use of algorithms in four different stages of adjudication: (i) information acquisition, (ii) information analysis, (iii) decision selection, and (iv) decision implementation. Our analysis indicates that individuals generally perceive the use of algorithms as fairer in the information acquisition stage than in other stages. However, individuals with a legal profession also perceive automation in the decision implementation stage as less fair compared to other individuals. Our findings, hence, suggest that individuals do care about how and when algorithms are used in the courts.

No Policy is an Island: Mitigating COVID-19 in View of Interaction Effects.

Why are COVID-19 mitigation strategies successful in reducing infections in some cases but not in others? Existing studies of individual policies tend to neglect the many interaction effects that arise when multiple policies are enacted simultaneously. Particularly, if a socially undesirable behavior has a close (and equally problematic) substitute, then a prohibition of that behavior will simply cause people to switch to the substitute, resulting in no effect on infections. However, joint policies that prohibit both the targeted behavior and the substitute will create a positive interaction effect, which closes the loophole. Respectively, behaviors that are complements (rather than substitutes) can be discouraged by prohibiting one behavior because this discourages complementary behaviors as well.We provide a new argument for why COVID-19 policies can fail and why the evaluation of such policies may be incorrect: policies are effective only when they reduce infections as a general equilibrium, accounting not only for the targeted behavior but also for interaction effects.We illustrate our arguments by applying insights from traditional and behavioral law and economics to several examples. Thereby, we highlight regulators' challenge when facing interaction effects and factors such as social norms and time preferences.

Crime and punishment in times of pandemics.

How should we think about crime deterrence in times of pandemics? The economic analysis of crime tells us that potential offenders will compare the costs and the benefits from crime and from innocence and then choose whichever option is more profitable. We must therefore ask ourselves how this comparison is affected by the outbreak of a pandemic and the policy changes which may accompany it, such as governmental restrictions, social distancing, and responses to economic crises. Using insights from law and economics, this paper investigates how the various components in the cost-benefit analysis of crime might change during a pandemic, focusing on Covid-19 as a test case. Building on classical theoretical models, existing empirical evidence, and behavioral aspects, the analysis reveals that there are many potentially countervailing effects on crime deterrence. The paper thus highlights the need to carefully consider which aspects are applicable given the circumstances of the pandemic, as whether crime deterrence will increase or decrease should depend on the strength of the effects at play.