1,720,973 research outputs found
Restraining ChatGPT
No full textChatGPT is a prominent example of how Artificial Intelligence (AI) has stormed into our lives. Within a matter of weeks, this new AI—which produces coherent and humanlike textual answers to questions—managed to become an object of both admiration and anxiety. Can we trust generative AI systems, such as ChatGPT, without regulatory oversight?
Designing an effective legal framework for AI requires answering three main questions: (i) is there a market failure that requires legal intervention?; (ii) should AI be governed through public regulation, tort liability, or a mixture of both?; and (iii) should liability be based on strict liability or a fault-based regime such as negligence? Law and economics literature offers clear considerations for these choices, focusing on the incentives of injurers and victims to take precautions, engage in efficient activity levels, and acquire information.
This Article is the first to comprehensively apply these considerations to ChatGPT as a leading test case. As the United States is lagging in its response to the AI revolution, I focus on the recent proposals in the European Union to restrain AI systems, which apply a risk-based approach and combine regulation and liability. The analysis reveals that this approach does not map neatly onto the relevant distinctions in law and economics, such as market failures, unilateral versus bilateral care, and known versus unknown risks. Hence, the existing proposals may lead to various incentive distortions and inefficiencies. This Article, therefore, calls upon regulators to emphasize law and economics concepts in their design of AI policy
Crime and punishment in times of pandemics
No full textHow 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
Restraining ChatGPT
Full text linkChatGPT is a prominent example of how Artificial Intelligence (AI) has stormed into our lives. Within a matter of weeks, this new AI—which produces coherent and humanlike textual answers to questions—managed to become an object of both admiration and anxiety. Can we trust generative AI systems, such as ChatGPT, without regulatory oversight?
Designing an effective legal framework for AI requires answering three main questions: (i) is there a market failure that requires legal intervention?; (ii) should AI be governed through public regulation, tort liability, or a mixture of both?; and (iii) should liability be based on strict liability or a fault-based regime such as negligence? Law and economics literature offers clear considerations for these choices, focusing on the incentives of injurers and victims to take precautions, engage in efficient activity levels, and acquire information.
This Article is the first to comprehensively apply these considerations to ChatGPT as a leading test case. As the United States is lagging in its response to the AI revolution, I focus on the recent proposals in the European Union to restrain AI systems, which apply a risk-based approach and combine regulation and liability. The analysis reveals that this approach does not map neatly onto the relevant distinctions in law and economics, such as market failures, unilateral versus bilateral care, and known versus unknown risks. Hence, the existing proposals may lead to various incentive distortions and inefficiencies. This Article, therefore, calls upon regulators to emphasize law and economics concepts in their design of AI policy
Restraining ChatGPT
No full textChatGPT is a prominent example of how Artificial Intelligence (AI) has stormed into our lives. Within a matter of weeks, this new AI—which produces coherent and humanlike textual answers to questions—managed to become an object of both admiration and anxiety. Can we trust generative AI systems, such as ChatGPT, without regulatory oversight?
Designing an effective legal framework for AI requires answering three main questions: (i) is there a market failure that requires legal intervention?; (ii) should AI be governed through public regulation, tort liability, or a mixture of both?; and (iii) should liability be based on strict liability or a fault-based regime such as negligence? Law and economics literature offers clear considerations for these choices, focusing on the incentives of injurers and victims to take precautions, engage in efficient activity levels, and acquire information.
This Article is the first to comprehensively apply these considerations to ChatGPT as a leading test case. As the United States is lagging in its response to the AI revolution, I focus on the recent proposals in the European Union to restrain AI systems, which apply a risk-based approach and combine regulation and liability. The analysis reveals that this approach does not map neatly onto the relevant distinctions in law and economics, such as market failures, unilateral versus bilateral care, and known versus unknown risks. Hence, the existing proposals may lead to various incentive distortions and inefficiencies. This Article, therefore, calls upon regulators to emphasize law and economics concepts in their design of AI policy
Algorithms in the court: does it matter which part of the judicial decision-making is automated? /
No full textArtificial 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
Delegation in a multi-tier court system: are remands in the U.S. federal courts driven by moral hazard?
No full textWe analyze the countervailing incentives that mid-level appellate judges face when deciding whether to remand a case back to the lower court. Although appellate courts' ability to remand cases can mitigate moral hazard problems, by restraining trial court judges, it may sometimes instead exacerbate such problems - by enabling the midlevel appellate judges to circumvent the top-level court's preferences through delegation. Our empirical assessment reveals a 'Subsequent Remand Effect': cases that are remanded by the Supreme Court to the appellate court are far more likely to be subsequently remanded again to the district court compared to other cases. We check whether this effect originates from legitimate case-relevant reasons or from moral hazard by exploiting variations in ideological distances between court levels and through a textual analysis. We find that the size of the effect varies with the composition of ideologies, which seems consistent with moral hazard
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Judicial Effort and the Appeals System: Theory and Experiment
No full textWe investigate theoretically and experimentally how the existence of an appeals system influences the judicial effort of judges in trial courts. We assume that judges care about correct decisions and face reputational losses in case of reversals by an appeals court. Our model suggests that the impact of appeals depends crucially on the degree with which the appeals court’s accuracy increases in the trial judge’s effort. Appeals yield higher levels of effort if this effect is strong, and effort is then increasing in the trial judge’s preferences for correct outcomes. Our experimental findings underline the positive impacts of reputational losses, the endogeneity of the appeals court’s accuracy, and social concerns, which we measure by several proxies. We argue that our findings are useful for comparison of the appeals process in civil-law systems with common-law systems
Judicial Effort and the Appeals System: Theory and Experiment
No full textWe investigate theoretically and experimentally how the existence of an appeals system influences the judicial effort of judges in trial courts. We assume that judges care about correct decisions and face reputational losses in case of reversals by an appeals court. Our model suggests that the impact of appeals depends crucially on the degree with which the appeals court’s accuracy increases in the trial judge’s effort. Appeals yield higher levels of effort if this effect is strong, and effort is then increasing in the trial judge’s preferences for correct outcomes. Our experimental findings underline the positive impacts of reputational losses, the endogeneity of the appeals court’s accuracy, and social concerns, which we measure by several proxies. We argue that our findings are useful for comparison of the appeals process in civil-law systems with common-law systems
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