Volume 11, Issue 2 - Special Issue: Law and Economics on Artificial Intelligence and Data Science; Guest Editor: Haksoo Ko

This paper provides an overview of machine learning models, as compared to traditional economic models. It also lays out emerging issues in law and economics that the machine learning methodology raises. In doing so, Asian contexts are considered. Law and economics scholarship has applied econometric models for statistical inferences, but law as social engineering often requires forward-looking predictions rather than retrospective inferences. Machine learning can be used as an alternative or supplementary tool to improve the accuracy of legal prediction by controlling out-of-sample variance along with in-sample bias and by fitting diverse models to data with non-linear or otherwise complex distribution. In the legal arena, the past experience of using economic models in antitrust and other high-stakes litigation provides a clue as to how to introduce artificial intelligence into the legal decision-making process. Law and economics is also expected to provide useful insights as to how to balance the development of the artificial intelligence technology with fundamental social values such as human rights and autonomy.

This paper seeks to examine the potential influences AI may have on the right to a fair trial when it is used in the courtroom. Essentially, AI systems can assume two roles in the courtroom. On the one hand, “AI assistants” can support judges in their decision-making process by predicting and preparing judicial decisions; on the other hand, “robot judges” can replace human judges and decide cases autonomously in fully automated court proceedings. Both roles will be tested against the requirements of the right to a fair trial as protected by Article 6 ECHR. An important element in this test is the role that a human judge plays in legal proceedings. As the justice system is a social process, the AI assistant is preferred to a situation in which a robot judge would completely replace human judges. Based on extensive literature, various examples and case studies, this paper concludes that the use of AI assistants can better serve legitimacy and guarantee a fair trial.

This paper analyses the outcomes of the cases resolved under Insolvency and Bankruptcy Code of India to examine the treatment of admitted claims for the two main classes of creditors i.e. operational and financial creditors. It shows that, at an aggregate level, operational creditors and financial creditors realize an equal percentage of their claims. However, at an individual level, there is wide heterogeneity in the treatment of the two classes of creditors. Three benchmark division rules – Proportional division, Constrained Equal Losses and Constrained Equal Awards, are used to characterize the actual divisions in each case. This analysis allows us to hypothesize that when aggregate claims of operational creditors are tiny, the Constrained Equal Awards (that is extremely generous to small creditors), does well. When aggregate claims of operational creditors are large, the Proportional rule does well. But when the claims of operational creditors fall in an intermediate range, the Constrained Equal Losses rule which is most unfavourable to the operational creditor comes to the fore. Hence, some broad guidelines that can be applied on a case by case basis appear to be desirable.

During past two decades government of Pakistan has made prominent attempts to transform its financial system through various reforms. Being chief element of macroeconomic policy, financial reforms are anticipated to fetch considerable economic benefits mostly through efficient utilization of resources and recruitment of national savings. Analyzing the effectiveness of financial sector reforms the findings of this investigation demonstrates considerable and noteworthy effect on the economy of Pakistan, however still the gap of improvement exits. From the analysis it can be suggested that to make the financial sector reforms in more effective way, the information gap between micro-macro prudential should be bridged to ensure the safety and soundness of individual institutions that encompasses all activities aimed at monitoring the exposure of systemic risk and identifying potential threats to financial stability arising from macroeconomic and financial sector reforms.