Civil liability is traditionally understood as indirect market regulation, since the risk of incurring liability for damages gives incentives to invest in safety. Such an approach, however, is inappropriate in the markets of artificial intelligence devices. In fact, according to the current paradigm of civil liability, compensation is allowed only to the extent that “someone” is identified as a debtor. However, in many cases it would not be useful to impose the obligation to pay such compensation to producers and programmers: the algorithms, in fact, can “behave” far independently from the instructions initially provided by programmers so that they can err despite no flaw in design or implementation. Therefore, application of “traditional” civil liability to AI may represent a disincentive to new technologies based on artificial intelligence. This is why I think artificial intelligence requires that the law evolves, on this matter, from an issue of civil liability into one of financial management of losses. No-fault redress schemes could be an interesting and worthy regulatory strategy in order to enable this evolution. Of course, such schemes should apply only in cases where there is no evidence that producers and programmers have acted under conditions of negligence, imprudence or unskillfulness and their activity is adequately compliant with scientifically validated standards.

The law relating to shareholders' meetings is almost invariably built on the assumptions of rational operators (shareholders, directors etc.), perfect information and no transaction costs. In such a model all operators select, within the alternatives available, the path of action that maximises their own utility; their decisions are based on stable, consistent and objective preferences; their choices are rational and based on all information available. Under this hypothesis, the only requirement needed in order to reach an "efficient" outcome in a shareholders' meeting is to provide its participants with the highest degree of information available. On the other hand, behavioural economics provides undisputable empirical evidence that all actors make their economic choices under the systematic - and, therefore, predictable - influence of errors in both reasoning and preferences, which leads them (sometimes far) away from the path of "rationality". When applied to shareholders' meetings and more generally to group decisions, behavioural analysis show that the patterns governing such events are very likely to bring the output of any group discussion far away from the objective of more informed decisions and a better composition between the different interests represented therein. In this paper we examine the most common errors at both the individual and group levels, providing some comments and highlighting how they may impact on shareholders' meetings. After such a summary, we develop some provisional proposals that we claim would contribute to debiase group decisions and help shareholders' meetings in reaching their scopes, highlighting the need for further research and action in this field.

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