How to make artificial intelligence (AI) systems safe and aligned with human
values is an open research question. Proposed solutions tend toward relying on
human intervention in uncertain situations, learning human values and
intentions through training or observation, providing off-switches,
implementing isolation or simulation environments, or extrapolating what people
would want if they had more knowledge and more time to think. Law-based
approaches--such as inspired by Isaac Asimov--have not been well regarded. This
paper makes a case that effective legal systems are the best way to address AI
safety. Law is defined as any rules that codify prohibitions and prescriptions
applicable to particular agents in specified domains/contexts and includes
processes for enacting, managing, enforcing, and litigating such rules.

人工智能系统如何安全并与人类价值相一致是一个开放性的研究问题，本文提出有效的法律制度是解决人工智能安全性的最佳途径。

AI 安全之法律研究

A Case for AI Safety via Law

The use of meta-rules in logic, i.e., rules whose content includes other
rules, has recently gained attention in the setting of non-monotonic reasoning:
a first logical formalisation and efficient algorithms to compute the
(meta)-extensions of such theories were proposed in Olivieri et al (2021) This
work extends such a logical framework by considering the deontic aspect. The
resulting logic will not just be able to model policies but also tackle
well-known aspects that occur in numerous legal systems. The use of Defeasible
Logic (DL) to model meta-rules in the application area we just alluded to has
been investigated. Within this line of research, the study mentioned above was
not focusing on the general computational properties of meta-rules.
This study fills this gap with two major contributions. First, we introduce
and formalise two variants of Defeasible Deontic Logic with Meta-Rules to
represent (1) defeasible meta-theories with deontic modalities, and (2) two
different types of conflicts among rules: Simple Conflict Defeasible Deontic
Logic, and Cautious Conflict Defeasible Deontic Logic. Second, we advance
efficient algorithms to compute the extensions for both variants.

本文针对行为规范方面，将 Meta rules 适用于可撤销逻辑中，提出了两个变体的 Defeasible Deontic Logic，并针对这两个变体提出了高效的算法，此方法可适用于法规和信息技术领域。

伦理元规则

Deontic Meta-Rules

The paper offers a contribution to the interdisciplinary constructs of
analyzing fairness issues in automatic algorithmic decisions. Section 1 shows
that technical choices in supervised learning have social implications that
need to be considered. Section 2 proposes a contextual approach to the issue of
unintended group discrimination, i.e. decision rules that are facially neutral
but generate disproportionate impacts across social groups (e.g., gender, race
or ethnicity). The contextualization will focus on the legal systems of the
United States on the one hand and Europe on the other. In particular,
legislation and case law tend to promote different standards of fairness on
both sides of the Atlantic. Section 3 is devoted to the explainability of
algorithmic decisions; it will confront and attempt to cross-reference legal
concepts (in European and French law) with technical concepts and will
highlight the plurality, even polysemy, of European and French legal texts
relating to the explicability of algorithmic decisions. The conclusion proposes
directions for further research.

本论文针对算法决策中的公平性问题提供了跨学科的构想。其中，法律系统的差异是一个核心问题，同时论文提出了一种上下文方法来处理意外的群体歧视，研究算法决策的可解释性。