Consent as a Foundation for Responsible Autonomy
This work addresses the need for responsible autonomy in AI systems, particularly in multiagent settings, but it is incremental as it focuses on conceptual groundwork rather than practical implementation.
The paper tackles the problem of making intelligent agents act responsibly at runtime by accommodating user desires and attitudes, proposing a conceptual analysis of consent and outlining challenges for modeling consent in multiagent systems to achieve responsible autonomy.
This paper focuses on a dynamic aspect of responsible autonomy, namely, to make intelligent agents be responsible at run time. That is, it considers settings where decision making by agents impinges upon the outcomes perceived by other agents. For an agent to act responsibly, it must accommodate the desires and other attitudes of its users and, through other agents, of their users. The contribution of this paper is twofold. First, it provides a conceptual analysis of consent, its benefits and misuses, and how understanding consent can help achieve responsible autonomy. Second, it outlines challenges for AI (in particular, for agents and multiagent systems) that merit investigation to form as a basis for modeling consent in multiagent systems and applying consent to achieve responsible autonomy.