Task-oriented accountability in autonomous systems

Abstract

In Artificial Intelligence (AI) systems, a key problem is to determine the group of agents that are accountable for delivering a task and, in case of failure, the extent to which each group member is partially accountable.

In this context, accountability is understood as being responsible for failing to deliver a task that a team was allocated and able to fulfil. This is, on one hand, about agents’ accountability as collaborative teams and, on the other hand, their individual degree of accountability in a team. Developing verifiable methods to address this problem is key for designing trustworthy autonomous systems and ensuring their safe and effective integration with other operational systems in society. Using degrees of accountability, one can trace back a failure to AI components and prioritise how to invest resources on fixing faulty components.

In this talk, we report on a line of research on the application of formal methods and modal logics for reasoning about accountability in multiagent systems and focus on answering “Who is accountable for an unfulfilled task in multiagent teams: when, why, and to what extent?”. In addition, we elaborate on open problems, link to ensuring safety in application domains such as Connected and Autonomous Vehicles (CAVs), and highlight the potentials of formal accountability reasoning in design and development of trustworthy AI systems.

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Identifiers

ORCID for Vahid Yazdanpanah: orcid.org/0000-0002-4468-6193

ORCID for Sebastian Stein: orcid.org/0000-0003-2858-8857

ORCID for Enrico Gerding: orcid.org/0000-0001-7200-552X

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