An Axiomatic Approach to Formalized Responsibility Ascription
Sarah Hiller, Jonas Israel, Jobst Heitzig

TL;DR
This paper introduces an axiomatic framework for quantifying responsibility in multi-agent systems using probabilistic measures, evaluating properties of responsibility functions, and proposing two maximally compliant responsibility metrics.
Contribution
It formalizes responsibility ascription with an axiomatic approach, analyzing properties of responsibility functions, and proposing two new group responsibility measures respecting key bounds.
Findings
Identified incompatibility between upper and lower bound axioms at the member level.
Axiomatic characterization of a promising group responsibility aggregation function.
Proposed two maximally axiomatic group responsibility measures.
Abstract
A formalized and quantifiable responsibility score is a crucial component in many aspects of the development and application of multi-agent systems and autonomous agents. We can employ it to inform decision making processes based on ethical considerations, as a measure to ensure redundancy that helps us in avoiding system failure, as well as for verifying that autonomous systems remain trustworthy by testing for unwanted responsibility voids in advance. We follow recent proposals to use probabilities as the basis for responsibility ascription in uncertain environments rather than the deterministic causal views employed in much of the previous formal philosophical literature. Using an axiomatic approach we formally evaluate the qualities of (classes of) proposed responsibility functions. To this end, we decompose the computation of the responsibility a group carries for an outcome into…
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Taxonomy
TopicsFree Will and Agency · Adversarial Robustness in Machine Learning · Risk Perception and Management
