Accountable Approval Sorting

Khaled Belahcene, Yann Chevaleyre, Christophe Labreuche, Nicolas Maudet, Vincent Mousseau, Wassila Ouerdane

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

Main track. Pages 70-76.

https://doi.org/10.24963/ijcai.2018/10

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Abstract

We consider decision situations in which a set of points of view (voters, criteria) are to sort a set of candidates to ordered categories (Good/Bad). Candidates are judged  good, when approved by a sufficient set of points of view; this corresponds to NonCompensatory Sorting. To be accountable, such approval sorting should provide guarantees about the decision process and decisions concerning specific candidates. We formalize accountability using a feasibility problem expressed as a boolean satisfiability formulation. We illustrate different forms of accountability when a committee decides with approval sorting and study the information that should be disclosed by the committee.

Keywords

  • Constraints and SAT: Modeling;Formulation
  • Knowledge Representation and Reasoning: Preference Modelling and Preference-Based Reasoning
  • Agent-based and Multi-agent Systems: Voting
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