Accountable Approval Sorting

Author:

Belahcene Khaled1,Chevaleyre Yann2,Labreuche Christophe3,Maudet Nicolas4,Mousseau Vincent1,Ouerdane Wassila1

Affiliation:

1. Laboratoire Genie Industriel, CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, France

2. Université Paris-Dauphine, PSL Research University, CNRS, UMR [7243], LAMSADE, France

3. Thales Reserach & Technology, Palaiseau, France

4. Sorbonne Université, CNRS, Laboratoire d'Informatique de Paris 6, LIP6, France

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.

Publisher

International Joint Conferences on Artificial Intelligence Organization

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