Accountable Approval Sorting

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

IJCAI 2018 pp. 70-76

doi:10.24963/IJCAI.2018/10 /ijcai/2018/belahcene2018ijcai-accountable/

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.

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Cite

Text

Belahcène et al. "Accountable Approval Sorting." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/10

Markdown

[Belahcène et al. "Accountable Approval Sorting." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/belahcene2018ijcai-accountable/) doi:10.24963/IJCAI.2018/10

BibTeX

@inproceedings{belahcene2018ijcai-accountable,
  title     = {{Accountable Approval Sorting}},
  author    = {Belahcène, Khaled and Chevaleyre, Yann and Labreuche, Christophe and Maudet, Nicolas and Mousseau, Vincent and Ouerdane, Wassila},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {70-76},
  doi       = {10.24963/IJCAI.2018/10},
  url       = {https://mlanthology.org/ijcai/2018/belahcene2018ijcai-accountable/}
}