Explainability in Mechanism Design: Recent Advances and the Road Ahead

Sharadhi Alape Suryanarayana, David Sarne, Sarit Kraus

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Designing and implementing explainable systems is seen as the next step towards increasing user trust in, acceptance of and reliance on Artificial Intelligence (AI) systems. While explaining choices made by black-box algorithms such as machine learning and deep learning has occupied most of the limelight, systems that attempt to explain decisions (even simple ones) in the context of social choice are steadily catching up. In this paper, we provide a comprehensive survey of explainability in mechanism design, a domain characterized by economically motivated agents and often having no single choice that maximizes all individual utility functions. We discuss the main properties and goals of explainability in mechanism design, distinguishing them from those of Explainable AI in general. This discussion is followed by a thorough review of the challenges one may face when working on Explainable Mechanism Design and propose a few solution concepts to those.

Original languageEnglish
Title of host publicationMulti-Agent Systems - 19th European Conference, EUMAS 2022, Proceedings
EditorsDorothea Baumeister, Jörg Rothe
PublisherSpringer Science and Business Media Deutschland GmbH
Pages364-382
Number of pages19
ISBN (Print)9783031206139
DOIs
StatePublished - 2022
Event19th European Conference on Multi-Agent Systems, EUMAS 2022 - Düsseldorf, Germany
Duration: 14 Sep 202216 Sep 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13442 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th European Conference on Multi-Agent Systems, EUMAS 2022
Country/TerritoryGermany
CityDüsseldorf
Period14/09/2216/09/22

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Explainability
  • Justification
  • Mechanism design

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