Axies: Identifying and Evaluating Context-Specific Values

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Abstract

The pursuit of values drives human behavior and promotes cooperation. Existing research is focused on general (e.g., Schwartz) values that transcend contexts. However, context-specific values are necessary to (1) understand human decisions, and (2) engineer intelligent agents that can elicit human values and take value-aligned actions. We propose Axies, a hybrid (human and AI) methodology to identify context-specific values. Axies simplifies the abstract task of value identification as a guided value annotation process involving human annotators. Axies exploits the growing availability of valueladen text corpora and Natural Language Processing to assist the annotators in systematically identifying context-specific values. We evaluate Axies in a user study involving 60 subjects. In our study, six annotators generate value lists for two timely and important contexts: Covid-19 measures, and sustainable Energy. Then, two policy experts and 52 crowd workers evaluate Axies value lists. We find that Axies yields values that are context-specific, consistent across different annotators, and comprehensible to end users.
Original languageEnglish
Title of host publicationProceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems
Place of PublicationRichland, SC
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems
Pages799-808
Number of pages10
ISBN (Electronic)9781450383073
Publication statusPublished - 2021
Event20th International Conference on Autonomous Agentsand Multiagent Systems - Virtual/online event due to COVID-19
Duration: 3 May 20217 May 2021
Conference number: 20

Publication series

NameAAMAS '21
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems
ISSN (Electronic)2523-5699

Conference

Conference20th International Conference on Autonomous Agentsand Multiagent Systems
Abbreviated titleAAMAS 2021
Period3/05/217/05/21

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