What values should an agent align with? An empirical comparison of general and context-specific values

The pursuit of values drives human behavior and promotes cooperation. Existing research is focused on general values (e.g., Schwartz) that transcend contexts. However, context-specific values are necessary to (1) understand human decisions, and (2) engineer intelligent agents that can elicit and ali...

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Veröffentlicht in:Autonomous agents and multi-agent systems Jg. 36; H. 1; S. 23
Hauptverfasser: Liscio, Enrico, van der Meer, Michiel, Siebert, Luciano C., Jonker, Catholijn M., Murukannaiah, Pradeep K.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.04.2022
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ISSN:1387-2532, 1573-7454
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Zusammenfassung:The pursuit of values drives human behavior and promotes cooperation. Existing research is focused on general values (e.g., Schwartz) that transcend contexts. However, context-specific values are necessary to (1) understand human decisions, and (2) engineer intelligent agents that can elicit and align with human values. 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 value-laden text corpora and Natural Language Processing to assist the annotators in systematically identifying context-specific values. We evaluate Axies in a user study involving 80 human subjects. In our study, six annotators generate value lists for two timely and important contexts: Covid -19 measures and sustainable Energy . We employ two policy experts and 72 crowd workers to evaluate Axies value lists and compare them to a list of general (Schwartz) values. We find that Axies yields values that are (1) more context-specific than general values, (2) more suitable for value annotation than general values, and (3) independent of the people applying the methodology.
Bibliographie:ObjectType-Article-1
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content type line 23
ISSN:1387-2532
1573-7454
DOI:10.1007/s10458-022-09550-0