Axies: Identifying and Evaluating Context-Specific Values

Adaptive Agents and Multi-Agents Systems, pp. 799–808

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 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 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.

Authors 3

1. Enrico Liscio

   Delft University of Technology

   Affiliation as printed

   Delft University of Technology, Delft, Netherlands

2. Michiel van der Meer Aachen

   Leiden University · Delft University of Technology

   Affiliation as printed

   Leiden University, Leiden, Netherlands

   Delft University of Technology, Delft, Netherlands

3. Pradeep Kumar Murukannaiah

   Delft University of Technology

   Affiliation as printed

   Delft University of Technology, Delft, Netherlands