Abstract
Computer systems are increasingly involved in making decisions. Therefore, it is increasingly important that they understand our values. To make values usable, context is important, both of the individual and the actions they underlie. This work aims to study if it is possible to make it easier to elicit an individual’s values by using the context of the action. Practically, we first held an expert survey (n = 7) to see if some values are more likely to underlie some actions than others. The results were positive on this score, so a second study (user, (n = 135)) was done showing that restricting the number of values made it easier to elicit values from users while not unnecessarily limiting their expression. This work shows that when linking actions to values, it is possible to make the elicitation easier by only showing the applicable options. This is an important step in being able to incorporate values in computerized decision making.
Original language | English |
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Title of host publication | 11th International Workshop Modelling and Reasoning in Context |
Pages | 17-21 |
Publication status | Published - Sept 2020 |
Event | 11th International Workshop Modelling and Reasoning in Context - https://easychair.org/cfp/mrc2020, Santiago de Compostela, Spain Duration: 29 Aug 2020 → 5 Sept 2020 https://easychair.org/cfp/mrc2020 |
Workshop
Workshop | 11th International Workshop Modelling and Reasoning in Context |
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Abbreviated title | MRC 2020 |
Country/Territory | Spain |
City | Santiago de Compostela |
Period | 29/08/20 → 5/09/20 |
Other | Virtual/online event due to COVID-19 |
Internet address |
Bibliographical note
Virtual/online event due to COVID-19Fingerprint
Dive into the research topics of 'Linking actions to value categories: a first step in categorization for easier value elicitation'. Together they form a unique fingerprint.Datasets
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Dataset belonging to the paper: Linking actions to value categories
Tielman, M. L. (Creator) & Moonen, D. D. M. (Creator), TU Delft - 4TU.ResearchData, 28 Aug 2020
DOI: 10.4121/12886757
Dataset/Software: Dataset