Algorithmic advice as a credence good:
Actors in various settings have been increasingly relying on algorithmic tools to support their decision-making. Much of the public debate concerning algorithms - especially the associated regulation of new technologies - rests on the assumption that humans can assess the quality of algorithms. We t...
Gespeichert in:
| Hauptverfasser: | , , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Mannheim, Germany
ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung GmbH Mannheim
[2022]
|
| Schriftenreihe: | Discussion paper / ZEW
no. 22-071 (12/2022) |
| Schlagworte: | |
| Online-Zugang: | 10419/268427 Volltext kostenfrei kostenfrei kostenfrei kostenfrei kostenfrei |
| Zusammenfassung: | Actors in various settings have been increasingly relying on algorithmic tools to support their decision-making. Much of the public debate concerning algorithms - especially the associated regulation of new technologies - rests on the assumption that humans can assess the quality of algorithms. We test this assumption by conducting an online experiment with 1263 participants. Subjects perform an estimation task and are supported by algorithmic advice. Our first finding is that, in our setting, humans cannot verify the algorithm's quality. We, therefore, argue that algorithms exhibit traits of a credence good - decision-makers cannot verify the quality of such goods, even after "consuming" them. Based on this finding, we test two interventions to improve the individual's ability to make good decisions in algorithmically supported situations. In the first intervention, we explain the way the algorithm functions. We find that while explanation helps participants recognize bias in the algorithm, it remarkably decreases human decision-making performance. In the second treatment, we reveal the task's correct answer after every round and find that this intervention improves human decision-making performance. Our findings have implications for policy initiatives and managerial practice. |
| Beschreibung: | 1 Online-Ressource (34 Seiten) Illustrationen |
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| 490 | 1 | |a Discussion paper / ZEW |v no. 22-071 (12/2022) | |
| 520 | 3 | |a Actors in various settings have been increasingly relying on algorithmic tools to support their decision-making. Much of the public debate concerning algorithms - especially the associated regulation of new technologies - rests on the assumption that humans can assess the quality of algorithms. We test this assumption by conducting an online experiment with 1263 participants. Subjects perform an estimation task and are supported by algorithmic advice. Our first finding is that, in our setting, humans cannot verify the algorithm's quality. We, therefore, argue that algorithms exhibit traits of a credence good - decision-makers cannot verify the quality of such goods, even after "consuming" them. Based on this finding, we test two interventions to improve the individual's ability to make good decisions in algorithmically supported situations. In the first intervention, we explain the way the algorithm functions. We find that while explanation helps participants recognize bias in the algorithm, it remarkably decreases human decision-making performance. In the second treatment, we reveal the task's correct answer after every round and find that this intervention improves human decision-making performance. Our findings have implications for policy initiatives and managerial practice. | |
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| spelling | Biermann, Jan 1977- Verfasser (DE-588)1035259052 aut Algorithmic advice as a credence good Jan Biermann, John Horton, and Johannes Walter Mannheim, Germany ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung GmbH Mannheim [2022] 1 Online-Ressource (34 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier Discussion paper / ZEW no. 22-071 (12/2022) Actors in various settings have been increasingly relying on algorithmic tools to support their decision-making. Much of the public debate concerning algorithms - especially the associated regulation of new technologies - rests on the assumption that humans can assess the quality of algorithms. We test this assumption by conducting an online experiment with 1263 participants. Subjects perform an estimation task and are supported by algorithmic advice. Our first finding is that, in our setting, humans cannot verify the algorithm's quality. We, therefore, argue that algorithms exhibit traits of a credence good - decision-makers cannot verify the quality of such goods, even after "consuming" them. Based on this finding, we test two interventions to improve the individual's ability to make good decisions in algorithmically supported situations. In the first intervention, we explain the way the algorithm functions. We find that while explanation helps participants recognize bias in the algorithm, it remarkably decreases human decision-making performance. In the second treatment, we reveal the task's correct answer after every round and find that this intervention improves human decision-making performance. Our findings have implications for policy initiatives and managerial practice. Human-algorithm decision making algorithmic advice credence goods Graue Literatur Horton, John J. Verfasser (DE-588)1043992766 aut Walter, Johannes Verfasser (DE-588)1279608471 aut Erscheint auch als Druck-Ausgabe ZEW Discussion paper no. 22-071 (12/2022) (DE-604)BV046176481 22-071 (12/2022) 10419/268427 application/pdf https://madoc.bib.uni-mannheim.de/64309 Verlag kostenfrei Volltext application/pdf http://ftp.zew.de/pub/zew-docs/dp/dp22071.pdf Verlag kostenfrei application/pdf https://www.zew.de/publikationen/algorithmic-advice-as-a-credence-good Verlag kostenfrei application/pdf http://hdl.handle.net/10419/268427 Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:bsz:180-madoc-643098 Resolving-System kostenfrei https://d-nb.info/1287585191/34 Langzeitarchivierung Nationalbibliothek kostenfrei |
| spellingShingle | Biermann, Jan 1977- Horton, John J. Walter, Johannes Algorithmic advice as a credence good |
| title | Algorithmic advice as a credence good |
| title_auth | Algorithmic advice as a credence good |
| title_exact_search | Algorithmic advice as a credence good |
| title_exact_search_txtP | Algorithmic advice as a credence good |
| title_full | Algorithmic advice as a credence good Jan Biermann, John Horton, and Johannes Walter |
| title_fullStr | Algorithmic advice as a credence good Jan Biermann, John Horton, and Johannes Walter |
| title_full_unstemmed | Algorithmic advice as a credence good Jan Biermann, John Horton, and Johannes Walter |
| title_short | Algorithmic advice as a credence good |
| title_sort | algorithmic advice as a credence good |
| url | 10419/268427 https://madoc.bib.uni-mannheim.de/64309 http://ftp.zew.de/pub/zew-docs/dp/dp22071.pdf https://www.zew.de/publikationen/algorithmic-advice-as-a-credence-good http://hdl.handle.net/10419/268427 https://nbn-resolving.org/urn:nbn:de:bsz:180-madoc-643098 https://d-nb.info/1287585191/34 |
| volume_link | (DE-604)BV046176481 |
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