Racism, responsibility and autonomy in HCI: Testing perceptions of an AI agent. (November 2019)
- Record Type:
- Journal Article
- Title:
- Racism, responsibility and autonomy in HCI: Testing perceptions of an AI agent. (November 2019)
- Main Title:
- Racism, responsibility and autonomy in HCI: Testing perceptions of an AI agent
- Authors:
- Hong, Joo-Wha
Williams, Dmitri - Abstract:
- Abstract: This study employs an experiment to test subjects' perceptions of an artificial intelligence (AI) crime-predicting agent that produces clearly racist predictions. It used a 2 (human crime predictor/AI crime predictor) x 2 (high/low seriousness of crime) design to test the relationship between the level of autonomy and responsibility for the unjust results. The seriousness of crime was manipulated to examine the relationship between the perceived threat and trust in the authority's decisions. Participants (N = 334) responded to an online questionnaire after reading one of four scenarios with the same story depicting a crime predictor unjustly reporting a higher likelihood of subsequent crimes for a black defendant than for a white defendant for similar crimes. The results indicate that people think that an AI crime predictor has significantly less autonomy than a human crime predictor. However, both the identity of the crime predictor and the seriousness of the crime showed insignificant results on the level of responsibility assigned to the predictor. Also, a clear positive relationship between autonomy and responsibility was found in both human and AI crime predictor scenarios. The implications of the findings for applications and theory are discussed. Highlights: People think that an AI crime predictor has significantly less autonomy than a human crime predictor. No difference was found between the type of crime predictor and between crime seriousness forAbstract: This study employs an experiment to test subjects' perceptions of an artificial intelligence (AI) crime-predicting agent that produces clearly racist predictions. It used a 2 (human crime predictor/AI crime predictor) x 2 (high/low seriousness of crime) design to test the relationship between the level of autonomy and responsibility for the unjust results. The seriousness of crime was manipulated to examine the relationship between the perceived threat and trust in the authority's decisions. Participants (N = 334) responded to an online questionnaire after reading one of four scenarios with the same story depicting a crime predictor unjustly reporting a higher likelihood of subsequent crimes for a black defendant than for a white defendant for similar crimes. The results indicate that people think that an AI crime predictor has significantly less autonomy than a human crime predictor. However, both the identity of the crime predictor and the seriousness of the crime showed insignificant results on the level of responsibility assigned to the predictor. Also, a clear positive relationship between autonomy and responsibility was found in both human and AI crime predictor scenarios. The implications of the findings for applications and theory are discussed. Highlights: People think that an AI crime predictor has significantly less autonomy than a human crime predictor. No difference was found between the type of crime predictor and between crime seriousness for assigned responsibility. A clear positive relationship between autonomy and responsibility was found in both human and AI crime predictor scenarios. … (more)
- Is Part Of:
- Computers in human behavior. Volume 100(2019)
- Journal:
- Computers in human behavior
- Issue:
- Volume 100(2019)
- Issue Display:
- Volume 100, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 100
- Issue:
- 2019
- Issue Sort Value:
- 2019-0100-2019-0000
- Page Start:
- 79
- Page End:
- 84
- Publication Date:
- 2019-11
- Subjects:
- Attribution theory -- CASA -- Predictive policing -- Racism -- Artificial intelligence -- Human-AI Communication
Interactive computer systems -- Periodicals
Man-machine systems -- Periodicals
004.019 - Journal URLs:
- http://www.sciencedirect.com/science/journal/07475632 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.chb.2019.06.012 ↗
- Languages:
- English
- ISSNs:
- 0747-5632
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.921600
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 14825.xml