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Attention:

The NSF Public Access Repository (PAR) system and access will be unavailable from 11:00 PM ET on Friday, May 16 until 2:00 AM ET on Saturday, May 17 due to maintenance. We apologize for the inconvenience.


Title: A Systematic Review of Ethics Disclosures in Predictive Mental Health Research
Applied machine learning (ML) has not yet coalesced on standard practices for research ethics. For ML that predicts mental illness using social media data, ambiguous ethical standards can impact peoples’ lives because of the area’s sensitivity and material con- sequences on health. Transparency of current ethics practices in research is important to document decision-making and improve research practice. We present a systematic literature review of 129 studies that predict mental illness using social media data and ML, and the ethics disclosures they make in research publications. Rates of disclosure are going up over time, but this trend is slow moving – it will take another eight years for the average paper to have coverage on 75% of studied ethics categories. Certain practices are more readily adopted, or "stickier", over time, though we found pri- oritization of data-driven disclosures rather than human-centered. These inconsistently reported ethical considerations indicate a gap between what ML ethicists believe ought to be and what actually is done. We advocate for closing this gap through increased trans- parency of practice and formal mechanisms to support disclosure.  more » « less
Award ID(s):
1704369
PAR ID:
10464087
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
2023 ACM Conference on Fairness, Accountability, and Transparency
Page Range / eLocation ID:
1311 to 1323
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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