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 consequences 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 prioritization 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 transparency of practice and formal mechanisms to support disclosure.
|Original language||English (US)|
|Title of host publication||Proceedings of the 6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023|
|Publisher||Association for Computing Machinery|
|Number of pages||13|
|State||Published - Jun 12 2023|
|Event||6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023 - Chicago, United States|
Duration: Jun 12 2023 → Jun 15 2023
|Name||2023 ACM Conference on Fairness, Accountability, and Transparency|
|Conference||6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023|
|Period||6/12/23 → 6/15/23|
Bibliographical noteFunding Information:
We thank Hanlin Li and our reviewers for their invaluable feedback on this paper. De Choudhury was partly funded by National Institute of Mental Health grant R01MH117172. Chancellor completed a portion of this work while at Northwestern University.
© 2023 ACM.
- mental health
- social media
- systematic literature review