A Systematic Review of Ethics Disclosures in Predictive Mental Health Research

Abstract

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.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 15 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.