New Search

Export article

Developing machine learning-based models to help identify child abuse and neglect: key ethical challenges and recommended solutions

Aviv Y Landau, Susi Ferrarello, Ashley Blanchard, Kenrick Cato, Nia Atkins, Stephanie Salazar, Desmond U Patton, Maxim Topaz

Abstract: Child abuse and neglect are public health issues impacting communities throughout the United States. The broad adoption of electronic health records (EHR) in health care supports the development of machine learning–based models to help identify child abuse and neglect. Employing EHR data for child abuse and neglect detection raises several critical ethical considerations. This article applied a phenomenological approach to discuss and provide recommendations for key ethical issues related to machine learning–based risk models development and evaluation: (1) biases in the data; (2) clinical documentation system design issues; (3) lack of centralized evidence base for child abuse and neglect; (4) lack of “gold standard “in assessment and diagnosis of child abuse and neglect; (5) challenges in evaluation of risk prediction performance; (6) challenges in testing predictive models in practice; and (7) challenges in presentation of machine learning–based prediction to clinicians and patients. We provide recommended solutions to each of the 7 ethical challenges and identify several areas for further policy and research.
Keywords: models / child abuse and neglect / EHR / machine / recommended

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

Share this article

Click here to see the statistics on "Journal of the American Medical Informatics Association" .
References (33)
    Back to Top Top