In this presentation, Dr. Melanie Sage offers an overview of her work exploring algorithm fairness regarding the use of machine learning and child welfare data. This work involves assessing which older youth in care receive which services, how services impact outcomes, and whether there are ways to use this data to improve fairness in service allocation through the use of algorithms. The National Science Foundation and Amazon jointly funded this work to identify ways to improve fairness and mitigate bias in machine learning. Dr. Sage is working with a team of computer scientists and other researchers to tackle this issue. She will discuss the ways that interdisciplinary collaborations can improve research in this area, their process for developing an ethics and values statement to guide their work, and the importance that social workers understand how algorithms work so that they know when to advocate for and against their use and assure that their agency stakeholders make informed decisions about the incorporation of machine learning in this work.
Dr. Sage is a co-lead of the Grand Challenge Harness Technology for Social Good; the Chair of husITa, an international group concerned with ethical use of tech and founding group of the Journal of Technology in Human Services; and the Director of the Institute for Healthy Engagement and Resilience with Technology (iHeartTech) housed within the Buffalo Center for Social Research and publisher of the open-access peer-reviewed blog Social Work with Digital Technology. She is the co-author of the teaching resource book titled Teaching Social Work with Digital Technology. She researches the ways that technology use can benefit older youth involved in the child welfare system.