Author: Sigal Samuel; Publisher: Vox; Publication Year: 2022. The following article introduced the idea of bias in a statistical sense and how bias is interpreted in society. This is an important distinction to recognize and is often overlooked. The author discussed that defining what “fair” is can be very tricky and provided different definitions of “fairness”. An example is procedural fairness: an algorithm is…
Author: Carmen Mazijn, Carina Prunkl, Andres Algaba; Publisher: Institute for Ethics in AI, Oxford University; Publication Year: 2022. The following paper discusses how artificial intelligence systems can generate, propagate, support, and automate bias in decision-making processes. Most notions of group fairness evaluate a model’s equality of outcomes by computing statistical metrics on the outputs. The authors contend that these output metrics face…
Author: Quentin J Louis, Joshua Walker, Katherine Lai, Victoria Matthews, Kitty Atwood, Susanna Raja, Ayomikun Bolaji; Publisher: Data Ethics 4 All; Publication Year: 2022. The following resource is an in-depth evaluation with three recommendations including 1). “efforts to acknowledge AI in the private sector must move beyond performative approaches driven by obligation; 2). “high-profile AI ethics job roles must be established alongside integrative, ‘bottom-up’ approaches to…
Author: Adriene Hill; Publisher: Crash Course; Publication Year: 2019. The following video provides important background on the context and consequences of data-driven research relative to the foundational elements of statistics. It also provides links to historical instances of data abuse to the current problems with data ethics and privacy. It challenges the reader to ponder questions…