AI Weekly: Recognition of bias in AI continues to grow

This week, the Partnership on AI (PAI), a nonprofit committed to responsible AI use, released a paper addressing how technology -- particularly AI -- can accentuate various forms of biases. While most proposals to mitigate algorithmic discrimination require the collection of data on so-called sensitive attributes -- which usually include things like race, gender, sexuality, and nationality -- the coauthors of the PAI report argue that these efforts can actually cause harm to marginalized people and groups. Rather than trying to overcome historical patterns of discrimination and social inequity with more data and "clever algorithms," they say, the value assumptions and trade-offs associated with the use of demographic data must be acknowledged. "Harmful biases have been found in algorithmic decision-making systems in contexts such as health care, hiring, criminal justice, and education, prompting increasing social concern regarding the impact these systems are having on the wellbeing ...