Conversations around the creation and deployment of artificial intelligence (AI) systems continue to raise concerns about systemic inequalities. Some have argued that biases are baked into algorithmic models that adopt the norms, values, and assumptions of their developers. There have also been conversations about the adaptation of certain algorithms for deployment in new contexts, resulting in overly discriminatory decisions. As the use of AI becomes more common in employment, housing, credit, and even college admissions decisions, it’s important to consider racial biases that may influence the creation and execution of machine learning algorithms, and the individuals impacted by them. Equally significant are the roles of anti-discrimination laws and other guardrails in averting both intended and unintended negative consequences.
On June 19, the Center for Technology Innovation at Brookings hosted a webinar of distinguished computer, social scientists and legal experts to talk about the intersection of race, AI, and systemic inequalities. The discussion shared existing research in this area and explore how fairness, equity, and ethics can be better addressed in the development of AI systems.