Panel: A Discussion on the Consequences of AI Bias

The panel discusses the importance of developers understanding the broader impact of their work and having agency to object, complain, and focus attention on ethical and lawful practices in technology development. They also emphasize the need for interdisciplinary perspectives, demographic perspectives, and conscientious objectors in the development community.

• Developers need to understand the broader impact of their work and consider whether some of their work should be done
• Technology workers have agency to object, complain, and focus attention on ethical and lawful practices in technology development
• Interdisciplinary perspectives and demographic perspectives are important in technology development
• Conscientious objectors in the development community can be leaders in helping us think about where the limits should be and what alternatives could be
• Developers should focus on algorithmic or machine learning hygiene to avoid missteps that result in foreclosed opportunities on various populations

The panel mentions the importance of having whistleblower protections for developers and people working in the tech sector, citing the work of a remarkable black woman in California who helped establish these protections. They also reference the importance of conscientious objectors in the development community, who can be leaders in helping us think about where the limits should be and what alternatives could be.

Join Nicol Turner-Lee, Senior Fellow at The Brookings Institution leads a panel discussion with Safia Noble Associate Professor at UCLA, Aylin Caliskan Assistant Professor at University of Washington and Mark MacCarthy Senior Fellow and Adjunct Professor at Georgetown University. AI enables an incredibly broad set of new use cases that are moving businesses forward. However, there have been several high-profile companies that have elected to discontinue investing in AI use cases that are especially vulnerable to bias. Recruiting, criminal justice, and facial recognition are just some examples where relying on existing training data can amplify today's prevalent inequalities. While AI systems can be game-changing, how should we think about managing the bias within AI? Join this panel discussion to hear leading researchers dissect the impact of AI bias, discuss approaches to operationalize fairness and explore how the roles of government and private industry can support each other, when tackling bias.