Algorithmic systems increasingly shape information people are exposed to as well as influence decisions about employment, finances, and other opportunities. In some cases, algorithmic systems may be more or less favorable to certain groups or individuals, sparking substantial discussion of algorithmic fairness in public policy circles, academia, the press, and other venues. However, such discussions sometimes appear to rely on assumptions that are at odds with observations from real-world practice, and these potentially disputable assumptions may lead researchers in unproductive directions. In this talk, I will draw on my experience working on algorithmic fairness in a corporate setting to make observations regarding the nature of this complex, emerging problem space. My hope is that a richer understanding of on-the-ground practicalities and nuance can allow researchers to more effectively tailor their agendas to high leverage problems.

Note: This talk will not be recorded.