Events Calendar

14 Oct
Event Type

Virtual

Target Audience

Undergraduate Students, Staff, Alumni, Prospective Students, Faculty, Graduate Students, Postdocs

Group
Year of Data and Society
University Unit
Institute for Cyber Law, Policy, and Security
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Responsible AI: How Can We Make Artificial Intelligence Work for Everyone?

With James Foulds, Assistant Professor, UMBC

This event is part of the Forbes Corridor Colloquia, sponsored by Pitt Cyber.

With the rising influence of artificial intelligence (AI) and machine learning (ML) systems on many important aspects of our daily lives, there are growing concerns that these systems may have harmful consequences, including the erosion of privacy, the potential for abuse, and unfair or discriminatory behavior.  In this talk, I will discuss the need for responsible AI methods and practices, and the technical and non-technical interventions which can help to ensure that the potential harms and pitfalls of AI technologies are mitigated. I will then focus on my research group's proposed methods for ensuring that machine learning algorithms behave in a fair and equitable manner. I will present methods which help to avoid harmful discrimination against protected groups along lines of gender, race, sexual orientation, class, and disability, and show how to extend AI fairness protections to the marginalized populations at the intersections of these groups.

Dial-In Information

For log-in information, please complete the registration form.

Thursday, October 14 at 4:00 p.m. to 5:15 p.m.

Virtual Event

Responsible AI: How Can We Make Artificial Intelligence Work for Everyone?

With James Foulds, Assistant Professor, UMBC

This event is part of the Forbes Corridor Colloquia, sponsored by Pitt Cyber.

With the rising influence of artificial intelligence (AI) and machine learning (ML) systems on many important aspects of our daily lives, there are growing concerns that these systems may have harmful consequences, including the erosion of privacy, the potential for abuse, and unfair or discriminatory behavior.  In this talk, I will discuss the need for responsible AI methods and practices, and the technical and non-technical interventions which can help to ensure that the potential harms and pitfalls of AI technologies are mitigated. I will then focus on my research group's proposed methods for ensuring that machine learning algorithms behave in a fair and equitable manner. I will present methods which help to avoid harmful discrimination against protected groups along lines of gender, race, sexual orientation, class, and disability, and show how to extend AI fairness protections to the marginalized populations at the intersections of these groups.

Dial-In Information

For log-in information, please complete the registration form.

Thursday, October 14 at 4:00 p.m. to 5:15 p.m.

Virtual Event

Event Type

Virtual

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