Algorithmic Accountability and Transparency and Other Alternatives to Digital Destiny

Speaker:  Jeanna N Matthews – Potsdam, NY, United States
Topic(s):  Computational Theory, Algorithms and Mathematics

Abstract

Big-data trained algorithms are increasingly used to make big decisions about people's lives, such as who gets loans, whose résumés are reviewed by humans for possible employment, and even the length of prison terms. While algorithmic decision making can offer benefits in terms of speed, efficiency, and even fairness, there is a common misconception that algorithms automatically result in unbiased decisions. In reality, inscrutable algorithms can also unfairly limit opportunities, restrict services, and even improperly curtail liberty. In this talk, I will discuss real cases illustrating the problem as well as key principles for achieving algorithmic accountability and transparency including awareness, access and redress, accountability,  explanation, data provenance, auditability, validation and testing.  I will also discuss the real challenges- technical and societal - to realizing these principles in practice as well as promising research that is advancing our ability to do so.

About this Lecture

Number of Slides:  30
Duration:  50 minutes
Languages Available:  English, Spanish
Last Updated: 

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