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What and Who
Title:Decision Making and The Value of Explanation
Speaker:Kathy Strandburg
coming from:NYU Law
Speakers Bio:
Event Type:SWS Colloquium
Visibility:D1, D2, D3, D4, D5, SWS, RG1
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Level:AG Audience
Date, Time and Location
Date:Friday, 7 July 2017
Duration:90 Minutes
Building:E1 5
Much of the policy and legal debate about algorithmic decision-making has focused on issues of accuracy and bias. Equally important, however, is the question of whether algorithmic decisions are understandable by human observers: whether the relationship between algorithmic inputs and outputs can be explained. Explanation has long been deemed a crucial aspect of accountability, particularly in legal contexts. By requiring that powerful actors explain the bases of their decisions — the logic goes — we reduce the risks of error, abuse, and arbitrariness, thus producing more socially desirable decisions. Decision-making processes employing machine learning algorithms complicate this equation. Such approaches promise to refine and improve the accuracy and efficiency of decision-making processes, but the logic and rationale behind each decision often remains opaque to human understanding. Indeed, at a technical level, it is not clear that all algorithms can be made explainable and, at a normative level, it is an open question when and if the costs of making algorithms explainable outweigh the benefits. This presentation will begin to map out some of the issues that must be addressed in determining in what contexts, and under what constraints, machine learning approaches to governmental decision-making are appropriate.
Name(s):Annika Meiser
EMail:--email address not disclosed on the web
Video Broadcast
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Annika Meiser/MPI-SWS, 07/07/2017 02:48 PM
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Annika Meiser/MPI-SWS, 07/07/2017 02:50 PM
  • Annika Meiser, 07/07/2017 02:50 PM -- Created document.