FairML : ToolBox for diagnosing bias in predictive modeling
Name
980349219-MIT.pdf
Description
Full printable version
Size
9.96 MB
Format
Adobe PDF
Checksum (MD5)
298e1dd68d0b87ae2e1e091fcfe37234
Author(s)
Adebayo, Julius A
Advisor(s)
Lalana Kagal, Harold Abelson and Alex "Sandy" Pentland.
Alternative Title
ToolBox for diagnosing bias in predictive modeling
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
Predictive models are increasingly deployed for the purpose of determining access to services such as credit, insurance, and employment. Despite societal gains in efficiency and productivity through deployment of these models, potential systemic flaws have not been fully addressed, particularly the potential for unintentional discrimination. This discrimination could be on the basis of race, gender, religion, sexual orientation, or other characteristics. This thesis addresses the question: how can an analyst determine the relative significance of the inputs to a black-box predictive model in order to assess the model's fairness (or discriminatory extent)? We present FairML, an end-to- end toolbox for auditing predictive models by quantifying the relative significance of the model's inputs. FairML leverages model compression and four input ranking algorithms to quantify a model's relative predictive dependence on its inputs. The relative significance of the inputs to a predictive model can then be used to assess the fairness (or discriminatory extent) of such a model. With FairML, analysts can more easily audit cumbersome predictive models that are difficult to interpret.
Description
Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program, 2016.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 94-99).
Subjects
Institute for Data, Systems, and Society.
Engineering Systems Division.
Technology and Policy Program.
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Engineering Systems Division
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Technology and Policy Program
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