FairML : ToolBox for diagnosing bias in predictive modeling
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.
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | أطروحة |
| اللغة: | eng |
| منشور في: |
Massachusetts Institute of Technology
2017
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/1721.1/108212 |
| _version_ | 1826213085688365056 |
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| author | Adebayo, Julius A |
| author2 | Lalana Kagal, Harold Abelson and Alex "Sandy" Pentland. |
| author_facet | Lalana Kagal, Harold Abelson and Alex "Sandy" Pentland. Adebayo, Julius A |
| author_sort | Adebayo, Julius A |
| collection | MIT |
| 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. |
| first_indexed | 2024-09-23T15:43:06Z |
| format | Thesis |
| id | mit-1721.1/108212 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:43:06Z |
| publishDate | 2017 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1082122022-01-31T21:23:37Z FairML : ToolBox for diagnosing bias in predictive modeling ToolBox for diagnosing bias in predictive modeling Adebayo, Julius A Lalana Kagal, Harold Abelson and Alex "Sandy" Pentland. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. 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 Institute for Data, Systems, and Society. Engineering Systems Division. Technology and Policy Program. Electrical Engineering and Computer Science. 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). 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. by Julius A. Adebayo. S.M. in Technology and Policy S.M. 2017-04-18T16:37:35Z 2017-04-18T16:37:35Z 2016 2016 Thesis http://hdl.handle.net/1721.1/108212 980349219 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 99 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Institute for Data, Systems, and Society. Engineering Systems Division. Technology and Policy Program. Electrical Engineering and Computer Science. Adebayo, Julius A FairML : ToolBox for diagnosing bias in predictive modeling |
| title | FairML : ToolBox for diagnosing bias in predictive modeling |
| title_full | FairML : ToolBox for diagnosing bias in predictive modeling |
| title_fullStr | FairML : ToolBox for diagnosing bias in predictive modeling |
| title_full_unstemmed | FairML : ToolBox for diagnosing bias in predictive modeling |
| title_short | FairML : ToolBox for diagnosing bias in predictive modeling |
| title_sort | fairml toolbox for diagnosing bias in predictive modeling |
| topic | Institute for Data, Systems, and Society. Engineering Systems Division. Technology and Policy Program. Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/108212 |
| work_keys_str_mv | AT adebayojuliusa fairmltoolboxfordiagnosingbiasinpredictivemodeling AT adebayojuliusa toolboxfordiagnosingbiasinpredictivemodeling |