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Algorithmic bias: introduction


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Algorithms are the new boogie men when it comes to social control and institutional discrimination. Researchers and now policy-makers decry bias and lack of accountability in the algorithms and predictive analytics that determine who gets hired for a job, who can get a loan, who qualifies for insurance, and even who goes to jail. This short introduction to a panel describes the landscape, lists key questions regarding bias in analytics, and describes the difficulties with the common suggestion that algorithms and code be published. The actual panel was recorded and posted as a video at

Published in: Technology
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Algorithmic bias: introduction

  1. 1. Algorithmic Bias: Where It Comes From and What to Do About It (Introduction) 26 March 2017 Andy Oram, Editor, O’Reilly Media This work is licensed under the Creative Commons Attribution 4.0 International License.
  2. 2. The Discoveries 2 Algorithmic Bias — Andy Oram
  3. 3. The Hype 3 Algorithmic Bias — Andy Oram
  4. 4. The Books 4 Algorithmic Bias — Andy Oram
  5. 5. The Feds 5 Algorithmic Bias — Andy Oram
  6. 6. Association for Computing Machinery principles 6 Algorithmic Bias — Andy Oram
  7. 7. Questions addressed in this talk  Why do algorithms discriminate? 7  What is unfair bias?  Who is in control of the data?  How can outsiders validate algorithms?  Where does free software play a role?  How should we use algorithms, given these risks? Algorithmic Bias — Andy Oram
  8. 8. The role of free software 8 Algorithmic Bias — Andy Oram htttps://
  9. 9. Barriers to opening the source code  Selfish actors might game the system 9  Machine learning doesn't produce human-readable decision processes  Can't keep up with constant tweaks to the algorithms  The devil often lies in the data  Trade secrets and other legal controls Algorithmic Bias — Andy Oram
  10. 10. Barriers to using the source code if it's open  How do you know at all if an algorithm is judging you? 10  Who can understand the algorithms?  Power imbalances--do you dare to challenge the owner of the algorithm?  The devil often lies in the data Algorithmic Bias — Andy Oram