The document discusses the IEEE P7003 standards aimed at minimizing algorithmic bias, highlighting issues such as confirmation bias in predictive policing and concerns over the fairness of tools like the COMPAS recidivism prediction system. It emphasizes the need for careful consideration of who is affected by algorithmic decisions and calls for transparency in criteria and justifications used in system development. The document invites participation in the P7003 working group to address these challenges in algorithmic bias.

1. IEEE P7003 Algorithmic Bias
Considerations
Minimize unintended, unjustified and unacceptable algorithmic bias
Ansgar Koene, Dr. Ir.
The Hague, 11 June 2018

2. Algorithmic Discrimination
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3. Confirmation Bias: Predictive policing
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 A need for counterfactual double blind trials

4. Complex individuals reduced to
simplistic binary stereotypes
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5. Case study: Recidivism risk prediction
 COMPAS recidivism prediction tool
– Built by a commercial company, Northpointe, Inc.
 Estimates likelihood of criminals re-offending in future
– Inputs: Based on a long questionnaire
– Outputs: Used across US by judges and parole officers
 Are COMPAS’ estimates fair to salient social groups?
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Machine Bias: There’s software used across the
country to predict future criminals. Propublica

6. Case study: Recidivism risk prediction
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Is the algorithm fair
to all groups?
When base rates differ, no non-trivial solution can achieve similar FPR,
FNR, FDR, FOR!

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Open invitation to join the P7003 working group
http://sites.ieee.org/sagroups-7003/

8. Key question when developing or
deploying an algorithmic system
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 Who will be affected?
 What are the decision/optimization criteria?
 How are these criteria justified?
 Are these justifications acceptable in the context where the system is
used?

9. P7003 foundational sections
 Taxonomy of Algorithmic Bias
 Legal frameworks related to Bias
 Psychology of Bias
 Cultural aspects
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P7003 algorithm development sections
 Algorithmic system design stages
 Person categorization and identifying affected population groups
 Assurance of representativeness of testing/training/validation data
 Evaluation of system outcomes
 Evaluation of algorithmic processing
 Assessment of resilience against external manipulation to Bias
 Documentation of criteria, scope and justifications of choices

10. Thank You!
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