Presentation by Ansgar Koene (Chair of IEEE P7003 – Algorithmic Bias Working Group, University of Notthingham, UK) at the event "Incorporating Ethical Considerations in Autonomous & Intelligent Systems (A/IS) – Policy & Industry Requirements in the Algorithmic Age". The event took place on 11 June 2018 and was jointly organized by IEEE Standards Association (IEEE-SA) and the Delft Design for Values Institute (DDFV). For more info see http://designforvalues.tudelft.nl/event/incorporating-ethical-considerations-in-ai-policy-industry-requirements/

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
 Person categorization and identifying affected population groups
 Legal frameworks related to Bias
 Psychology of Bias
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P7003 algorithm development sections
 Algorithmic system design stages
 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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