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Ethical machines: data mining and fairness – the optimistic view

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Ethical machines: data mining and fairness – the optimistic view

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Introductory remarks to a seminar on algorithms and discrimination arranged by the Academy of Finland Centre of Excellence in the Philosophy of the Social Sciences at the University of Helsinki, 2016-05-02.

Introductory remarks to a seminar on algorithms and discrimination arranged by the Academy of Finland Centre of Excellence in the Philosophy of the Social Sciences at the University of Helsinki, 2016-05-02.

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Ethical machines: data mining and fairness – the optimistic view

  1. 1. Ethical machines: data mining and fairness – the optimistic view Anna Ronkainen chief scientist, TrademarkNow it’s complicated, UU of Helsinki & Turku @ ronkaine 2016-05-02
  2. 2. My three points 1.  people aren’t exactly perfect, either, and sometimes algorithms can be an improvement 2.  different types of algorithms needed for arriving at decisions and validating/ disproving them 3.  data protection law about automated decision-making needs to be taken seriously
  3. 3. Heuristics or biases? (Dhami 2003)
  4. 4. Sometimes people fail in unexpected ways... (Danziger et al (2011): Extraneous Factors in Judicial Decisions)
  5. 5. Systems 1 and 2 in legal reasoning: interaction System 1: making the decision System 2: validation and justification (Ronkainen 2011)
  6. 6. Implications for algorithms (hypothesis) -  System-1-like processes cannot be captured reliably with GOFAI -> machine learning and other statistical approaches needed -  the System 2 part (finding supporting arguments and validating/falsifying the decision candidate) can (and should) be implemented with rule-based GOFAI for accountability, maintainability etc etc etc
  7. 7. Taking data protection seriously? (2016 EU General Data Protection Regulation)
  8. 8. Seriously-seriously? (1995 EU Data Protection Directive 95/46/EC)
  9. 9. My three points 1.  people aren’t exactly perfect, either, and sometimes algorithms can be an improvement 2.  different types of algorithms needed for arriving at decisions and validating/ disproving them 3.  data protection law about automated decision-making needs to be taken seriously
  10. 10. Thank you!

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