ethical machines: data mining and fairness – the optimistic view

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

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

Heuristics or biases?

(Dhami 2003)

Sometimes people fail in unexpected ways...

(Danziger et al (2011):Extraneous Factors in Judicial Decisions)

Systems 1 and 2 in legal reasoning: interaction System 1: making the decision System 2: validation and justification

(Ronkainen2011)

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

Taking data protection seriously?

(2016 EU General Data Protection Regulation)

Seriously-seriously?

(1995 EU Data Protection Directive 95/46/EC)

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

Thank you!