The document discusses the role of algorithms in decision-making, highlighting that they can sometimes improve on human imperfections. It emphasizes the necessity for different types of algorithms for decision-making and validation, along with the importance of adhering to data protection laws regarding automated decisions. The author argues for a combination of machine learning for initial decision-making and rule-based systems for accountability.

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. 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. Heuristics
or biases?
(Dhami 2003)

4. Sometimes people fail in unexpected
ways...
(Danziger et al (2011): Extraneous Factors
in Judicial Decisions)

5. Systems 1 and 2 in legal reasoning:
interaction
System 1:
making the
decision
System 2:
validation and
justification
(Ronkainen 2011)

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. Taking data protection seriously?
(2016 EU General Data Protection Regulation)

8. Seriously-seriously?
(1995 EU Data Protection Directive 95/46/EC)

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. Thank you!