Studium Generale presenation at TU Eindhoven on 25 October 2017 (http://www.studiumgenerale-eindhoven.nl/nl/agenda/archief/the-age-of-the-algorithm/0/1109/) discussing the impact of algorithmic decision making on modern society and the ethical responsibility of engineers who build these systems

1. The Age of Algorithms
Complex society reduced to
simplistic binary stereotypes?
Ansgar Koene
Horizon Digital Economy Research institute, University of Nottingham
Studium Generale, Eindhoven, 25th October 2017
http://unbias.wp.horizon.ac.uk/

2. Data Driven Society
• Data driven service
optimization
• Evidence based policy
making
2

3. An 18th century idea – Age of Enlightenment
– Scientific Revolution
3
Who defines the targets?
Need to avoid policy based evidence making
Only what is measured has value

4. Algorithmic Society: Growth of Data
4United Nations Economic Commission for Europe, 2013 report

5. Algorithmic Society: Processing capacity
5

6. Algorithmic Society: loss of human agency
6
Google search =
f(PageRank, 300+ other factors)

7. Algorithmic Discrimination
7

8. Complex individuals reduced to
simplistic binary stereotypes
8

9. Use case for inferred intimate data
9

10. • Maatschappelijk verantwoord innoveren (MVI)
Responsible Research and Innovation
10

11. UnBias project: education, design and
regulation for fair algorithms
11
http://unbias.wp.horizon.ac.uk/
Stakeholder workshops
Youth Juries

12. Algorithm fairness – a wicked problem
This is not a technical problem – it is socio-technical
• Almost all applications involve some form of
resource limitation (e.g. human attention)
• Fairness is culture and context dependent
• Values of users are often not explicitly specified
12

13. Letting users choose the Algorithm

14. Evaluating fairness from outputs only
Most preferred
Least preferred

15. Evaluating fairness with knowledge about
the algorithm decision principles
 A1: minimise disparity while
guaranteeing at least 70% of
maximum possible total
 A2: maximise the minimum
individual outcome while
guaranteeing at least 70% of
maximum possible total
 A3: maximise total
 A4: maximise the minimum
individual outcome
 A5: minimise disparity
Most preferred
Least preferred

16. Professional associations taking responsibility
16

17. 17

18. Ethically Aligned Design
A Vision for Prioritizing Human Wellbeing with
Artificial Intelligence and Autonomous Systems
18

19. IEEE-SA Standards Projects
• IEEE P7000: Model Process for Addressing Ethical Concerns
During System Design
• IEEE P7001: Transparency of Autonomous Systems
• IEEE P7002: Data Privacy Process
• IEEE P7003: Algorithmic Bias Considerations
• IEEE P7004: Standard on Child and Student Data
Governance
• IEEE P7005: Standard on Employer Data Governance
• IEEE P7006: Standard on Personal Data AI Agent Working
Group
• IEEE P7007: Ontological Standard for Ethically Driven
Robotics and Automation Systems
• IEEE P7008: Standard for Ethically Driven Nudging for
Robotic, Intelligent and Autonomous Systems
• IEEE P7009: Standard for Fail-Safe Design of Autonomous
and Semi-Autonomous Systems
• IEEE P7010: Wellbeing Metrics Standard for Ethical Artificial
Intelligence and Autonomous Systems
19

20. 20
Open invitation to join the P7003 working group
http://sites.ieee.org/sagroups-7003/

21. ACM Principles on Algorithmic
Transparency and Accountability
• Awareness
• Access and Redress
• Accountability
• Explanation
• Data Provenance
• Auditability
• Validation and Testing
21
IEEE – ACM joint panel on Professional
Responsibility at RightsCon2018

22. Contributing to the policy debate
22

23. Parliamentary inquiries
23

24. Engage with public media
24

25. Thank you – questions?
25
http://unbias.wp.horizon.ac.uk/
ansgar.koene@nottingham.ac.uk