The document discusses the challenges and ethical considerations related to AI bias, highlighting how human biases and insufficiently diverse data sets can lead to flawed outcomes in AI applications, such as product design and automated systems. It emphasizes the need for ethical guidelines, human involvement, and transparency in the development of AI technologies, alongside a consideration of various regulatory frameworks. Key initiatives from organizations like IEEE are cited to provide guidance on responsible AI design and implementation.

1. Technology for everyone
By Marion Mulder
#ToonTechTalks 27 September 2018
AI Bias and Ethics
Image source: https://www.silvergroup.asia/2012/10/10/new-tech-can-also-challenge-the-40-consumer/

2. Apparently I’m a sad angry
“world ethic” male…
Marion Mulder

3. we all want to
make great
products

4. But sometimes
along the line
things don’t quite
turn out right (yet)

5. Some examples of where it did not go quite right….
Sales of helmet in
Asia (of US brand)
were low…
Turns out the
average head size
in the database was
not diverse enough
Image source: Che-Wei Wang – mindful algorithms: the new role of the designer in generative design - TNW conference 2018

6. Some examples of where it did not go quite right….
Automatic translations are gender biased

7. Some examples of where it did not go quite right….
Google’s image
classifier wrongly
assigned black people

8. Some examples of where it did not go quite right….

9. Or they turn our
right (do they?),
but…

10. Source: Average faces of men and women around the world.
Average faces of the world
(really??)

14. Source: Virginia Dignum – UMEA University

15. Criminal face detection AI
Source: Juriaan van Diggelen - TNO

17. Source: https://ilga.org/maps-sexual-orientation-laws

20. People blindly trust a robot to lead them out of a building In case of a fire emergency.
[Overtrust of robots in emergency evacuation scenarios, P Robinette, W Li, R Allen, AM Howard, AR Wagner - Human-Robot Interaction, 2016]
Source: Juriaan van Diggelen - TNO

21. So things go ‘wrong’
because…

22. So things go ‘wrong’ because…?
• Human bias
• Size of data set
• Source of data
• Completeness
• How well is representative
• Intended use (was data set originally setup for what you are now trying to do with it)
• Is historical data accurate*
• labelling
• Ontology isn’t always binary
• Complexity reduced to binary
• Demographical, geographical, cultural differences on definition, law and
interpretation
• .....

23. Image Source: Valerie Frissen – Erasmus University /SIDN fonds at Fraiday event (presentation on slideshare)

24. Lets start with BIAS

25. Dealing with human BIAS
• Bias: expectations derived from experience
regularities in the world. Knowing what
programmer means, including that most are
male.
• Stereotypes: biases based on regularities we
do not wish to persist. Knowing that (most)
programmes are male.
• Prejudice: action on stereotypes. Hiring only
male programmers.
Source: Joanna Bryson at AI Summit - Caliskan, Bryson &Narayanan 2017
Ada Lovelace

26. Source: beperktzicht.nl

27. Source: beperktzicht.nl

28. Source: google search result for start-up founder

29. ✅ Be aware of confirmation bias
tendency to search for,
interpret, favor, and recall
information in a way that
confirms one's pre-existing
beliefs or hypotheses.
Source: Virginia Dignum – UMEA University

30. Source: Virginia Dignum – UMEA University
✅ realise: things won’t always binary or clear

31. Laws, regulation,
Self regulations

32. ✅ Laws, regulation, self regulation
• Values
• Code of conduct
• GDPR
• IEEE Ethically Aligned Design
• UN Human Rights Declaration
• Global sustainability goals
• Product liability laws
• AI on EU level initiatives
• Ethical Guidelines
• Responsible AI frameworks

33. IEEE Ethically Aligned Design
A Vision for Prioritizing Human Well-being with Autonomous and
Intelligent Systems
• 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 for Transparent Employer Data Governance
• IEEE P7006™ - Standard for Personal Data Artificial Intelligence (AI) Agent
• IEEE P7007™ - Ontological Standard for Ethically Driven Robotics and
Automation Systems
• IEEE P7008™ - Standard for Ethically Driven Nudging for Robotic,
Intelligent, and Automation 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
• IEEE P7011 - Standard for the Process of Identifying and Rating the
Trustworthiness of News Sources
• IEEE P7012 - Standard for Machine Readable Personal Privacy Terms
• IEEE P7013 - Inclusion and Application Standards for Automated Facial
Analysis Technology
See https://ethicsinaction.ieee.org/

34. Human rights

36. ✅ product liability laws
• AI is a product,
product liability laws apply

37. Source: Virginia Dignum – UMEA University

38. Source: Virginia Dignum – UMEA University

39. Source: Rumman Chowdhury - Accenture

40. Source: Rumman Chowdhury - Accenture

42. Some other initiatives
worth following

43. Source: Virginia Dignum – UMEA University

44. Google
Introducing the
Inclusive
Images
Competition
Thursday, September
6, 2018
Posted by Tulsee
Doshi, Product
Manager, Google AI

45. https://futureproof.community/circle/tech-for-good/challenges/denk-mee-over-humaan-datagebruik-met-behulp-
van-equality-matters

47. Design, human in the
loop

48. ✅ Design Human in the Loop
systems
• Consult non-technical experts and
resources
• Involve diverse groups in your design,
data gathering, testing
• Transparency (explainable) of your
decision making and algorithms
• Analyze –synthetize –evaluate -repeat

49. ✅ Key questions when developing or deploying an algorithmic system
• Who will be affected?
• What are the decisions/optimisation criteria?
• How are these criteria justified?
• Are these justifications acceptable in the context where the system is
used?
• How are we training our algorithm?
• Does training data resemble the context of use?
IEEE P7003 Algorithmic Bias Considerations by Ansgar Koene
IEEE standard Algorithmic bias https://standards.ieee.org/project/7003.html

50. ✅ Be explicit & transparent
• Question your options and choices
• Motivate your choices
• Document your choices and options
Source: Virginia Dignum – UMEA University

51. Lets be critical about
our data

52. ✅ Lets check some data!

53. In summary
We are all biased, so lets be aware lost of laws, regulations and other
sources for guidance
And lets be critical about the data
we use
Lets design, test and implement with
humans in the loop

54. AI summit 11 oct
full morning about AI ethics topics
Wilder will go into data and code site
next
#Aiethics #databias #responsibleAI #responsibleData

55. Sources

56. Reference Resources
• Joanna Bryson
• Virginia Dignum
• Rumman Chowdhury
• Catelijne Muller - EU High Level Expert Group on AI
• IEEE.org
• Ada-AI
• WCAG Accessibility guidelines (https://www.w3.org/TR/WCAG/)
• Incorporating Ethical Considerations in Autonomous & Intelligent Systems
• #Aiethics #databias #responsibleAI #responsibleData
• See my twitter list https://twitter.com/muldimedia/lists/ai-ethics/members
• Of check my pinterest boards https://nl.pinterest.com/muldimedia/artificial-intelligence/ or
https://nl.pinterest.com/muldimedia/artificial-intelligence/ai-ethics-responsible-ai-bias-and-privacy/
• https://futurism-com.cdn.ampproject.org/c/s/futurism.com/artificial-intelligence-benefits-select-few/amp/
• http://leginfo.legislature.ca.gov/faces/billTextClient.xhtml?bill_id=201720180ACR215
• https://ec.europa.eu/jrc/communities/community/humaint/event/humaint-winter-school-ai-ethical-social-legal-and-economic-impact
• https://theintercept.com/2018/09/06/nypd-surveillance-camera-skin-tone-search/