Dr. Kim Kyllesbech Larsen discusses the impact of human bias on artificial intelligence (AI) and highlights that AI systems reflect societal biases present in their training data. The document outlines various examples of classification and allocation biases across different sectors, including policing, recruitment, and public service. It emphasizes the necessity for ethical measures in identifying and mitigating biases within AI systems to ensure fairness.

1. Ethical Dilemmas
13th of June, 2019, Bonn, Germany.
Dr. Kim Kyllesbech Larsen, CTIO, T-Mobile Netherlands.
Behind every AI going
wrong is a (hu)man.
AI

2. Dr. Kim K. Larsen / Big Data @ NT 2

3. Bias is so much more than mathematics!
See also Kate Crawford NIPS (NeurlPS) 2017 keynote ”The Trouble with BIas” https://m.youtube.com/watch?v=fMym_BKWQzk&feature=youtu.be

4. 4
Dr. Kim K. Larsen / How do we Humans feel about AI?

5. PEOPLE
LIE
DATA
DON’T or

6. Liar, liar – who’s fake & who’s not?
BA
http://www.whichfaceisreal.com/ and see also https://skymind.ai/wiki/generative-adversarial-network-gan for a great intro to GAN.

7. Selection bias & data gaps.
Representing (hu)man.
White male.
25 - 30 years old.
178 cm.
70 kg.
Student.

8. ≈
≈
( )
( )
Blue
Class
Magenta
Class
…
…
Label
(e.g., Outcome)
Features or attributes
(e.g., defining the policy)
Class tag(s) (e.g., Female vs
Male) – might not be wise to
consider in policy unless policy
requires class differentiation.
Discrete Feature Distribution
(e.g., age range, marital status,
education, etc..)
Continuous Feature
Distribution identified for
example by its mean and
variance (or standard deviation)
Mean
Standard Deviation
Approved
Approved
Rejected
Rejected
Gender Education Income Age Relationship
status
BMI Debt
…
…
“Hidden” attributes
…
Classes
Structure of data we choose and use.
…
…
Sources: non-mathematical representation https://www.linkedin.com/pulse/machine-why-aint-thee-fair-dr-kim-kyllesbech-larsen-/ & for the mathematics
see https://www.linkedin.com/pulse/mathematical-formulation-fairness-ali-bahramisharif/

9. True or False?
TRUE
NEGATIVE
TRUE
POSITIVE
FALSE
POSITIVE
FALSE
NEGATIVE
ActualHappiness
Predicted Happiness
AI/ML
applied to
test data
HAPPY
UNHAPPY
HAPPYUNHAPPY
Our model predict that our
Unhappy customer is Happy
Our model predict that our
Happy customer is Unhappy
See also https://aistrategyblog.com/2018/10/11/machine-why-aint-thee-fair/

10. Behind every AI going wrong is a (hu)man.
NEED TO
“CLEAN” DATA!
QUALITY
GOALS!
Limited computing resources may result in a higher bias. Do you have ethical measures in place to identify biases?
IT STARTS
HERE!
Train
Test
COMPUTING
POWER
LOTS
OF DATA!
Data reflects the society, if biased so will the data be.
ML MODEL /
ARCHITECTURE
Your problem context inherently be unfair/biased. With data cleaning & selection it is very easy to introduce bias.
Models can (& will) amplify biases in training data

11. FALSE POSITIVEFALSE NEGATIVE
Allocation / classification bias (& unfairness).
AI assessment of the risk of re-offending
Data Source: https://github.com/propublica/compas-analysis, see also https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
Stole a kids bike ($80 of value)
prior as juvenile (misdemeanor).
?
Do we treat our
customers the same
irrespective of
race or
gender or
age or
Location, etc.. ?
Shoplifting ($86 of value)
Prior: armed robbery &
misdemeanors as juvenile.

12. Allocation bias (& unfairness).
Minority neighborhoods underserved
Source: https://www.bloomberg.com/graphics/2016-amazon-same-day/
Issues
 Credit / loans.
 Insurances.
 Policing.
 Shops.
 Permits.
 Cleaning.
 Schools.
 Public transport.
 Public areas.
 Public safety.
 Telco services.

13. Classification / allocation bias (& unfairness).
AI-based Recruitment.
Amazon's AI were trained on resumes submitted
to the company over a 10-year period.
https://uk.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women

14. Father is to a doctor as a Mother is to a nurse
Man is to Telecom Engineer as Woman is to homemaker
Boy is to gun as Girl is to Doll
Man is to Manager as Woman is to Assistant
Gender (classification) biases.
https://developers.google.com/machine-learning/fairness-overview/

15. "Gay faces tended to be gender atypical," the researchers said.
"Gay men had narrower jaws and longer noses, while lesbians had larger jaws."
Wang, Y., & Kosinski, M. (in press). Deep neural networks are more accurate than humans at
detecting sexual orientation from facial images. Journal of Personality and Social Psychology
DEEP NEURAL NETWORKS
CAN DETECT
SEXUAL ORIENTATION
FROM YOUR FACE.
(ALLEGEDLY!)
Classification bias (& unfairness).
Quotes;

16. Classification bias (& unfairness).
"law-biding public have a greater degree of resemblance compared with the faces of criminals.”
“We have discovered that a law of normality for faces of non-criminals."
DEEP NEURAL NETWORKS
CAN DETECT
CRIMINALITY FROM YOUR FACE
(ALLEGEDLY!)
Xiaolin Wu & Xi Zhang, “Automated Inference on Criminality using Face Images” (2016); https://arxiv.org/pdf/1611.04135v1.pdf
Quotes;

17. ?
64x64x3
Female
German
Telekom
DNN Architecture e.g., 128/64/32/1 (4 Layers)
Trained on 6,992+ LinkedIn pictures.
TRUE POSITIVES
Male
Polish
Vodafone
FALSE NEGATIVES
What’s your Gender, Nationality & Employer.
How much does your face tell about you?

18. What’s your Gender?
45%
32%
16%
7%

19. Confirmation bias …
Are autonomous cars racially biased?
Source: Benjamin Wilson et al “Predictive Inequity in Object Detection”, https://arxiv.org/pdf/1902.11097.pdf and
https://twitter.com/katecrawford/status/1100958020203409408
Object detection systems of autonomous
cars are better at detecting humans with
light skin than dark skin.
Object detection systems are better at
detecting humans wearing light clothes than
dark clothes at night (& vice versa at day).
Thesis of Wilson, Hoffman & Morgenstern – confirmation bias?
The anti-thesis – that may seems more plausible?
See also Marc Green’s https://www.visualexpert.com/Resources/pedestrian.html

21. THANK YOU!
Acknowledgement
Thanks to many colleagues who have
contributed with valuable insights, discussions
& comments throughout this work.
Also I would like to thank my wife Eva Varadi
for her patience during this work.
Contact:
Email: kim.larsen@t-mobile.nl
Linkedin: www.linkedin.com/in/kimklarsen
Blogs: www.aistrategyblog.com & www.techneconomyblog.com
Twitter: @KimKLarsen
Recommendation:
Source: https://www.amazon.com/Invisible-Women-Exposing-
World-Designed-ebook/dp/B07CQ2NZG6/