This document discusses fairness in artificial intelligence and machine learning. It begins by noting that AI can encode and amplify human biases, leading to unfair outcomes at scale. It then discusses different ways to measure fairness, such as demographic parity and equality of opportunity. The document presents an example of predicting income using census data and shows how the initial model is unfair, with low probabilities for certain groups. It explores potential sources of bias in systems and methods for enforcing fairness, such as adversarial training to iteratively train a classifier and adversarial model. The document emphasizes that fairness is complex with many approaches and no single solution, requiring active work to avoid unfair outcomes.

1. Fairness in AI
“Facilis descensus Averno”
Henk Griffioen - 2019-05-07

2. Data Scientists have to put it the work
to not end up in ML hell
2
“The gates of hell are open night and day;
Smooth the descent, and easy is the way:
But to return, and view the cheerful skies,
In this the task and mighty labor lies.”
The Works of Virgil (John Dryden)

3. The impact of AI on society is not all good. AI can encode and
amplify human biases, leading to unfair outcomes at scale
3

4. Fairness is a hot topic and gaining traction!
4
https://fairmlclass.github.io/

5. Let’s work through an example
5

6. Predict if income exceeds $50K/year from 1994 census data
with sensitive attributes
6

7. Getting a predictive model is easy!
But is it fair?
7

8. How to measure fairness?
8

9. Ratio of
• probability of a positive outcome given the sensitive attribute being true;
• probability of a positive outcome given the sensitive attribute being false;
is no less than p:100
p%-rule: measure demographic parity
9
40% : 50% = 80%10% : 50% = 20%

10. Our model is unfair: low probability of high income for black
people and women
10

11. How are we getting bias in our systems?
11

12. • Skewed sample: initial bias that compounds over time
• Tainted examples: bias in the data caused by humans
• Sample size disparity: minority groups not as well represented
• Limited features: less informative or accurate data collected on minority groups
• Proxies: data implicitly encoding sensitive attributes
• …
Many reasons why bias is creeping into our systems
12Barocas & Selbst, 2016

13. Intermezzo:
Dutch tax authorities in the news
13

14. Ethnic profiling by the
Dutch tax authorities
14
Profiling people for fraud
“A daycare center in Almere sounded the alarm when
only non-Dutch parents were confronted with
discontinuation of childcare allowances…
…The Tax and Customs Administration says that it
uses the data on Dutch nationality or non-Dutch
nationality in the so-called automatic risk selection
for fraud.”
https://www.nrc.nl/nieuws/2019/05/20/autoriteit
-persoonsgegevens-onderzoekt-mogelijke-
discriminatie-door-belastingdienst-a3960840

15. Is it enough to leave out
data on (second)
nationality?
15
…In a response, the Tax and Customs Administration
states that the information about the (second) nationality
of parents or intermediary is not used in this
investigation…
“Since 2014, a second nationality with Dutch nationality
is no longer included in the basic registration. This has
been introduced to prevent discrimination for people
with dual nationality.”
Is this enough to
assure that non-Dutch
parents in Almere will
not suffer another tax
injustice?
Towards a fair future?

16. The model is still unfair
without sensitive data.
Biases are still encoded
by proxies in the dataset!
16

17. How can we enforce fairness?

18. Start out with our normal (biased) classifier…
18
Louppe, 2017

19. … and add an adversarial classifier as a fairness referee
19
Louppe, 2017

20. Warming up: train classifier to predict income
20

21. Warming up: train adversarial to detect unfairness
21
Louppe, 2017

22. Adversarial training: iteratively train classifier and adversarial
22
Louppe, 2017

23. After enough training rounds the classifier gives fair income
predictions!
23
https://blog.godatadriven.com/fairness-in-ml

24. Putting in the work

25. Demographics parity
• Decision uncorrelated with sensitive attribute
Equality of opportunity
• Outcome probability is same for different groups
No mathematical formulation of fairness.
There are many (conflicting) measures
25http://www.ece.ubc.ca/~mjulia/publications/Fairness_Definitions_
Explained_2018.pdf

26. Many ML fairness approaches
26
https://dzone.com/articles/machine-learning-
models-bias-mitigation-strategies

27. • Identify product goals
• What? For whom?
• Get the right people in the room
• Identify stakeholders
• Who might be harmed? How?
• Select a fairness approach
• How to measure? What interaction?
• Analyze and evaluate your system
• What decisions are made?
• Mitigates issues
• Intervention needed?
• Monitor continuously and escalation plans
• Auditing and transparency
• Who certifies your system?
Fairness should be part of your product process and larger
strategy
27
https://www.slideshare.net/KrishnaramKenthapadi/
fairnessaware-machine-learning-practical-
challenges-and-lessons-learned-www-2019-tutorial

28. Fairness is far from being solved and needs active work!
28

29. Artificial Intelligence at Google: Our Principles - Objectives for AI applications
1. Be socially beneficial.
2. Avoid creating or reinforcing unfair bias.
3. Be built and tested for safety.
4. Be accountable to people.
5. Incorporate privacy design principles.
6. Uphold high standards of scientific excellence.
7. Be made available for uses that accord with these principles.
Even big companies can’t always practice what they preach
29
https://ai.google/principles/

30. Data Scientists have to put it the work
to not end up in ML hell
30
“The gates of hell are open night and day;
Smooth the descent, and easy is the way:
But to return, and view the cheerful skies,
In this the task and mighty labor lies.”
The Works of Virgil (John Dryden)

31. An ethics checklist for data scientists
• http://deon.drivendata.org/
Tutorial on fairness for products
• sites.google.com/view/wsdm19-fairness-tutorial
Community concerned with fairness in ML
• www.fatml.org
Our blogs
• blog.godatadriven.com/fairness-in-ml
• blog.godatadriven.com/fairness-in-pytorch
Where to go from here?
31