This document discusses fairness in automated decision systems. It outlines challenges like algorithmic bias from training on biased data. It presents fairness metrics like disparate impact ratio and statistical parity that quantify group fairness. Approaches to mitigate bias include pre-processing to learn fair representations, in-processing like regularization and adversarial debiasing, and post-processing like calibrated equal odds. A case study describes LinkedIn's fairness architecture using a fairness analyzer and mitigation trainer to learn from previous outputs and apply post-processing corrections. The summary emphasizes the need to consider bias from data selection and regularization during model development as well as continuous fairness evaluation after deployment.

1. Fairness in Automated Decision Systems
Data Protectionin Software Development- WS 21/22
Dr. AmirshayanAhmadian
• Sai Sri Praveen Gadiyaram
• Sowjanya Chennamaneni
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2. OUTLINE
1. Motivation
2. Background
3. Challenges
4. Fairness metrics
5. Fairness approaches
6. Fairness Architecture
7. Adversarial Debiasing
8. Case-study
9. Summary
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3. MOTIVATION
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4. BACKGROUND
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AutomatedDecision Systems : Decisionstaken by the systems without any human involvement.
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5. .
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Name Education Race Gender Zip code
John Bachelor's White Male 56783
Mary Bachelor's White Female 45673
Annie Bachelor's Black Female 15674
Jack Bachelor's Black Male 13456
TrainedML
model
Predictions
Eligible
Yes
Yes
No
No
BACKGROUND
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• Protected Attributes: Can be a source of social bias. For example, age, race and gender
• Unprotected Attributes: Attributes that are not protected.

6. • Fairness ThroughAwareness:
An algorithm is fair if it gives similar predictions to similar individuals.
It is also known as Individual Fairness.
• DemographicParity :
The likelihood of a positive outcome should be the same regardless of whether the person is in
the protected (e.g., female) group. It comes under Group Fairness
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BACKGROUND

7. CHALLENGES
• Decisions are dependent on the "Data".
• A model trained on biased data can produce
unfair and discriminatory decisions.
• Algorithm bias
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Interpretabilityof models
Universalfairnessdefinition
Universalfairnessmetrics
Concretebias mitigation
Lack of social fairness
Data Algorithm Decision
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8. FAIRNESS METRICS
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S. No Metric Definition formula
1. Disparate Impact
Ratio(DIR)
Ratio of the rate of positive outcome for
minority group to majority group
P(y=1|x=0)/P(y=1|x=1)
2. Statistical Parity
Difference(SPD)
Difference of the rate of positive outcome for
minority group to majority group
P(y=1|x=0) - P(y=1|x=1)
• Quantifying Group Fairness
• 2 Groups:
• Male, x=1
• Female, x=0
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9. FAIRNESS METRICS
• Effectively search the attribute space
• Test-intensive – Can't be done manually
• Generate automatic Test cases for
indiscrimination.
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Protected
Non-
protected
Individual
discrimination
• path coverage and individual discrimination
• Always negate a non-protected attribute
• Success score= |setof generated testcasesfrom unprotected attributes|
|individual discrimination detected|
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10. FAIRNESS APPROACHES
• Achieving Fairness == Mitigating Bias
• Consider fairness constraints in decision system building
• 3 different approaches to include fairness constraints
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Pre-processing
• Learn fair
Representations
• Remove
disparate impact
In-processing
• Regularization
• Train
independently
and tune
together
• Adversarial
debiasing
Post-processing
• Calibrated Equal
Odds
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11. FAIRNESS ARCHITECTURE
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12. ADVERSARIAL DE-BIASING
• Optimize with respect to the fairness loss
• Adversarial NN can predict the attributes which leads to an unfair decision.
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Gender ??
Race ??
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13. CASE-STUDY
• Fairness architecture at LinkedIn
• "Equal opportunities to equally
qualified members"
• Model agnostic and scalable
• Fair model analyzer:
• Specialized test set
• Creates the fairness correction
• Mitigation trainer
• Learns from the previous step output
• post-processing approach
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14. SUMMARY
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• 100 % data-driven systems
are not ideal
• Focus on Bias
• Data source & variety
• Regularization
• Lack of Fairness knowledge
• Social Fairness, ethics
• Fairness constraints
• Context & diversity
• Continuous fairness evaluation
• Testing after deployment
• Optimize fairness metrics
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15. REFERENCES
• Mehrabi, Ninareh & Morstatter, Fred & Saxena, Nripsuta & Lerman, Kristina & Galstyan, Aram. (2019). A Survey on Bias and Fairness in Machine Learning.
• Kulshrestha, Ankit and Ilya Safro. “CONFAIR: Configurable and Interpretable Algorithmic Fairness.” (2021).
• https://engineering.linkedin.com/blog/2022/a-closer-look-at-how-linkedin-integrates-fairness-into-its-ai-pr
• https://www.slideshare.net/godatadriven/fairness-in-ai-ddsw-2019
• https://blog.insightdatascience.com/tackling-discrimination-in-machine-learning-5c95fde95e95
• https://www.persistent.com/blogs/fairness-in-ai-systems/
• Aniya Aggarwal, Pranay Lohia, Seema Nagar, Kuntal Dey, and Diptikalyan Saha. 2019. Black box fairness testing of machine learning models. In <i>Proceedings of the
2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering</i> (<i>ESEC/FSE
2019</i>). Association for Computing Machinery, New York, NY, USA, 625–635. DOI:https://doi.org/10.1145/3338906.3338937
• Ignore icons created by Parzival’ 1997 - Flaticon
• Ethics icons created by Freepik - Flaticon
• Testing icons created by surang – Flaticon
• https://www.datanami.com/2018/09/05/how-to-build-a-better-machine-learning-pipeline/
• https://www.oreilly.com/library/view/ai-fairness/9781492077664/ch01.html
• https://fortune.com/2018/10/10/amazon-ai-recruitment-bias-women-sexist
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16. 26-01-2022
Thank you.
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