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Fairness and Transparency in Machine Learning
The document discusses the challenges of fairness and transparency in machine learning, highlighting that algorithms can perpetuate or mitigate biases found in training data. It uses the NYC stop-and-frisk dataset as a case study to illustrate the complexities of designing fair algorithms while avoiding discrimination. Key takeaways emphasize the necessity of recognizing implicit biases, analyzing sensitive data securely, and leveraging machine learning to enhance fairness in decision-making processes.
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Fairness and Transparency in Machine Learning
- 1. Fairness and transparencyin machine learning Tools and techniques PyData Berlin – 2017 Andreas Dewes (@japh44)
- 2.
- 3. Fairness in MachineLearning • Fairness is not a technological problem, but unfair behavior can be replicated / automated using technology. • Machine learning systems are not per se fair or unfair, but have the potential to be either depending on how we use them. • We have a chance to eliminate unfairness by using machine learning and data analysis to make personal biases explicit and design systems that eliminate it!
- 4. Discrimination Discrimination is treatmentor consideration of, or making a distinction in favor of or against, a person or thing based on the group, class, or category to which that person or thing is perceived to belong to rather than on individual merit. Protected attributes (examples): Ethnicity, Gender, Sexual Orientation, ...
- 5. When is aprocess discriminating? Disparate Impact: Adverse impact of a process C on a given group X 𝑃 𝐶 = 𝑌𝐸𝑆 𝑋 = 0 𝑃 𝐶 = 𝑌𝐸𝑆 𝑋 = 1 < τ see e.g. "Certifying and Removing Disparate Impact" M. Feldman et. al.
- 6. Estimating withreal-world data τ = 𝑐/ 𝑎 + 𝑐 𝑑/ 𝑏 + 𝑑 c a d b
- 7. Alternative Approaches: Individual-Based Fairness 𝑓𝑥1 − 𝑓 𝑥2 ≤ 𝐿 𝑥1 − 𝑥2 similar individual => similar treatment!
- 8. Let‘s try to design afair & transparent algorithm
- 9. NYC Stop & FriskDataset https://gist.github.com/dannguyen/67ece10c6132282b1da2 • Design a stop & frisk algorithm that is as fair as possible • Ensure it fulfills the other goals that we have for it
- 10. InputVariables appearance-related behavior-related Attribute Name Description Age SUSPECT'SAGE Weight SUSPECT'S WEIGHT Ht_feet SUSPECT'S HEIGHT (FEET) Eyecolor SUSPECT'S EYE COLOR Haircolor SUSPECT'S HAIRCOLOR Race SUSPECT'S RACE Sex SUSPECT'S SEX Build SUSPECT'S BUILD CS_Cloth WEARING CLOTHES COMMONLY USED IN A CRIME CS_Objcs CARRYING SUSPICIOUS OBJECT CS_Bulge SUSPICIOUS BULGE CS_Descr FITS A RELEVANT DESCRIPTION RF_Attir INAPPROPRIATE ATTIRE FOR SEASON Attribute Name Description ac_evasv EVASIVE RESPONSE TO QUESTIONING ac_assoc ASSOCIATING WITH KNOWN CRIMINALS cs_lkout SUSPECT ACTING AS A LOOKOUT cs_drgtr ACTIONS INDICATIVE OF A DRUG TRANSACTION cs_casng CASING A VICTIM OR LOCATION cs_vcrim VIOLENT CRIME SUSPECTED ac_cgdir CHANGE DIRECTION AT SIGHT OF OFFICER cs_furtv FURTIVE MOVEMENTS ac_stsnd SIGHTS OR SOUNDS OF CRIMINAL ACTIVITY rf_othsw OTHER SUSPICION OF WEAPONS rf_knowl KNOWLEDGE OF SUSPECTS PRIOR CRIMINAL BEHAVIOR rf_vcact ACTIONS OF ENGAGING IN A VIOLENT CRIME rf_verbl VERBAL THREATS BY SUSPECT Attribute Name Description inout WAS STOP INSIDE OR OUTSIDE? trhsloc WAS LOCATION HOUSING OR TRANSIT AUTHORITY? timestop TIME OF STOP (HH:MM) pct PRECINCT OF STOP (FROM 1 TO 123) ac_prox m PROXIMITY TO SCENE OF OFFENSE cs_other OTHER ac_rept REPORT BY VICTIM / WITNESS / OFFICER ac_inves ONGOING INVESTIGATION ac_incid AREA HAS HIGH CRIME INCIDENCE ac_time TIME OF DAY FITS CRIME INCIDENCE circumstance-related ?
- 11. Process Model Possible goals: Builda system that decides whether to frisk someone or Try to maximize discovery of criminals while not bothering law-abiding citizens. Do not discriminate against individual groups of people.
- 12. Choosing A LossFunction Give a reward α if our algorithm correctly identifies a person to frisk Give a penalty -1 if our algorithm wrongly identifies a person to frisk α (weight parameter) „It‘s okay to frisk α +1 people to find one criminal“.
- 13. Measuring Fairness viaDisparateTreatment
- 14. Building a FirstModel Clean input data Select attributes Convert to binary values using „one hot“ method Train a classifier on the target value Measure the score and discrimination metrics Load CSV data into dataframe, discretize all attributes, clean input data. Use a logistic regression classifier to predict the target attribute. Split the data in training/test sets using a 70/30 split. Generate models for a range of α values and compare performance.
- 15. First Attempt:To FriskOr NotTo Frisk… Input attributes was this person frisked?
- 16. HowTo JudgeThe SuccessRate ofThe Algorithm Our algorithm should at least be as good as a random algorithm at picking people to frisk. It can „buy“ true positives by accepting false positives. The higher α is, the more profitable this trade becomes. Eventually we will have frisked all people, which is a solution to the problem (but not a good one…)
- 17. Example: Predicting OnlyWithNoise (No Information) We give no useful information to the algorithm at all. It will therefore pick the action (frisk / not frisk) that will globally maximize the score when chosen for all people.
- 18. Predicting „frisk“ withall available input attributes Now we give it all the input attributes that we got. It will make a prediction that is much better than randomly choosing a person to frisk.
- 19. What does itmean for individual groups? There is strong mistreatment of individual groups. The algorithm learned to be just as biased as the training data.
- 20. Where does thebias come from? Let‘s see! Predict „black“ from available attributes. The algorithm can easily differentiate between „white“ and „black“
- 21. EliminatingThe Bias: DifferentApproaches Remove /Modify Data Points … 1 2 3 Constrain the Algorithm forbidden solutions allowed solutions …or change the target attribute! … 1 2 3 Remove Attributes
- 22. Trying DifferentAttribute SetsToPredict „Black“ only behavior-based attributes only circumstance-based attributes Almost no prediction possible! Prediction still possible (probably due to „pct“ attribute)
- 23. Let‘sTry It Reducingthe Features: Use Only Behavior previous model with all features
- 24. DisparateTreatment Is Reduced(But So Is Usefulness) previous model with all features
- 25. Let'sTry Using ADifferentTargetAttribute Input attributes was this person arrested / summoned?
- 26. Training with ADifferentTarget: Arrests + Summons (only using circumstance-based attributes) There should be less bias in the arrests, as it is harder (but still possible) to arrest someone who is innocent.
- 27. As Expected, BiasIs Reduced No „preferential“ treatment evident for white people in the data (on the contrary) Much better!
- 28. Better (But StillImperfect)Treatment ByThe Algorithm
- 29. Take-Aways • Most trainingdata that we use contains biases • Some of these biases are implicit and not easy to recognize (if we don‘t look) • To protect people from discrimination, we need to record and analyze their sensitive data (in a secure way) • Machine learning and data analysis can uncover hidden biases in processes (if we're transparent about the methods) • Algorithmic systems can improve the fairness of manual processes by ensuring no biases are present
- 30. Outlook:What Future MLSystems Could Look Like ML Algorithm Non-protected Input Data Sanitized Input Data Protected Input Data Explainer explanations results Auditor fairness metrics
- 31. Thanks! Slides, code, literatureand data will be made available here: https://github.com/adewes/fatml-pydata Contact me: ich@andreas-dewes.de (@japh44) Image Credits: https://gist.github.com/dannguyen/67ece10c6132282b1da2 https://commons.wikimedia.org/wiki/File:Deadpool_and_Predator_at_Big_Apple_Con_2009.jpg