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Fairness-aware Machine Learning:
Practical Challenges and Lessons Learned
WSDM 2019 Tutorial
February 2019
Paul Bennett (M...
The Coded Gaze [Joy Buolamwini 2016]
• Face detection
software:
Fails for some
darker faces
• Cf. "racist soap
dispensers"...
Gender Shades [Joy Buolamwini & Timnit Gebru, 2018]
• Facial analysis
software:
Higher accuracy for
light skinned men
• Er...
Algorithmic Bias
▪ Ethical challenges posed by AI
systems
▪ Inherent biases present in society
– Reflected in training dat...
Laws against Discrimination
Immigration Reform and Control Act
Citizenship
Rehabilitation Act of 1973;
Americans with Disa...
Fairness Privacy
Transparency Explainability
Fairness Privacy
Transparency Explainability
Fairness Privacy
Transparency Explainability
Fairness Privacy
Transparency Explainability
Fairness Privacy
Transparency Explainability
Related WSDM’19 sessions:
1.Tutorial: Privacy-Preserving Data Mining in Indus...
“Fairness by Design” for AI products
Outline / Learning Outcomes
• Algorithmic Bias / Discrimination
• Industry Best Practices
• Sources of Biases in ML Lifecy...
Algorithmic Bias / Discrimination
and broader / related issues
Other Great Tutorials
Fairness in Machine Learning
Solon Barocas and Moritz Hardt, NeurIPS 2017
Challenges of incorporatin...
[Barocas & Hardt 2017]
"[H]iring could become faster and less expensive, and […] lead
recruiters to more highly skilled people who are better mat...
[Barocas & Hardt 2017]
"But software is not free of human influence. Algorithms are written
and maintained by people, and machine learning algori...
Do Better
Avoid Harm
[Cramer et al 2019]
More positive outcomes & avoiding harmful outcomes
of algorithms for groups of people
[Cramer et al 2019]
[Cramer et al 2019]
More positive outcomes & avoiding harmful outcomes
of automated systems for groups of people
Legally Recognized Protected Classes
Race (Civil Rights Act of 1964); Color (Civil Rights Act of
1964); Sex (Equal Pay Act...
Other Categories
Societal Categories
i.e., political ideology, language, income, location, topical interests,
(sub)culture...
Types of Harm
Harms of allocation
withhold opportunity or resources
Harms of representation
reinforce subordination along ...
Bias, Discrimination & Machine Learning
Isn’t bias a technical concept?
Selection, sampling, reporting bias, Bias of an es...
Discrimination is not a general concept
It is domain specific
Concerned with important opportunities that affect people’s ...
Regulated Domains
Credit (Equal Credit Opportunity Act)
Education (Civil Rights Act of 1964; Education Amendments of 1972)...
Discrimination Law and Legal Terms
Treatment
Disparate Treatment, Equality of Opportunity, Procedural Fairness
Outcome
Dis...
[https://www.reddit.com/r/GCdebatesQT/comments/7qpbpp/food_for_thought_equality_vs_equity_vs_justice/]
Fairness is Political
Equal Treatment vs Equal Outcome
Jobs Product
Women and Men get equally good job recommendations
Both click on recommendations equally
Women and Men apply ...
Fairness is Political
Someone must decide
Decisions will depend on the
product, company, laws, country, etc.
Why do this?
Better product and Serving Broader Population
Responsibility and Social Impact
Legal and Policy
Competitive A...
Industry Best Practices
for Product Conception, Design, Implementation, and Evolution
Is this simple?
Process Best Practices
Identify product goals
Get the right people in the room
Identify stakeholders
Select a fairness app...
Repeat for every new feature,
product change, etc.
Identify product goals
Be specific
What are you trying to achieve?
i.e., remove all violent content
For what population of...
Jobs: Identify product goals
What are you trying to achieve?
Match people with job opportunities
For what population of pe...
Get the right people in the room
Different domains require different expertise and decision makers to
be involved
Internal...
Jobs: Get the right people in the room
Internal People
Product leaders, legal, policy, social scientists
External People
A...
Identify stakeholders
Who has a stake in this product?
i.e., content producers, content consumers
Who might be harmed?
i.e...
Jobs: Identify stakeholders
Who has a stake in this product?
business trying to hire, people seeking jobs
groups of people...
Select a fairness approach
What type of fairness?
Group vs individual
At what point?
Equal outcome vs equal treatment
What...
Jobs: Select a fairness approach
What type of fairness?
Group: women and men
At what point?
Equal treatment
What distribut...
Analyze and evaluate your system
Consider the complete system end-to-end including people, technology
and processes
Break ...
Engineering for equity during all phases of ML design
Problem
Formation
Dataset
Construction
Algorithm
Selection
Training
...
Jobs V1: Analyze and evaluate your system
Analyzed recommendations:
Women apply to many more jobs than men
Women click on ...
Jobs V2: Analyze and evaluate your system
Analyzed recommendations:
Women apply to many more jobs than men
Women click on ...
Mitigate issues
Decide if you need to change your design, data, or metrics
Consider all types of interventions
Add balanci...
Jobs: Mitigate issues
V1
Make men more represented in the data set
Track proportion of men and women in the system and in ...
Monitor Continuously and Escalation Plans
Build in monitoring and testing for all the metrics you are tracking
Things drif...
Jobs: Escalation Plans
The night before our first major launch we discover the model doesn’t
perform well for men because ...
Auditing and Transparency
Important to also consider who else needs visibility into your process
and your system
Do you ne...
Process Best Practices
Identify product goals
Get the right people in the room
Identify stakeholders
Select a fairness app...
Sources of Biases in ML Lifecycle
Collaborators
Much of this section is based on survey paper and
tutorial series written by Alexandra Olteanu,
Carlos Casti...
Design Data Model Application
Design Data Model Application
Design Data Model Application
Data bias: a systematic
distortion in data that
compromises its use
for a task.
Note: Bias must be considered relative to task
62
Gender discrimination is
illegal
Gender-specific medical
diagnosis is de...
What does data bias look like?
Measure systematic distortions along 5 data properties
1. Population Biases
2. Behavioral B...
What does data bias look like?
Measure distortions along 5 data properties
1. Population Biases
Differences in demographic...
Example:
Different user
demographics on
different social
platforms
See [Hargittai’07] for statistics about social media us...
Systematic distortions must be evaluated in a
task dependent way
Gender Shades
E.g., for many tasks, populations
should ma...
What does data bias look like?
Measure distortions along 5 data properties
1. Population Biases
2. Behavioral Biases
Diffe...
Behavioral Biases from Functional Issues
Platform functionality and algorithms influence human behaviors
and our observati...
Cultural elements and social contexts are
reflected in social datasets Figure from
[Hannak et al. CSCW 2017]
69
Societal biases embedded in behavior can be
amplified by algorithms
Users pick
biased options
Biased
actions are
used as
f...
What does data bias look like?
Measure distortions along 5 data properties
1. Population Biases
2. Behavioral Biases
3. Co...
Behavioral Biases from Normative Issues
Community norms and societal biases influence observed behavior
and vary across on...
Privacy concerns affect what content users share, and,
thus, the type of patterns we observe.
Foursquare/Image from [Lindq...
As other media, social
media contains
misinformation and
disinformation
Misinformation is false information,
unintentional...
What does data bias look like?
Measure distortions along 5 data properties
1. Population Biases
2. Behavioral Biases
3. Co...
Behavior-based and connection-based social links are
different
76
Figure from [Wilson et al. EuroSys’09]
What does data bias look like?
Measure distortions along 5 data properties
1. Population Biases
2. Behavioral Biases
3. Co...
Different demographics can
exhibit different growth rates
across and within social
platforms
TaskRabbit and Fiverr are onl...
E.g., Change in Features over Time
Introducing a new feature or
changing an existing feature
impacts usage patterns on the...
Biases can creep in at data collection as well
• Common data collection issues:
• Acquisition – Platform restrictions on d...
Design Data Model Application
Best Practices for Bias Avoidance/Mitigation
Design Data Model Application
Best Practices for Bias Avoidance/Mitigation
Consider
team composition
for diversity of thou...
Design Data Model Application
Best Practices for Bias Avoidance/Mitigation
Understand the task,
stakeholders, and
potentia...
Design Data Model Application
Best Practices for Bias Avoidance/Mitigation
Check data sets
Consider data provenance
What i...
Design Data Model Application
Best Practices for Bias Avoidance/Mitigation
Check models and validate results
Why is the mo...
Design Data Model Application
Best Practices for Bias Avoidance/Mitigation
Post-Deployment
Ensure optimization and guardra...
Techniques for Fairness in ML
Google's Responsible Fairness Practices
https://ai.google/education/responsible-ai-practices?category=fairness
Summary:
• ...
Techniques for Fairness in ML
1. Product Introspection
2. Practical Testing ← Main focus today
3. Training Data
4. Modelin...
Product Introspection (1):
Make Your Key Choices Explicit [Mitchell et al., 2018]
Goals Decision Prediction
Profit from lo...
Group Exercise!?
1. Form small groups (2-4)
for group exercise
2. Introduce yourselves
Shad(e)vice™ LyricGram™
You have a music website for online
discussion of song lyrics. In response to
seeing increasing le...
Shad(e)vice™ LyricGram™
You have a music website for online
discussion of song lyrics. In response to
seeing increasing le...
Shad(e)vice™ LyricGram™
You have a music website for online
discussion of song lyrics. In response to
seeing increasing le...
Shad(e)vice™ LyricGram™
You have a music website for online
discussion of song lyrics. In response to
seeing increasing le...
Shad(e)vice™ LyricGram™
Immediate goal: prevent distress
Long term goal: increase engagement
Decision: block comment?
Inpu...
Shad(e)vice™ LyricGram™
Immediate goal: prevent distress
Long term goal: increase engagement
Decision: block comment?
Inpu...
Shad(e)vice™ LyricGram™
Immediate goal: prevent distress
Long term goal: increase engagement
Decision: block comment?
Inpu...
Product Introspection (2):
Identify Potential Harms
• What are the potential harms?
• Applicants who would have repaid are...
Product Introspection (2):
Identify Potential Harms
Types of Harms
• Representational
• Allocative
• Exclusion
e.g., image...
Seek out Diverse Perspectives
• Fairness Experts
• User Researchers
• Privacy Experts
• Legal
• Social Science Backgrounds...
Strengthen your Ethical Thinking Skills
https://aiethics.princeton.edu/case-studies/case-study-pdfs/
https://www.scu.edu/e...
Shad(e)vice™ LyricGram™
Predictions:
- Language(text)
- Abusive(text, language)
User Harms: ???
Most impacted users: ???
P...
Shad(e)vice™ LyricGram™
Predictions:
- Language(text)
- Abusive(text, language)
User Harms: ???
Most impacted users: ???
P...
Shad(e)vice™ LyricGram™
Predictions:
- Language(text)
- Abusive(text, language)
User Harms:
- Failure to protect from abus...
Launch with Confidence: Testing for Bias
• How will you know if users are being
harmed?
• How will you know if harms are u...
Model Predictions
Evaluate for Inclusion - Confusion Matrix
Model Predictions
Positive Negative
Evaluate for Inclusion - Confusion Matrix
Model Predictions
Positive Negative
● Exists
● Predicted
True Positives
● Doesn’t exist
● Not predicted
True Negatives
Eva...
Model Predictions
Positive Negative
● Exists
● Predicted
True Positives
● Exists
● Not predicted
False Negatives
● Doesn’t...
Efficient Testing for Bias
• Development teams are under multiple
constraints
• Time
• Money
• Human resources
• Access to...
Choose your evaluation metrics in light
of acceptable tradeoffs between
False Positives and False Negatives
Privacy in Images
False Positive: Something that doesn’t
need to be blurred gets blurred.
Can be a bummer.
False Negative:...
Spam Filtering
False Negative: Email that is SPAM is
not caught, so you see it in your inbox.
Usually just a bit annoying....
Types of Practical
Fairness Testing
1. Targeted Tests
2. Quick Tests
3. Comprehensive Tests
4. Ecologically Valid Tests
5....
1. Targeted Tests
Based on prior experience/knowledge
• Computer Vision
⇒ Test for dark skin
• Natural Language Processing...
Targeted Testing of a Gender Classifier
[Joy Buolamwini & Timnit Gebru, 2018]
• Facial recognition
software:
Higher accura...
2. Quick
Tests
• "Cheap"
• Useful throughout product cycle
• Spot check extreme cases
• Low coverage but high informativit...
Quick Tests
for Gender in
Translate
Quick Counterfactual Testing: What If Tool
3. Comprehensive Tests
Include sufficient data for each subgroup
• May include relevant combinations of attributes
• Somet...
Comprehensive Testing of a Toxic Language Detector
[Dixon et al., 2018]
AUC Metrics for
Comprehensive Testing
• Subgroup AUC:
• Subgroup Positives vs
Subgroup Negatives
• "BPSN" AUC:
• Backgroun...
Comprehensive Testing of a Toxicity Detector
https://github.com/conversationai/perspectiveapi/blob/master/model_cards/Engl...
Inclusive Images Competition
4. Ecologically Valid
Testing
Data is drawn from a distribution representative of the
deployment distribution
• Goal is NO...
Ecologically Valid Testing:
Distributions Matter
What is being compared?
Over what data?
Challenges with Ecologically Valid Testing
• Post-deployment distributions may not be known
• Product may not be launched ...
5. Adversarial
Tests
Search for rare but extreme harms
• “Poison needle in haystack”
• Requires knowledge of society
Typic...
Hypothetical Example of Adversarial Testing
• Emoji autosuggest: are happy emoji suggested for sad sentences?
My dog has g...
Summary of Practical
Fairness Testing
1. Targeted Tests: domain specific (image, language, etc)
2. Quick Tests: cheap test...
Fairness Testing Practices
are Good ML Practices
• Confidence in your product's fairness
requires fairness testing
• Fairn...
Shad(e)vice™ LyricGram™
Predictions:
- Language(text)
- Abusive(text, language)
User Harms: ???
- Failure to detect from a...
Shad(e)vice™
Predictions:
- CropFace(image)
- Purchase(face, sunglasses)
- FuturePurchases(face, sunglasses)
User Harms:
-...
LyricGram™
Predictions:
- Language(text)
- Abusive(text, language)
User Harms: ???
- Failure to detect from abuse
- Censor...
Fairness-aware Data Collection
[Holstein et al., 2019]
• ML literature generally assumes data is fixed
• Often the solutio...
Get to Know Your Training Data: Facets Dive
Datasheets for Datasets [Gebru et al., 2018]
Datasheets for Datasets [Gebru et al., 2018]
Fairness-Aware Data Collection Techniques
1. Address population biases
• Target under-represented (with respect to the use...
Shad(e)vice™ LyricGram™
Predictions:
- Language(text)
- Abusive(text, language)
User Harms:
- Failure to protectfrom abuse...
Shad(e)vice™
Predictions:
- CropFace(image)
- Purchase(face, sunglasses)
- FuturePurchases(face, sunglasses)
User Harms:
-...
LyricGram™
Predictions:
- Language(text)
- Abusive(text, language)
User Harms:
- Failure to detect from abuse
- Censoring ...
Sometimes data biases are
unavoidable
Solution: ML Techniques
Practical Concerns with Fair Machine Learning
•Is the training process stable?
•Can we guarantee that fairness policies
wi...
Machine Learning Techniques: Adversarial Training?
P(Label=1) P(Group)
Negative
Gradient
Fairly well-studied with some
nic...
Machine Learning: Correlation Loss
[Beutel et al., 2018]
Motivation: Overcome training instability with adversarial traini...
Predicted P(Target) distribution
for “Blue” and “Red” examples
(Illustrative Example)
min Loss(Label, Pred)
Pred = P(Label...
min Loss(Label, Pred)
+ Abs(Corr(Pred, Group))|Label=0
Pred = P(Label=1)
Predicted P(Target) distribution
for “Blue” and “...
● Computed per batch
● Easy to use
● More stable than
adversarial training.
min Loss(Label, Pred)
+ Abs(Corr(Pred, Group))...
Machine Learning: Constrained Optimization
[Cotter et al., 2018]
Motivation: Can we ensure that fairness policies are sati...
Model Cards for Model Reporting[Mitchell et al., 2018]
Further Machine Learning Techniques
Many more approaches are linked to from the tutorial website.
https://sites.google.com...
Fairness in UI/Product Design
1. Robust UIs handle ML failures gracefully
2. UIs should empower users
Shad(e)vice™ LyricGram™
Predictions:
- Language(text)
- Abusive(text, language)
User Harms: ???
- Failure to detect from a...
Shad(e)vice™
Predictions:
- CropFace(image)
- Purchase(face, sunglasses)
- FuturePurchases(face, sunglasses)
User Harms:
-...
LyricGram™
Predictions:
- Language(text)
- Abusive(text, language)
User Harms:
- Failure to detect from abuse
- Censoring ...
Thanks to
Alex Beutel (Research Scientist, fairness in ML),
Allison Woodruff (UX Research, privacy, fairness and ethics),
...
Fairness Methods in Practice
(Case Studies)
Google Assistant
[Play Google Assistant video from desktop]
Key points:
• Think about user harms
• How does your product make people feel?
...
Computer Vision
Google Camera
Key points:
• Check for unconscious bias
• Comprehensive testing:
"make sure this works
for everybody"
Night Sight
This is a “Shirley Card”
Named after a Kodak studio model
named Shirley Page, they were the
primary method for calibrating...
Until about 1990, virtually all
Shirley Cards featured Caucasian
women.
SKIN TONE IN PHOTOGRAPHY
SOURCES
Color film was bu...
As a result, photos featuring
people with light skin looked fairly
accurate.
SKIN TONE IN PHOTOGRAPHY
SOURCES
Color film w...
Photos featuring people with
darker skin, not so much...
SKIN TONE IN PHOTOGRAPHY
SOURCES
Color film was built for white p...
Google Clips
Google Clips
"We created controlled datasets by
sampling subjects from different genders
and skin tones in a balanced mann...
Geena Davis Inclusion Quotient
[with Geena Davis Institute on Gender in Media]
Machine Translation
(Historical)
Gender
Pronouns in
Translate
Three Step Approach
1. Detect Gender-Neutral Queries
Train a text classifier to detect when a Turkish query is gender-neutral.
• trained on th...
2. Generate Gender-Specific Translations
• Training: Modify training data to add an additional input token specifying the
...
3. Check for Accuracy
Verify:
1. If the requested feminine translation is feminine.
2. If the requested masculine translat...
Result: Reduced Gender Bias in Translate
Smart Compose
Adversarial Testing
for Smart Compose
in Gmail
Conversational Agents
Responsible AI
and Conversational
Agents
Background: Conversational Agents
• Social bots
• Informational bots
• Task-oriented bots
Eliza, Joseph Weizenbaum (MIT) 1...
Design Data Model Application
Design Data Model Application
Design Data Model Application
Questions to ask during
AI Design
Who is affected by AI?
How might AI cause harms?
Stakeholders: Who might be affected?
1. Humans speaking with the agent
• Emotional harms, misinformation, threaten task co...
How might AI cause harms?
Functional Harms
• Misrepresentation of capabilities
• Misinforming user about task status
• Mis...
How might AI cause harms? Functional Harms
Functional Harms
• Misrepresentation of capabilities
• Misinforming user about ...
How might AI cause harms? Functional Harms
Functional Harms
• Misrepresentation of capabilities
• Misinforming user about ...
How might AI cause harms? Functional Harms
Functional Harms
• Misrepresentation of capabilities
• Misinforming user about ...
How might AI cause harms?
Functional Harms
• Misrepresentation of capabilities
• Misinforming user about task status
• Mis...
How might AI cause harms?
Social Harms: Harms to Individuals
• Inciting/encouraging harmful behavior
• Self/harm, suicide
...
How might AI cause harms?
Social Harms: Harms to Communities
• Promoting violence, war, ethnic cleansing, …
• Including pr...
Why is this hard?
Language is ambiguous, complex,
with social context
Examples of complex failures:
• Failure to deflect/t...
Why is this hard?
Language is ambiguous, complex,
with social context
Examples of complex failures:
• Failure to deflect/t...
Why is this hard?
Language is ambiguous, complex,
with social context
Examples of complex failures:
• Failure to deflect/t...
Design Data Model Application
Implications for data collection
Common data sources
• Hand-written rules
• Existing conversational data (e.g., social med...
Design Data Model Application
Design Data Model Application
Responsible bots: 10 guidelines for
developers of conversational AI
1. Articulate the purpos...
Design Data Model Application
Responsible bots: 10 guidelines for
developers of conversational AI
1. Articulate the purpos...
Design Data Model Application
Responsible bots: 10 guidelines for
developers of conversational AI
1. Articulate the purpos...
Key take-away points
• Many stakeholders affected by conversational agent AIs
• Not only people directly interacting with ...
Acknowledgments
• Chris Brockett, Bill Dolan, Michel Galley, Ece Kamar
Deep Dive: Talent Search
Fairness in AI @ LinkedIn
Create economic opportunity for every
member of the global workforce
LinkedIn’s Vision
Connect the world's professionals t...
610M
Members
30M
Companies
20M
Jobs
50K
Skills
90K
Schools
100B+
Updates viewed
LinkedIn Economic Graph
AI @LinkedIn
25 B
ML A/B experiments
per week
data processed
offline per day
2002.15 PB
data processed
nearline per day
2 ...
Guiding Principle:
“Diversity by Design”
“Diversity by Design” in LinkedIn’s Talent Solutions
Insights to
Identify Diverse
Talent Pools
Representative
Talent Searc...
Plan for Diversity
Plan for Diversity
Identify Diverse Talent Pools
Inclusive Job Descriptions / Recruiter Outreach
Representative Ranking for Talent Search
S. C. Geyik,
K. Kenthapadi,
Building
Representative Talent
Search at LinkedIn,
Li...
Intuition for Measuring Representativeness
• Ideal: same distribution on gender/age/… for
• Top ranked results and
• Quali...
Measuring (Lack of) Representativeness
• Skew@k
• (Logarithmic) ratio of the proportion of candidates having a given attri...
Reranking Algorithm for Representativeness
• Determine the target proportions within the attribute of interest,
correspond...
Target Proportions within the Attribute of Interest
• Compute the proportions of the values of the attribute (e.g., gender...
Fairness-aware Reranking Algorithm
• Partition the set of potential candidates into different buckets for
each attribute v...
Architecture
Validating Our Approach
• Gender Representativeness
• Over 95% of all searches are representative compared to the qualifie...
Key takeaway points
• Post-processing approach desirable
• Agnostic to the specifics of each model
• Scalable across diffe...
Acknowledgements
•Team:
• AI/ML: Sahin Cem Geyik, Stuart Ambler, Krishnaram Kenthapadi
• Application Engineering: Gurwinde...
Reflections
• Lessons from fairness challenges  Need
“Fairness by Design” approach when building AI
products
• Case studi...
Key Takeaways
Good ML
Practices Go a
Long Way
• Lots of low hanging fruit in terms of improving
fairness simply by using machine learnin...
Breadth and
Depth
Required
• Looking End-to-End is critical
• Need to be aware of bias and potential
problems at every sta...
The Real
World is What
Matters
• Decisions should be made considering the real
world goals and outcomes
• You must have pe...
Key Open Problems in
Applied Fairness
Key Open
Problems in
Applied
Fairness
• What if you don’t have the sensitive attributes?
• When should you use what approa...
Related Tutorials / Resources
• Sara Hajian, Francesco Bonchi, and Carlos Castillo, Algorithmic bias: From discrimination
...
Fairness Privacy
Transparency Explainability
Related WSDM’19 sessions:
1.Tutorial: Privacy-Preserving Data Mining in Indus...
Thanks! Questions?
•Tutorial website:
https://sites.google.com/view/wsdm19-fairness-
tutorial
•Feedback most welcome 
• s...
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Fairness-aware Machine Learning: Practical Challenges and Lessons Learned (WSDM 2019 Tutorial)

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Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learned models and data-driven systems, and the potential for such systems to discriminate against certain population groups, due to biases in algorithmic decision-making systems. This tutorial presents an overview of algorithmic bias / discrimination issues observed over the last few years and the lessons learned, key regulations and laws, and evolution of techniques for achieving fairness in machine learning systems. We will motivate the need for adopting a "fairness by design" approach (as opposed to viewing algorithmic bias / fairness considerations as an afterthought), when developing machine learning based models and systems for different consumer and enterprise applications. Then, we will focus on the application of fairness-aware machine learning techniques in practice by presenting non-proprietary case studies from different technology companies. Finally, based on our experiences working on fairness in machine learning at companies such as Facebook, Google, LinkedIn, and Microsoft, we will present open problems and research directions for the data mining / machine learning community.

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Fairness-aware Machine Learning: Practical Challenges and Lessons Learned (WSDM 2019 Tutorial)

  1. 1. Fairness-aware Machine Learning: Practical Challenges and Lessons Learned WSDM 2019 Tutorial February 2019 Paul Bennett (Microsoft) Sarah Bird (Facebook/Microsoft) Ben Hutchinson (Google) Krishnaram Kenthapadi (LinkedIn) Emre Kıcıman (Microsoft) Margaret Mitchell (Google) https://sites.google.com/view/wsdm19-fairness-tutorial
  2. 2. The Coded Gaze [Joy Buolamwini 2016] • Face detection software: Fails for some darker faces • Cf. "racist soap dispensers" https://www.youtube.com/watch?v=KB9sI9rY3cA
  3. 3. Gender Shades [Joy Buolamwini & Timnit Gebru, 2018] • Facial analysis software: Higher accuracy for light skinned men • Error rates for dark skinned women: 20% - 34%
  4. 4. Algorithmic Bias ▪ Ethical challenges posed by AI systems ▪ Inherent biases present in society – Reflected in training data – AI/ML models prone to amplifying such biases ▪ ACM FAT* conference / KDD’16 & NeurIPS’17 Tutorials
  5. 5. Laws against Discrimination Immigration Reform and Control Act Citizenship Rehabilitation Act of 1973; Americans with Disabilities Act of 1990 Disability status Civil Rights Act of 1964 Race Age Discrimination in Employment Act of 1967 Age Equal Pay Act of 1963; Civil Rights Act of 1964 Sex And more...
  6. 6. Fairness Privacy Transparency Explainability
  7. 7. Fairness Privacy Transparency Explainability
  8. 8. Fairness Privacy Transparency Explainability
  9. 9. Fairness Privacy Transparency Explainability
  10. 10. Fairness Privacy Transparency Explainability Related WSDM’19 sessions: 1.Tutorial: Privacy-Preserving Data Mining in Industry (Monday, 9:00 - 12:30) 2.H.V. Jagadish's invited talk: Responsible Data Science (Tuesday, 14:45 - 15:30) 3.Session 4: FATE & Privacy (Tuesday, 16:15 - 17:30) 4.Aleksandra Korolova’s invited talk: Privacy-Preserving WSDM (Wednesday, 14:45 - 15:30)
  11. 11. “Fairness by Design” for AI products
  12. 12. Outline / Learning Outcomes • Algorithmic Bias / Discrimination • Industry Best Practices • Sources of Biases in ML Lifecycle • Techniques for Fairness in ML • Fairness Methods in Practice: Case Studies • Key Takeaways • Key Open Problems in Applied Fairness
  13. 13. Algorithmic Bias / Discrimination and broader / related issues
  14. 14. Other Great Tutorials Fairness in Machine Learning Solon Barocas and Moritz Hardt, NeurIPS 2017 Challenges of incorporating algorithmic fairness into practice Henriette Cramer, Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miroslav Dudík, Hanna Wallach, Sravana Reddy, Jean Garcia-Gathright, FAT* 2019 Defining and Designing Fair Algorithms Sam Corbett-Davies, Sharad Goel, ICML 2018 The Trouble with Bias Kate Crawford, NeurIPS 2017 Keynote
  15. 15. [Barocas & Hardt 2017]
  16. 16. "[H]iring could become faster and less expensive, and […] lead recruiters to more highly skilled people who are better matches for their companies. Another potential result: a more diverse workplace. The software relies on data to surface candidates from a wide variety of places and match their skills to the job requirements, free of human biases." Miller (2015) [Barocas & Hardt 2017]
  17. 17. [Barocas & Hardt 2017]
  18. 18. "But software is not free of human influence. Algorithms are written and maintained by people, and machine learning algorithms adjust what they do based on people’s behavior. As a result […] algorithms can reinforce human prejudices." Miller (2015) [Barocas & Hardt 2017]
  19. 19. Do Better Avoid Harm [Cramer et al 2019]
  20. 20. More positive outcomes & avoiding harmful outcomes of algorithms for groups of people [Cramer et al 2019]
  21. 21. [Cramer et al 2019] More positive outcomes & avoiding harmful outcomes of automated systems for groups of people
  22. 22. Legally Recognized Protected Classes Race (Civil Rights Act of 1964); Color (Civil Rights Act of 1964); Sex (Equal Pay Act of 1963; Civil Rights Act of 1964); Religion (Civil Rights Act of 1964);National origin (Civil Rights Act of 1964); Citizenship (Immigration Reform and Control Act); Age (Age Discrimination in Employment Act of 1967);Pregnancy (Pregnancy Discrimination Act); Familial status (Civil Rights Act of 1968); Disability status (Rehabilitation Act of 1973; Americans with Disabilities Act of 1990); Veteran status (Vietnam Era Veterans' Readjustment Assistance Act of 1974; Uniformed Services Employment and Reemployment Rights Act); Genetic information (Genetic Information Nondiscrimination Act) [Boracas & Hardt 2017]
  23. 23. Other Categories Societal Categories i.e., political ideology, language, income, location, topical interests, (sub)culture, physical traits, etc. Intersectional Subpopulations i.e., women from tech Application-specific subpopulations i.e., device type
  24. 24. Types of Harm Harms of allocation withhold opportunity or resources Harms of representation reinforce subordination along the lines of identity, stereotypes [Cramer et al 2019, Shapiro et al., 2017, Kate Crawford, “The Trouble With Bias” keynote N(eur)IPS’17]
  25. 25. Bias, Discrimination & Machine Learning Isn’t bias a technical concept? Selection, sampling, reporting bias, Bias of an estimator, Inductive bias Isn’t discrimination the very point of machine learning? Unjustified basis for differentiation [Barocas & Hardt 2017]
  26. 26. Discrimination is not a general concept It is domain specific Concerned with important opportunities that affect people’s life chances It is feature specific Concerned with socially salient qualities that have served as the basis for unjustified and systematically adverse treatment in the past [Barocas & Hardt 2017]
  27. 27. Regulated Domains Credit (Equal Credit Opportunity Act) Education (Civil Rights Act of 1964; Education Amendments of 1972) Employment (Civil Rights Act of 1964) Housing (Fair Housing Act) ‘Public Accommodation’ (Civil Rights Act of 1964) Extends to marketing and advertising; not limited to final decision [Barocas & Hardt 2017]
  28. 28. Discrimination Law and Legal Terms Treatment Disparate Treatment, Equality of Opportunity, Procedural Fairness Outcome Disparate Impact, Distributive justice, Minimized inequality of outcome
  29. 29. [https://www.reddit.com/r/GCdebatesQT/comments/7qpbpp/food_for_thought_equality_vs_equity_vs_justice/]
  30. 30. Fairness is Political Equal Treatment vs Equal Outcome
  31. 31. Jobs Product Women and Men get equally good job recommendations Both click on recommendations equally Women and Men apply to jobs equally Both apply to the jobs at the same rate Both apply to the same total number of jobs Both apply according to their proportion in the population (in the product? In the US? In the world?) Women and Men are hired to jobs equally Full time vs part time? Temp vs permanent? Women and Men are hired equally to equally good jobs What is a good job? One that they value? Best hours? Best income?
  32. 32. Fairness is Political Someone must decide Decisions will depend on the product, company, laws, country, etc.
  33. 33. Why do this? Better product and Serving Broader Population Responsibility and Social Impact Legal and Policy Competitive Advantage and Brand [Boracas & Hardt 2017]
  34. 34. Industry Best Practices for Product Conception, Design, Implementation, and Evolution
  35. 35. Is this simple?
  36. 36. Process Best Practices Identify product goals Get the right people in the room Identify stakeholders Select a fairness approach Analyze and evaluate your system Mitigate issues Monitor Continuously and Escalation Plans Auditing and Transparency
  37. 37. Repeat for every new feature, product change, etc.
  38. 38. Identify product goals Be specific What are you trying to achieve? i.e., remove all violent content For what population of people? i.e., for all users, for younger users What metrics are you tracking? i.e., percentage of violent content removed
  39. 39. Jobs: Identify product goals What are you trying to achieve? Match people with job opportunities For what population of people? US users What jobs? Local entry-level jobs What metrics are you tracking? Job applications per job and per user
  40. 40. Get the right people in the room Different domains require different expertise and decision makers to be involved Internal People Product leaders, legal, policy, user research, design, social scientists, domain experts, machine learning experts External People Academics, Consultants, Advocacy groups, Government agencies
  41. 41. Jobs: Get the right people in the room Internal People Product leaders, legal, policy, social scientists External People Academics, advocacy groups, government agencies
  42. 42. Identify stakeholders Who has a stake in this product? i.e., content producers, content consumers Who might be harmed? i.e., small local content producers How? i.e., underrepresentation
  43. 43. Jobs: Identify stakeholders Who has a stake in this product? business trying to hire, people seeking jobs groups of people, society as whole? Who might be harmed? business trying to hire, people seeking jobs, society as whole How? Allocation
  44. 44. Select a fairness approach What type of fairness? Group vs individual At what point? Equal outcome vs equal treatment What distributions? US population, users of the product
  45. 45. Jobs: Select a fairness approach What type of fairness? Group: women and men At what point? Equal treatment What distributions? US population
  46. 46. Analyze and evaluate your system Consider the complete system end-to-end including people, technology and processes Break your system into components Analyze each component to understand the decisions made and their impact Determine how well it matches up to your selected fairness approach
  47. 47. Engineering for equity during all phases of ML design Problem Formation Dataset Construction Algorithm Selection Training Process Testing Process Deployment Feedback Does our data include enough minority samples? Is the data skewed? Can we collect more data or reweight? Are there missing/biased features? Was our historical data generated by a biased processed that we reify? Do our labels reinforce stereotypes? Do we need to apply debiasing algorithms to preprocess our data? Credit: K. Browne & J. Draper
  48. 48. Jobs V1: Analyze and evaluate your system Analyzed recommendations: Women apply to many more jobs than men Women click on many more of the recommendations than men Men and women are seeing similar job recommendations Women click on different types of jobs than men There are not many men in the training data Note: this gets worse with feedback
  49. 49. Jobs V2: Analyze and evaluate your system Analyzed recommendations: Women apply to many more jobs than men Women click on many more of the recommendations than men Men and women are seeing similar job recommendations Women click on different types of jobs than men Women and men are equally represented in the data set We have very few of the types of jobs men click on in the inventory
  50. 50. Mitigate issues Decide if you need to change your design, data, or metrics Consider all types of interventions Add balancing tracking metrics
  51. 51. Jobs: Mitigate issues V1 Make men more represented in the data set Track proportion of men and women in the system and in the data V2 Interview male users and potential users, find out what they are looking for Reach out to local business to improve job inventory Track different job categories and properties in inventory
  52. 52. Monitor Continuously and Escalation Plans Build in monitoring and testing for all the metrics you are tracking Things drift because the world changes, user behavior changes, etc. Every time you deploy a model (or before), as the system runs, etc. Develop response and escalation plans How are you going to respond when something happens? What blocks launch? Who decides?
  53. 53. Jobs: Escalation Plans The night before our first major launch we discover the model doesn’t perform well for men because of issues with the dataset We told investors that we would launch tomorrow and all of the PR and marketing is set to go live Do we launch? Who decides?
  54. 54. Auditing and Transparency Important to also consider who else needs visibility into your process and your system Do you need to prove that your system meets regulations? Do you want outside experts to certify your system? Do users need to understand fairness in the system?
  55. 55. Process Best Practices Identify product goals Get the right people in the room Identify stakeholders Select a fairness approach Analyze and evaluate your system Mitigate issues Monitor Continuously and Escalation Plans Auditing and Transparency
  56. 56. Sources of Biases in ML Lifecycle
  57. 57. Collaborators Much of this section is based on survey paper and tutorial series written by Alexandra Olteanu, Carlos Castillo, Fernando Diaz, Emre Kıcıman
  58. 58. Design Data Model Application
  59. 59. Design Data Model Application
  60. 60. Design Data Model Application
  61. 61. Data bias: a systematic distortion in data that compromises its use for a task.
  62. 62. Note: Bias must be considered relative to task 62 Gender discrimination is illegal Gender-specific medical diagnosis is desirable
  63. 63. What does data bias look like? Measure systematic distortions along 5 data properties 1. Population Biases 2. Behavioral Biases 3. Content Production Biases 4. Linking Biases 5. Temporal Biases
  64. 64. What does data bias look like? Measure distortions along 5 data properties 1. Population Biases Differences in demographics or other user characteristics between a user population represented in a dataset or platform and a target population 2. Behavioral Biases 3. Content Production Biases 4. Linking Biases 5. Temporal Biases
  65. 65. Example: Different user demographics on different social platforms See [Hargittai’07] for statistics about social media use among young adults according to gender, race and ethnicity, and parental educational background. 65 Figure from http://www.pewinternet.org/2016/11/11/social-media-update-2016/
  66. 66. Systematic distortions must be evaluated in a task dependent way Gender Shades E.g., for many tasks, populations should match target population, to improve external validity But for some other tasks, subpopulations require approximately equal representation to achieve task parity http://gendershades.org/
  67. 67. What does data bias look like? Measure distortions along 5 data properties 1. Population Biases 2. Behavioral Biases Differences in user behavior across platforms or contexts, or across users represented in different datasets 3. Content Production Biases 4. Linking Biases 5. Temporal Biases
  68. 68. Behavioral Biases from Functional Issues Platform functionality and algorithms influence human behaviors and our observations of human behaviors [Miller et al. ICWSM’16] Figure from: http://grouplens.org/blog/investigating-the-potential-for-miscommunication-using-emoji/
  69. 69. Cultural elements and social contexts are reflected in social datasets Figure from [Hannak et al. CSCW 2017] 69
  70. 70. Societal biases embedded in behavior can be amplified by algorithms Users pick biased options Biased actions are used as feedback System learns to mimic biased options System presents options, influencing user choice
  71. 71. What does data bias look like? Measure distortions along 5 data properties 1. Population Biases 2. Behavioral Biases 3. Content Production Biases Lexical, syntactic, semantic, and structural differences in the contents generated by users 4. Linking Biases 5. Temporal Biases
  72. 72. Behavioral Biases from Normative Issues Community norms and societal biases influence observed behavior and vary across online and offline communities and contexts What kind of pictures would you share on Facebook, but not on LinkedIn? Are individuals comfortable contradicting popular opinions? E.g., after singer Prince died, most SNs showed public mourning. But not anonymous site PostSecret The same mechanism can embed different meanings in different contexts [Tufekci ICWSM’14] [the meaning of retweets or likes] “could range from affirmation to denunciation to sarcasm to approval to disgust”
  73. 73. Privacy concerns affect what content users share, and, thus, the type of patterns we observe. Foursquare/Image from [Lindqvist et al. CHI’11] The awareness of being observed by other impacts user behavior: Privacy and safety concerns 73
  74. 74. As other media, social media contains misinformation and disinformation Misinformation is false information, unintentionally spread Disinformation is false information, deliberately spread Figures from [Kumar et al. 2016] Hoaxes on Wikipedia: (left) impact as number of views per day for hoaxes surviving at least 7 days, and (right) time until a hoax gets detected and flagged. 74
  75. 75. What does data bias look like? Measure distortions along 5 data properties 1. Population Biases 2. Behavioral Biases 3. Content Production Biases 4. Linking Biases Differences in the attributes of networks obtained from user connections, interactions, or activity 5. Temporal Biases
  76. 76. Behavior-based and connection-based social links are different 76 Figure from [Wilson et al. EuroSys’09]
  77. 77. What does data bias look like? Measure distortions along 5 data properties 1. Population Biases 2. Behavioral Biases 3. Content Production Biases 4. Linking Biases 5. Temporal Biases Differences in populations and behaviors over time
  78. 78. Different demographics can exhibit different growth rates across and within social platforms TaskRabbit and Fiverr are online freelance marketplaces. Figure from [Hannak et al. CSCW 2017] 78
  79. 79. E.g., Change in Features over Time Introducing a new feature or changing an existing feature impacts usage patterns on the platform.
  80. 80. Biases can creep in at data collection as well • Common data collection issues: • Acquisition – Platform restrictions on data gathering (sampling, APIs, …) • Querying – Bias from limited expressiveness, keywords, geo- and other • Filtering – Outliers, stop words
  81. 81. Design Data Model Application Best Practices for Bias Avoidance/Mitigation
  82. 82. Design Data Model Application Best Practices for Bias Avoidance/Mitigation Consider team composition for diversity of thought, background and experiences
  83. 83. Design Data Model Application Best Practices for Bias Avoidance/Mitigation Understand the task, stakeholders, and potential for errors and harm
  84. 84. Design Data Model Application Best Practices for Bias Avoidance/Mitigation Check data sets Consider data provenance What is the data intended to represent? Verify through qualitative, experimental, survey and other methods
  85. 85. Design Data Model Application Best Practices for Bias Avoidance/Mitigation Check models and validate results Why is the model making decision? What mechanisms would explain results? Is supporting evidence consistent? Twyman’s law: The more unusual the result, more likely it’s an error
  86. 86. Design Data Model Application Best Practices for Bias Avoidance/Mitigation Post-Deployment Ensure optimization and guardrail metrics consistent w/responsible practices and avoid harms Continual monitoring, including customer feedback Have a plan to identify and respond to failures and harms as they occur
  87. 87. Techniques for Fairness in ML
  88. 88. Google's Responsible Fairness Practices https://ai.google/education/responsible-ai-practices?category=fairness Summary: • Design your product using concrete goals for fairness and inclusion. • Engage with social scientists and other relevant experts. • Set fairness goals • Check system for unfair biases. • Include diverse testers and adversarial/stress testing. • Consider feedback loops • Analyze performance. • Evaluate user experience in real-world scenarios. • Use representative datasets to train and test your model.
  89. 89. Techniques for Fairness in ML 1. Product Introspection 2. Practical Testing ← Main focus today 3. Training Data 4. Modeling 5. UI/Product Design
  90. 90. Product Introspection (1): Make Your Key Choices Explicit [Mitchell et al., 2018] Goals Decision Prediction Profit from loans Whether to lend Loan will be repaid Justice, Public safety Whether to detain Crime committed if not detained • Goals are ideally measurable • What are your non-goals? • Which decisions are you not considering? • What is the relationship between Prediction and Decision?
  91. 91. Group Exercise!? 1. Form small groups (2-4) for group exercise 2. Introduce yourselves
  92. 92. Shad(e)vice™ LyricGram™ You have a music website for online discussion of song lyrics. In response to seeing increasing levels of online abuse, you design an automated comment moderation system. You run an online sunglasses shop. You are designing a new feature that lets users upload photos in order to get automated suggestions based on their facial features.
  93. 93. Shad(e)vice™ LyricGram™ You have a music website for online discussion of song lyrics. In response to seeing increasing levels of online abuse, you design an automated comment moderation system. You run an online sunglasses shop. You are designing a new feature that lets users upload photos in order to get automated suggestions based on their facial features. Immediate goal: sell sunglasses to users Long term goal: customer loyalty Decision: recommend sunglasses? Input: image
  94. 94. Shad(e)vice™ LyricGram™ You have a music website for online discussion of song lyrics. In response to seeing increasing levels of online abuse, you design an automated comment moderation system. Immediate goal: prevent distress Long term goal: increase engagement Decision: block comment? Input: text You run an online sunglasses shop. You are designing a new feature that lets users upload photos in order to get automated suggestions based on their facial features. Immediate goal: sell sunglasses to users Long term goal: customer loyalty Decision: recommend sunglasses? Input: image
  95. 95. Shad(e)vice™ LyricGram™ You have a music website for online discussion of song lyrics. In response to seeing increasing levels of online abuse, you design an automated comment moderation system. Immediate goal: prevent distress Long term goal: increase engagement Decision: block comment? Input: text You run an online sunglasses shop. You are designing a new feature that lets users upload photos in order to get automated suggestions based on their facial features. Immediate goal: sell sunglasses to users Long term goal: customer loyalty Decision: recommend sunglasses? Input: image Q1: What prediction(s) will your ML system(s) make? Q2: How will you use these predictions to make decisions?
  96. 96. Shad(e)vice™ LyricGram™ Immediate goal: prevent distress Long term goal: increase engagement Decision: block comment? Input: text Predictions??? Immediate goal: sell sunglasses to users Long term goal: customer loyalty Decision: recommend sunglasses? Input: image Predictions??? Q1: What prediction(s) will your ML system(s) make? Q2: How will you use these predictions to make decisions?
  97. 97. Shad(e)vice™ LyricGram™ Immediate goal: prevent distress Long term goal: increase engagement Decision: block comment? Input: text Predictions??? Immediate goal: sell sunglasses to users Long term goal: customer loyalty Decision: recommend sunglasses? Input: image Predictions: - CropFace(image) - Purchase(face, sunglasses) - FuturePurchases(face, sunglasses) Q1: What prediction(s) will your ML system(s) make? Q2: How will you use these predictions to make decisions?
  98. 98. Shad(e)vice™ LyricGram™ Immediate goal: prevent distress Long term goal: increase engagement Decision: block comment? Input: text Predictions: - Language(text) - IsAbusive(text, language) Immediate goal: sell sunglasses to users Long term goal: customer loyalty Decision: recommend sunglasses? Input: image Predictions: - CropFace(image) - Purchase(face, sunglasses) - FuturePurchases(face, sunglasses) Q1: What prediction(s) will your ML system(s) make? Q2: How will you use these predictions to make decisions?
  99. 99. Product Introspection (2): Identify Potential Harms • What are the potential harms? • Applicants who would have repaid are not given loans • Convicts who would not commit a crime are locked up. • Are there also longer term harms? • Applicants are given loans, then go on to default, harming their credit score • Are some harms especially bad?
  100. 100. Product Introspection (2): Identify Potential Harms Types of Harms • Representational • Allocative • Exclusion e.g., image classification • Disadvantage e.g., poor recommendations • Opportunity Denial e.g., loans
  101. 101. Seek out Diverse Perspectives • Fairness Experts • User Researchers • Privacy Experts • Legal • Social Science Backgrounds • Diverse Identities • Gender • Sexual Orientation • Race • Nationality • Religion
  102. 102. Strengthen your Ethical Thinking Skills https://aiethics.princeton.edu/case-studies/case-study-pdfs/ https://www.scu.edu/ethics-in-technology-practice/case-studies/ https://ethicalos.org/
  103. 103. Shad(e)vice™ LyricGram™ Predictions: - Language(text) - Abusive(text, language) User Harms: ??? Most impacted users: ??? Predictions: - CropFace(image) - Purchase(face, sunglasses) - FuturePurchases(face, sunglasses) User Harms: ??? Most impacted users: ? Q3: What are potential user harms? Q4: Which users are most likely to be affected?
  104. 104. Shad(e)vice™ LyricGram™ Predictions: - Language(text) - Abusive(text, language) User Harms: ??? Most impacted users: ??? Predictions: - CropFace(image) - Purchase(face, sunglasses) - FuturePurchases(face, sunglasses) User Harms: - Failure to detect face - Poor sunglass recommendations Most impacted users: - Dark skin - Cultural minorities Q3: What are potential user harms? Q4: Which users are most likely to be affected?
  105. 105. Shad(e)vice™ LyricGram™ Predictions: - Language(text) - Abusive(text, language) User Harms: - Failure to protect from abuse - Censoring of non-abusive comments Most impacted users: - Targets of hate speech - People using LGBT terms positively - Users discussing songs with sexual/violent lyrics Predictions: - CropFace(image) - Purchase(face, sunglasses) - FuturePurchases(face, sunglasses) User Harms: - Failure to detect face - Poor sunglass recommendations Most impacted users: - Dark skin - Cultural minorities Q3: What are potential user harms? Q4: Which users are most likely to be affected?
  106. 106. Launch with Confidence: Testing for Bias • How will you know if users are being harmed? • How will you know if harms are unfairly distributed? • Detailed testing practices are often not covered in academic papers • Discussing testing requirements is a useful focal point for cross-functional teams
  107. 107. Model Predictions Evaluate for Inclusion - Confusion Matrix
  108. 108. Model Predictions Positive Negative Evaluate for Inclusion - Confusion Matrix
  109. 109. Model Predictions Positive Negative ● Exists ● Predicted True Positives ● Doesn’t exist ● Not predicted True Negatives Evaluate for Inclusion - Confusion Matrix
  110. 110. Model Predictions Positive Negative ● Exists ● Predicted True Positives ● Exists ● Not predicted False Negatives ● Doesn’t exist ● Predicted False Positives ● Doesn’t exist ● Not predicted True Negatives Evaluate for Inclusion - Confusion Matrix
  111. 111. Efficient Testing for Bias • Development teams are under multiple constraints • Time • Money • Human resources • Access to data • How can we efficiently test for bias? • Prioritization • Strategic testing
  112. 112. Choose your evaluation metrics in light of acceptable tradeoffs between False Positives and False Negatives
  113. 113. Privacy in Images False Positive: Something that doesn’t need to be blurred gets blurred. Can be a bummer. False Negative: Something that needs to be blurred is not blurred. Identity theft. False Positives Might be Better than False Negatives
  114. 114. Spam Filtering False Negative: Email that is SPAM is not caught, so you see it in your inbox. Usually just a bit annoying. False Positive: Email flagged as SPAM is removed from your inbox. If it’s from a friend or loved one, it’s a loss! False Negatives Might Be Better than False Positives
  115. 115. Types of Practical Fairness Testing 1. Targeted Tests 2. Quick Tests 3. Comprehensive Tests 4. Ecologically Valid Tests 5. Adversarial Testing
  116. 116. 1. Targeted Tests Based on prior experience/knowledge • Computer Vision ⇒ Test for dark skin • Natural Language Processing ⇒ Test for gender stereotypes Cf. smoke tests (non-exhaustive tests that check that most important functions work)
  117. 117. Targeted Testing of a Gender Classifier [Joy Buolamwini & Timnit Gebru, 2018] • Facial recognition software: Higher accuracy for light skinned men • Error rates for dark skinned women: 20% - 34%
  118. 118. 2. Quick Tests • "Cheap" • Useful throughout product cycle • Spot check extreme cases • Low coverage but high informativity • Need to be designed thoughtfully, e.g. • World knowledge • Prior product failures
  119. 119. Quick Tests for Gender in Translate
  120. 120. Quick Counterfactual Testing: What If Tool
  121. 121. 3. Comprehensive Tests Include sufficient data for each subgroup • May include relevant combinations of attributes • Sometimes synthetic data is appropriate Particularly important if model will be used in larger system Cf. Unit tests (verify correct outputs for wide range of correct inputs)
  122. 122. Comprehensive Testing of a Toxic Language Detector [Dixon et al., 2018]
  123. 123. AUC Metrics for Comprehensive Testing • Subgroup AUC: • Subgroup Positives vs Subgroup Negatives • "BPSN" AUC: • Background Positives vs Subgroup Negatives • "BNSP" AUC: • Background Negatives vs Subgroup Positives
  124. 124. Comprehensive Testing of a Toxicity Detector https://github.com/conversationai/perspectiveapi/blob/master/model_cards/English/toxicity.md
  125. 125. Inclusive Images Competition
  126. 126. 4. Ecologically Valid Testing Data is drawn from a distribution representative of the deployment distribution • Goal is NOT to be representative of the training distribution • (When appropriate) Condition on labels & certainty Example usage scenarios : • Continuous monitoring • You have historical product usage data • You can estimate user distribution reasonably well
  127. 127. Ecologically Valid Testing: Distributions Matter What is being compared? Over what data?
  128. 128. Challenges with Ecologically Valid Testing • Post-deployment distributions may not be known • Product may not be launched yet! • Sensitive attributes often not available in deployment • User distributions may change • We may want user distributions to change • e.g., broaden user base
  129. 129. 5. Adversarial Tests Search for rare but extreme harms • “Poison needle in haystack” • Requires knowledge of society Typical usage scenario: • Close to launch
  130. 130. Hypothetical Example of Adversarial Testing • Emoji autosuggest: are happy emoji suggested for sad sentences? My dog has gone to heaven Suggest: Input: 😊
  131. 131. Summary of Practical Fairness Testing 1. Targeted Tests: domain specific (image, language, etc) 2. Quick Tests: cheap tests throughout dev cycle 3. Comprehensive Tests: thorough 4. Ecologically Valid Tests: real-world data 5. Adversarial Testing: find poison needles
  132. 132. Fairness Testing Practices are Good ML Practices • Confidence in your product's fairness requires fairness testing • Fairness testing has a role throughout the product iteration lifecycle • Contextual concerns should be used to prioritize fairness testing
  133. 133. Shad(e)vice™ LyricGram™ Predictions: - Language(text) - Abusive(text, language) User Harms: ??? - Failure to detect from abuse - Censoring of non-abusive comments Most impacted users: ??? - Groups which are targets of hate speech - Groups writing comments - confusable for abuse - in multiple languages Predictions: - CropFace(image) - Purchase(face, sunglasses) - FuturePurchases(face, sunglasses) User Harms: - Failure to detect face - Poor sunglass recommendations Most impacted users: - Dark skin - Cultural minorities Q5: How could you test for fairness?
  134. 134. Shad(e)vice™ Predictions: - CropFace(image) - Purchase(face, sunglasses) - FuturePurchases(face, sunglasses) User Harms: - Failure to detect face - Poor sunglass recommendations Most impacted users: - Dark skin - Cultural minorities Q5: How could you test for fairness? • targeted testing: • faces with darker skin tones • comprehensive testing: • diverse gender expression (male/female/androgynous) • diverse headwear (caps, hijabs, turbans, ...) • ecologically valid testing: • compare sales conversions for users of different skin tones and locations • adversarial testing: • does system recommend sunglasses which are offensive or have cultural, religious or political associations?
  135. 135. LyricGram™ Predictions: - Language(text) - Abusive(text, language) User Harms: ??? - Failure to detect from abuse - Censoring of non-abusive comments Most impacted users: ??? - Targets of hate speech - People using LGBT terms positively - Users discussing songs with sexual/violent lyrics Q5: How could you test for fairness? • targeted testing: • comments about gangster rap lyrics • comments using LGBT identity terms • comprehensive testing: • comments using many identity terms • comments in many languages • ecologically valid testing: • compare error rates for different song genres • compare error rates for users in different locations • adversarial testing: • comments with offensive slang which model may not have seen before
  136. 136. Fairness-aware Data Collection [Holstein et al., 2019] • ML literature generally assumes data is fixed • Often the solution is more and/or better training data But: need to be Thoughtful! When might more Data not Help? • If your data sampling techniques are biased • Fundamental problems in data quality [Eckhouse et al., 2018] • What does your data really represent? E.g. crimes vs arrests • Recall: Product Introspection: How do Predictions relate to Decisions?
  137. 137. Get to Know Your Training Data: Facets Dive
  138. 138. Datasheets for Datasets [Gebru et al., 2018]
  139. 139. Datasheets for Datasets [Gebru et al., 2018]
  140. 140. Fairness-Aware Data Collection Techniques 1. Address population biases • Target under-represented (with respect to the user population) groups 2. Address representation issues • Oversample from minority groups • Sufficient data from each group may be required to avoid model treating them as "outliers" 3. Data augmentation: synthesize data for minority groups • E.g. from observed "he is a doctor" → synthesize "she is a doctor" 4. Fairness-aware active learning • Collect more data for group with highest error rates
  141. 141. Shad(e)vice™ LyricGram™ Predictions: - Language(text) - Abusive(text, language) User Harms: - Failure to protectfrom abuse - Censoring of non-abusive comments Most impacted users: - Groups which are targets of hate speech - Groups writing comments - confusable for abuse - in multiple languages Predictions: - CropFace(image) - Purchase(face, sunglasses) - FuturePurchases(face, sunglasses) User Harms: - Failure to detect face - Poor sunglass recommendations Most impacted users: - Dark skin - Cultural minorities Q6: How could you improve your training data?
  142. 142. Shad(e)vice™ Predictions: - CropFace(image) - Purchase(face, sunglasses) - FuturePurchases(face, sunglasses) User Harms: - Failure to detect face - Poor sunglass recommendations Most impacted users: - Dark skin - Cultural minorities Q6: How could you improve your training data? • Include more faces from under- represented groups • Include historical data from offline sales (rather than just online sales) • Include more recent data (more predictive of fashion trends)
  143. 143. LyricGram™ Predictions: - Language(text) - Abusive(text, language) User Harms: - Failure to detect from abuse - Censoring of non-abusive comments Most impacted users: - Groups which are targets of hate speech - Groups writing comments - confusable for abuse - in multiple languages Q6: How could you improve your training data? • Include more comments data related to under-represented music genres • Include comments data unrelated to music • especially from minority language variants • Include more recent data (discussing new songs)
  144. 144. Sometimes data biases are unavoidable Solution: ML Techniques
  145. 145. Practical Concerns with Fair Machine Learning •Is the training process stable? •Can we guarantee that fairness policies will be satisfied? • Cf. Legal requirements in education, employment, finance
  146. 146. Machine Learning Techniques: Adversarial Training? P(Label=1) P(Group) Negative Gradient Fairly well-studied with some nice theoretical guarantees. But can be difficult to train. Features, Label, Group
  147. 147. Machine Learning: Correlation Loss [Beutel et al., 2018] Motivation: Overcome training instability with adversarial training Key idea: include fairness objective in the loss function
  148. 148. Predicted P(Target) distribution for “Blue” and “Red” examples (Illustrative Example) min Loss(Label, Pred) Pred = P(Label=1) Features, Label, Group Machine Learning Techniques: Correlation Loss
  149. 149. min Loss(Label, Pred) + Abs(Corr(Pred, Group))|Label=0 Pred = P(Label=1) Predicted P(Target) distribution for “Blue” and “Red” examples (Illustrative Example) Features, Label, Group Machine Learning Techniques: Correlation Loss
  150. 150. ● Computed per batch ● Easy to use ● More stable than adversarial training. min Loss(Label, Pred) + Abs(Corr(Pred, Group))|Label=0 Pred = P(Label=1) Features, Label, Group Machine Learning Techniques: Correlation Loss
  151. 151. Machine Learning: Constrained Optimization [Cotter et al., 2018] Motivation: Can we ensure that fairness policies are satisfied? • Fairness goals are explicitly stated as constraints on predictions, e.g. • FPR on group 1 <= 0.8 * FPR on group 2 • Machine learner optimizes objective function subject to the constraints
  152. 152. Model Cards for Model Reporting[Mitchell et al., 2018]
  153. 153. Further Machine Learning Techniques Many more approaches are linked to from the tutorial website. https://sites.google.com/corp/view/wsdm19-fairness-tutorial
  154. 154. Fairness in UI/Product Design 1. Robust UIs handle ML failures gracefully 2. UIs should empower users
  155. 155. Shad(e)vice™ LyricGram™ Predictions: - Language(text) - Abusive(text, language) User Harms: ??? - Failure to detect from abuse - Censoring of non-abusive comments Most impacted users: ??? - Groups which are targets of hate speech - Groups writing comments - confusable for abuse - in multiple languages Predictions: - CropFace(image) - Purchase(face, sunglasses) - FuturePurchases(face, sunglasses) User Harms: - Failure to detect face - Poor sunglass recommendations Most impacted users: - Dark skin - Cultural minorities Q7: How could UI Help to Mitigate Unfairness?
  156. 156. Shad(e)vice™ Predictions: - CropFace(image) - Purchase(face, sunglasses) - FuturePurchases(face, sunglasses) User Harms: - Failure to detect face - Poor sunglass recommendations Most impacted users: - Dark skin - Cultural minorities Q7: How could UI Help to Mitigate Unfairness? • Be graceful when face detection fails • Still give some suggestions! • Use additional signals rather than just face • Let users crop image to face themselves • Give users some control in guiding predictions
  157. 157. LyricGram™ Predictions: - Language(text) - Abusive(text, language) User Harms: - Failure to detect from abuse - Censoring of non-abusive comments Most impacted users: - Groups which are targets of hate speech - Groups writing comments - confusable for abuse - in multiple languages Q7: How could UI Help to Mitigate Unfairness? • UI might distinguish quoted lyrics from user comments about those lyrics • Ability to flag comments for review • Ability to appeal automated censoring decisions
  158. 158. Thanks to Alex Beutel (Research Scientist, fairness in ML), Allison Woodruff (UX Research, privacy, fairness and ethics), Andrew Zaldivar (Developer Advocate, ethics and fairness in AI), Hallie Benjamin (Senior Strategist, ethics and fairness in ML), Jamaal Barnes (Program Manager, fairness in ML), Josh Lovejoy (UX Designer, People and AI Research; now Microsoft), Margaret Mitchell (Research Scientist, ethics and fairness in AI), Rebecca White (Program Manager, fairness in ML) and others!
  159. 159. Fairness Methods in Practice (Case Studies)
  160. 160. Google Assistant
  161. 161. [Play Google Assistant video from desktop] Key points: • Think about user harms • How does your product make people feel? • Adversarial ("stress") testing for all Google Assistant launches • People might say racist, sexist, homphobic stuff • Diverse testers • Think about expanding who your users could and should be • Consider the diversity of your users
  162. 162. Computer Vision
  163. 163. Google Camera Key points: • Check for unconscious bias • Comprehensive testing: "make sure this works for everybody"
  164. 164. Night Sight
  165. 165. This is a “Shirley Card” Named after a Kodak studio model named Shirley Page, they were the primary method for calibrating color when processing film. SKIN TONE IN PHOTOGRAPHY SOURCES Color film was built for white people. Here's what it did to dark skin. (Vox) How Kodak's Shirley Cards Set Photography's Skin-Tone Standard, NPR
  166. 166. Until about 1990, virtually all Shirley Cards featured Caucasian women. SKIN TONE IN PHOTOGRAPHY SOURCES Color film was built for white people. Here's what it did to dark skin. (Vox) Colour Balance, Image Technologies, and Cognitive Equity, Roth How Photography Was Optimized for White Skin Color (Priceonomics)
  167. 167. As a result, photos featuring people with light skin looked fairly accurate. SKIN TONE IN PHOTOGRAPHY SOURCES Color film was built for white people. Here's what it did to dark skin. (Vox) Colour Balance, Image Technologies, and Cognitive Equity, Roth How Photography Was Optimized for White Skin Color (Priceonomics) Film Kodachrome Year 1970 Credit Darren Davis, Flickr
  168. 168. Photos featuring people with darker skin, not so much... SKIN TONE IN PHOTOGRAPHY SOURCES Color film was built for white people. Here's what it did to dark skin. (Vox) Colour Balance, Image Technologies, and Cognitive Equity, Roth How Photography Was Optimized for White Skin Color (Priceonomics) Film Kodachrome Year 1958 Credit Peter Roome, Flickr
  169. 169. Google Clips
  170. 170. Google Clips "We created controlled datasets by sampling subjects from different genders and skin tones in a balanced manner, while keeping variables like content type, duration, and environmental conditions constant. We then used this dataset to test that our algorithms had similar performance when applied to different groups." https://ai.googleblog.com/2018/05/automat ic-photography-with-google-clips.html
  171. 171. Geena Davis Inclusion Quotient [with Geena Davis Institute on Gender in Media]
  172. 172. Machine Translation
  173. 173. (Historical) Gender Pronouns in Translate
  174. 174. Three Step Approach
  175. 175. 1. Detect Gender-Neutral Queries Train a text classifier to detect when a Turkish query is gender-neutral. • trained on thousands of human-rated Turkish examples
  176. 176. 2. Generate Gender-Specific Translations • Training: Modify training data to add an additional input token specifying the required gender: • (<2MALE> O bir doktor, He is a doctor) • (<2FEMALE> O bir doktor, She is a doctor) • Deployment: If step (1) predicted query is gender-neutral, add male and female tokens to query • O bir doktor -> {<2MALE> O bir doktor, <2FEMALE> O bir doktor}
  177. 177. 3. Check for Accuracy Verify: 1. If the requested feminine translation is feminine. 2. If the requested masculine translation is masculine. 3. If the feminine and masculine translations are exactly equivalent with the exception of gender-related changes.
  178. 178. Result: Reduced Gender Bias in Translate
  179. 179. Smart Compose
  180. 180. Adversarial Testing for Smart Compose in Gmail
  181. 181. Conversational Agents
  182. 182. Responsible AI and Conversational Agents
  183. 183. Background: Conversational Agents • Social bots • Informational bots • Task-oriented bots Eliza, Joseph Weizenbaum (MIT) 1964
  184. 184. Design Data Model Application
  185. 185. Design Data Model Application
  186. 186. Design Data Model Application Questions to ask during AI Design Who is affected by AI? How might AI cause harms?
  187. 187. Stakeholders: Who might be affected? 1. Humans speaking with the agent • Emotional harms, misinformation, threaten task completion 2. The agent “owner” • Harm practices and reputation of the owner 3. Third-party individuals and groups • People mentioned in conversations! 4. Audiences listening to the conversation • This may include general public!
  188. 188. How might AI cause harms? Functional Harms • Misrepresentation of capabilities • Misinforming user about task status • Misunderstanding user and doing the wrong task • Revealing private information inappropriately Yes, I can do that! Can you order sushi for me? Great, one California roll please I don’t understand. ?!?!
  189. 189. How might AI cause harms? Functional Harms Functional Harms • Misrepresentation of capabilities • Misinforming user about task status • Misunderstanding user and doing the wrong task • Revealing private information inappropriately In just 1 minute When will my order arrive? Where’s my order? Arriving in 1 minute ?!?!
  190. 190. How might AI cause harms? Functional Harms Functional Harms • Misrepresentation of capabilities • Misinforming user about task status • Misunderstanding user and doing the wrong task • Revealing private information inappropriately Ordering egg yolks Tell me a joke ?!?!
  191. 191. How might AI cause harms? Functional Harms Functional Harms • Misrepresentation of capabilities • Misinforming user about task status • Misunderstanding user and doing the wrong task • Revealing private information inappropriately Bob Smith’s CC number is … What’s Bob Smith’s number? ?!?!
  192. 192. How might AI cause harms? Functional Harms • Misrepresentation of capabilities • Misinforming user about task status • Misunderstanding user and doing the wrong task • Revealing private information inappropriately These harms are even more problematic when they systematically occur for some groups of people but not others
  193. 193. How might AI cause harms? Social Harms: Harms to Individuals • Inciting/encouraging harmful behavior • Self/harm, suicide • Violence or harassment against others • Discouraging good behavior, e.g., visiting doctors • Providing wrong information • Medical, financial, legal advice • Verbal harassment • Bullying, sexual harassment
  194. 194. How might AI cause harms? Social Harms: Harms to Communities • Promoting violence, war, ethnic cleansing, … • Including promoting related organizations and philosophies • Engaging in hate speech, disparagement, mocking, … • Including inadvertent, or Inappropriate imitation (dialect, accent,…) • Disruption to social processes • Election disruption, fake news, false disaster response, …
  195. 195. Why is this hard? Language is ambiguous, complex, with social context Examples of complex failures: • Failure to deflect/terminate contentious topics • Refusing to discuss when disapproval would be better • Polite agreement with unrecognized bias Yes, I can do that! Let’s talk about <something evil>
  196. 196. Why is this hard? Language is ambiguous, complex, with social context Examples of complex failures: • Failure to deflect/terminate contentious topics • Polite agreement with unrecognized bias • Refusing to discuss when disapproval would be better Sounds ok. Men are better at <whatever> than women
  197. 197. Why is this hard? Language is ambiguous, complex, with social context Examples of complex failures: • Failure to deflect/terminate contentious topics • Polite agreement with unrecognized bias • Refusing to discuss when disapproval would be better I don’t like talking about religion I was bullied at school because I’m muslim
  198. 198. Design Data Model Application
  199. 199. Implications for data collection Common data sources • Hand-written rules • Existing conversational data (e.g., social media) • New online conversations (e.g., from new customer interactions) Cleaning training data • For anonymization • E.g., remove individual names. But keep famous names (fictional characters, celebrities, politicians, …) • Ensure adheres to social norms • Not enough to filter individual words: Filter “I hate [X]”, and you’ll miss “I’m not a fan of [X]. • Remember meanings change with context • Differentiate between bot input and bot output in training data • Remove offensive text from bot output training • But don’t remove from bot inputs  allow learning of good responses to bad inputs
  200. 200. Design Data Model Application
  201. 201. Design Data Model Application Responsible bots: 10 guidelines for developers of conversational AI 1. Articulate the purpose of your bot 2. Be transparent that you use bots 3. Elevate to a human when needed 4. Design bot to respect cultural norms 5. Ensure bot is reliable (metrics, feedback) 6. Ensure your bot treats people fairly 7. Ensure your bot respects privacy 8. Ensure your bot handles data securely 9. Ensure your bot is accessible 10.Accept responsibility https://www.microsoft.com/en-us/research/publication/responsible-bots/
  202. 202. Design Data Model Application Responsible bots: 10 guidelines for developers of conversational AI 1. Articulate the purpose of your bot 2. Be transparent that you use bots 3. Elevate to a human when needed 4. Design bot to respect cultural norms 5. Ensure bot is reliable (metrics, feedback) 6. Ensure your bot treats people fairly 7. Ensure your bot respects privacy 8. Ensure your bot handles data securely 9. Ensure your bot is accessible 10.Accept responsibility https://www.microsoft.com/en-us/research/publication/responsible-bots/
  203. 203. Design Data Model Application Responsible bots: 10 guidelines for developers of conversational AI 1. Articulate the purpose of your bot 2. Be transparent that you use bots 3. Elevate to a human when needed 4. Design bot to respect cultural norms 5. Ensure bot is reliable (metrics, feedback) 6. Ensure your bot treats people fairly 7. Ensure your bot respects privacy 8. Ensure your bot handles data securely 9. Ensure your bot is accessible 10.Accept responsibility https://www.microsoft.com/en-us/research/publication/responsible-bots/
  204. 204. Key take-away points • Many stakeholders affected by conversational agent AIs • Not only people directly interacting with AI, but also indirectly affected • Many potential functional, social harms to individuals, communities • Functional harms exacerbated when systematically biased against groups • Challenges include complexity and ambiguity of natural language • Avoiding these harms requires careful consideration across the entire AI lifecycle.
  205. 205. Acknowledgments • Chris Brockett, Bill Dolan, Michel Galley, Ece Kamar
  206. 206. Deep Dive: Talent Search Fairness in AI @ LinkedIn
  207. 207. Create economic opportunity for every member of the global workforce LinkedIn’s Vision Connect the world's professionals to make them more productive and successful LinkedIn’s Mission
  208. 208. 610M Members 30M Companies 20M Jobs 50K Skills 90K Schools 100B+ Updates viewed LinkedIn Economic Graph
  209. 209. AI @LinkedIn 25 B ML A/B experiments per week data processed offline per day 2002.15 PB data processed nearline per day 2 PB Scale graph edges with 1B nodes 53 B parameters in ML models
  210. 210. Guiding Principle: “Diversity by Design”
  211. 211. “Diversity by Design” in LinkedIn’s Talent Solutions Insights to Identify Diverse Talent Pools Representative Talent Search Results Diversity Learning Curriculum
  212. 212. Plan for Diversity
  213. 213. Plan for Diversity
  214. 214. Identify Diverse Talent Pools
  215. 215. Inclusive Job Descriptions / Recruiter Outreach
  216. 216. Representative Ranking for Talent Search S. C. Geyik, K. Kenthapadi, Building Representative Talent Search at LinkedIn, LinkedIn engineering blog post, October’18.
  217. 217. Intuition for Measuring Representativeness • Ideal: same distribution on gender/age/… for • Top ranked results and • Qualified candidates for a search request • LinkedIn members matching the search criteria • Same proportion of members with each given attribute value across both these sets • “Equal opportunity” definition [Hardt et al, NIPS’16]
  218. 218. Measuring (Lack of) Representativeness • Skew@k • (Logarithmic) ratio of the proportion of candidates having a given attribute value among the top k ranked results to the corresponding proportion among the set of qualified candidates • Minimum Discrete Skew: Minimum over all attribute values genders (e.g., the most underrepresented gender’s skew value). • Skew = 0 if we have ⌊pq,r,v * k⌋ candidates from value v in the top k results
  219. 219. Reranking Algorithm for Representativeness • Determine the target proportions within the attribute of interest, corresponding to a search request • Compute a fairness-aware ranking of size k
  220. 220. Target Proportions within the Attribute of Interest • Compute the proportions of the values of the attribute (e.g., gender, gender-age combination) amongst the set of qualified candidates • “Qualified candidates” = Set of candidates that match the search query criteria • Retrieved by LinkedIn’s Galene search engine • Target proportions could also be obtained based on legal mandate / voluntary commitment
  221. 221. Fairness-aware Reranking Algorithm • Partition the set of potential candidates into different buckets for each attribute value • Rank the candidates in each bucket according to the scores assigned by the machine-learned model • Merge the ranked lists, balancing the representation requirements and the selection of highest scored candidates
  222. 222. Architecture
  223. 223. Validating Our Approach • Gender Representativeness • Over 95% of all searches are representative compared to the qualified population of the search • Business Metrics • A/B test over LinkedIn Recruiter users for two weeks • No significant change in business metrics (e.g., # InMails sent or accepted) • Ramped to 100% of LinkedIn Recruiter users worldwide
  224. 224. Key takeaway points • Post-processing approach desirable • Agnostic to the specifics of each model • Scalable across different model choices for our application • Robust to application-specific business logic • Easier to incorporate as part of existing systems • Build a stand-alone service or component for post-processing • No significant modifications to the existing components • Complementary to efforts to reduce bias from training data & during model training
  225. 225. Acknowledgements •Team: • AI/ML: Sahin Cem Geyik, Stuart Ambler, Krishnaram Kenthapadi • Application Engineering: Gurwinder Gulati, Chenhui Zhai • Analytics: Patrick Driscoll, Divyakumar Menghani • Product: Rachel Kumar •Acknowledgements (in alphabetical order) • Deepak Agarwal, Erik Buchanan, Patrick Cheung, Gil Cottle, Nadia Fawaz, Rob Hallman, Joshua Hartman, Sara Harrington, Heloise Logan, Stephen Lynch, Lei Ni, Igor Perisic, Ram Swaminathan, Ketan Thakkar, Janardhanan Vembunarayanan, Hinkmond Wong, Lin Yang, Liang Zhang, Yani Zhang
  226. 226. Reflections • Lessons from fairness challenges  Need “Fairness by Design” approach when building AI products • Case studies on fairness-aware ML in practice • Collaboration/consensus across key stakeholders (product, legal, PR, engineering, AI, …)
  227. 227. Key Takeaways
  228. 228. Good ML Practices Go a Long Way • Lots of low hanging fruit in terms of improving fairness simply by using machine learning best practices • Representative data • Introspection tools • Visualization tools • Testing • Fairness improvements often lead to overall improvements • It’s a common misconception that it’s always a tradeoff
  229. 229. Breadth and Depth Required • Looking End-to-End is critical • Need to be aware of bias and potential problems at every stage of product and ML pipelines (from design, data gathering, … to deployment and monitoring) • Details Matter • Slight changes in features or labeler criteria can change the outcome • Must have experts who understand the effects of decisions • Many details are not technical such as how labelers are hired
  230. 230. The Real World is What Matters • Decisions should be made considering the real world goals and outcomes • You must have people involved that understand these real world effects • Social scientist, Lawyers, domain experts… • Hire experts (even ones that don’t code) • You need different types of testing depending on the application • We need more research focused on people, applications, and real world effects • A lot of the current research is not that useful in practice • We need more social science + machine learning research
  231. 231. Key Open Problems in Applied Fairness
  232. 232. Key Open Problems in Applied Fairness • What if you don’t have the sensitive attributes? • When should you use what approach? For example, Equal treatment vs equal outcome? • How to identify harms? • Process for framing AI problems: Will the chosen metrics lead to desired results? • How to tell if data generation and collection method is appropriate for a task? (e.g., causal structure analysis?) • Processes for mitigating harms and misbehaviors quickly
  233. 233. Related Tutorials / Resources • Sara Hajian, Francesco Bonchi, and Carlos Castillo, Algorithmic bias: From discrimination discovery to fairness-aware data mining, KDD Tutorial, 2016. • Solon Barocas and Moritz Hardt, Fairness in machine learning, NeurIPS Tutorial, 2017. • Arvind Narayanan, 21 fairness definitions and their politics, FAT* Tutorial, 2018. • Sam Corbett-Davies and Sharad Goel, Defining and Designing Fair Algorithms, Tutorials at EC 2018 and ICML 2018. • Ben Hutchinson and Margaret Mitchell, Translation Tutorial: A History of Quantitative Fairness in Testing, FAT* Tutorial, 2019. • Henriette Cramer, Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miroslav Dudík, Hanna Wallach, Sravana Reddy, and Jean Garcia-Gathright, Translation Tutorial: Challenges of incorporating algorithmic fairness into industry practice, FAT* Tutorial, 2019. • ACM Conference on Fairness, Accountability, and Transparency (ACM FAT*)
  234. 234. Fairness Privacy Transparency Explainability Related WSDM’19 sessions: 1.Tutorial: Privacy-Preserving Data Mining in Industry (Monday, 9:00 - 12:30) 2.H.V. Jagadish's invited talk: Responsible Data Science (Tuesday, 14:45 - 15:30) 3.Session 4: FATE & Privacy (Tuesday, 16:15 - 17:30) 4.Aleksandra Korolova’s invited talk: Privacy-Preserving WSDM (Wednesday, 14:45 - 15:30)
  235. 235. Thanks! Questions? •Tutorial website: https://sites.google.com/view/wsdm19-fairness- tutorial •Feedback most welcome  • slbird@microsoft.com, benhutch@google.com, kkenthapadi@linkedin.com, emrek@microsoft.com, mmitchellai@google.com

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