This talk explains why machine learning algorithms are prone to bias, shows concrete examples, and examines regulatory, conceptual, and technical means to address these issues.

1. Ethical Algorithms
Bias and Explainability in Machine Learning
VP Product & Strategy
@integrateai
Kathryn Hume
@humekathryn Venture Partner
quamproxime.com @ffvc

2. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

3. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

5. Algorithms are evil?

6. Algorithms are convex mirrors that refract human biases
Parmagianino, 1524 Ashbery, 1975

7. Why?

8. The Raw Ingredients
Deep understanding of a business problem
Data, data, data
Algorithmic capability

9. Data Product Lifecycle
Design
What problem
are we solving?
Data Exploration
What does the data
look like?
Data Engineering
Production
Can we harden and
scale the model?
Maintenance
How to update
as data changes?
Data Processing
Is our data ready
for use?
Model Prototyping
Will this work?
Should we pivot?

10. Supervised and Unsupervised Learning

11. Unsupervised Learning

12. Clustering households based on TV-viewing habits

13. 70% of machine learning products use
supervised learning
https://www.sas.com/en_ca/insights/analytics/machine-learning.html

14. Supervised Learning
Find a function that defines a correlation between P and C
Use this function to make guesses about C
Find a proxy (P) for something hard to know (C)

15. Use square footage (P) to
predict housing prices (C)

16. Use “Nigerian Prince” (P) to
predict if emails are spam (C)

17. What (P) should we pick
to decide if it’s a cat or dog?

18. Deep Learning
• Use layers to transform complex input into mathematical expressions
• Remove need for human to select which features matter

19. Universal Approximation Theorem
Neural networks can approximate arbitrary functions

21. The art lies in designing your model,
not feature engineering

23. Where does bias creep in?

24. Classical Statistics & ML
Higher unconscious bias
in feature selection
Higher explainability
in the model
Deep Learning
Lower unconscious bias
in feature selection
Lower explainability
in the model

26. Introduce Blind Fairness?

27. https://visual.ly/community/infographic/
human-rights/taxonomy-transitions
Redundant Encodings

28. Fairness does not align with accuracy
http://blog.mrtz.org/2016/09/06/approaching-fairness.html

29. the controversial world of sentiment analysis

30. • System use algorithms to identify negative sentiment
• Performs better with strident, unambiguous expressions of emotions
• Men more likely to use those expressions
• Men attract disproportionate attention from brands
https://blog.dominodatalab.com/video-how-machine-learning-amplifies-societal-privilege/

31. Bluntness and bias
• Precision <> Recall
• Marketing wants high precision
• Implies low recall
• We’re better at identifying extremes
• They’re likely in a particular group

32. https://medium.com/@blaisea/physiognomys-new-clothes-f2d4b59fdd6a

33. Developments in Language Processing
Traditional NLP N-grams Word Embeddings

34. Bolukbasi, Chang, Zou, Saligrama, Kalai, 2016
Man : King :: Woman : Queen
Man : Computer Programmer :: Woman : Homemaker
Black Male : Assaulted :: White Male: Entitled To
Inherent Bias in Word Embeddings

35. http://www.andrew.cmu.edu/user/danupam/dtd-pets15.pdf

36. What can we do about it?

37. Regulate to require explainability

38. Machine Talk People Talk
h/t Hilary Mason of Fast Forward Labs

40. Affect important human rights
Education
Housing
Health
Work
Justice
Finance/credit

41. Lack of understanding stymies adoption

42. Deterministic framework on a probabilistic tool

43. Refine our conceptual framework

44. Inflated media rhetoric

45. https://medium.com/inventing-intelligent-machines/machine-learning-alien-knowledge-and-other-
ufos-1a44c66508d1
Observations, not explanations

47. Comfort with competence without comprehension

48. How come?
What for?
Can I intervene?

49. Get into the guts of the technology

51. Bolukbasi, Chang, Zou, Saligrama, Kalai, 2016
Remove bias without compromising utility

52. FairML: Measure dependence on inputs by changing them
http://blog.fastforwardlabs.com/2017/03/09/fairml-auditing-black-box-predictive-models.html

53. LIME: As if linear functions
https://homes.cs.washington.edu/~marcotcr/blog/lime/

54. https://homes.cs.washington.edu/~marcotcr/blog/lime/
LIME: As if linear functions

56. Fair representations: treat similar individuals similarly
http://proceedings.mlr.press/v28/zemel13.html

57. Fair representations: treat similar individuals similarly
http://proceedings.mlr.press/v28/zemel13.html
“We formulate fairness as an optimization
problem of finding an intermediate representation
of the data that best encodes the data (i.e., preserving
as much information about the individual’s attributes
as possible), while simultaneously obfuscates aspects of
it, removing any information about membership with
respect to the protected subgroup.”

58. Equal Opportunity

59. Equal Opportunity

60. LOVE PEOPLE
Find opportunities to maximize mutual lifetime value
Respect the principles of contextual integrity
Protect individual and corporate data using differential privacy
Consider the goals of the people affected by the systems we build

61. Questions?
@integrateai@humekathryn quamproxime.com @ffvc