Slides for an intro talk I gave at the first DDMA Monthly Meetup AI.

1. DDMA Monthly Meetup AI @ online, 8 April 2021
Pragmatic ethical and fair AI for
data scientists
👨 David Graus
✉ david.graus@randstadgroep.nl
🌐 www.graus.nu
🐦 @dvdgrs

2. ⚠ Disclaimer
This slide deck contains opinion: mine (not my employer’s)

3. Motivation
Narrative: From Negative

4. Motivation
Narrative: From Negative to Positive

5. Topic 1:
⚖ Fair AI in recruitment & HR

6. bias is everywhere
in humans…
“All the curricula vitae actually came
from a real-life scientist [...] but the
names were changed to traditional
male and female names.
[…]
Both men and women were more
likely to hire a male job applicant
than a female job applicant with an
identical record.”
Rhea E. Steinpreis, Katie A. Anders and Dawn Ritzke. The Impact of Gender on the Review
of the Curriculum Vitae of Job Applicants and Tenure Candidates. Sex Roles, Springer.

7. bias is everywhere
in humans…
“White” names receive 50 percent
more callbacks for interviews [than
“African-American” names]. Results
suggest that racial discrimination
is still a prominent feature of the
labor market.
Marianne Bertrand and Sendhil Mullainathan. Are Emily and Greg More Employable
than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination. NBER.

8. bias is everywhere
…and algorithms
Amazon’s system taught itself that
male candidates were preferable. It
penalized résumés that included
the word “women’s”, as in
“women’s chess club captain”. And
it downgraded graduates of two
all-women’s colleges.
Amazon ditched AI recruiting tool that favored men for technical jobs
[https://www.theguardian.com/technology/2018/oct/10/amazon-hiring-ai-gender-bias-
recruiting-engine]

9. bias is everywhere
…and algorithms
“We examine gender inequality on
the resume search engines. We ran
queries on each site’s resume
search engine for 35 job titles. [...]
even when controlling for all other
visible candidate features, there is a
slight penalty against feminine
candidates.”
Le Chen, Ruijun Ma, Anikó Hannák, and Christo Wilson. Investigating the Impact
of Gender on Rank in Resume Search Engines. CHI 2018, ACM.

10. bias is everywhere

11. bias is everywhere

12. bias is everywhere

13. bias is everywhere

14. bias is everywhere

15. 🦾🤖 AI to the rescue

16. Fair AI #1
Representational ranking
“representation re-ranking” makes
sure the proportion of female
candidates shown is the same as
the corresponding proportion of
profiles matching that query.

17. Fair AI #1
Representational ranking
“representation re-ranking” makes
sure the proportion of female
candidates shown is the same as
the corresponding proportion of
profiles matching that query.
“In the US, a form of demographic parity
is required by the Equal Employment
Opportunity Commission (EEOC)
according to the 4/5ths (or P%) rule.”
https://www.ifow.org/publications/artificial-intelligence-in-hiring-
assessing-impacts-on-equality

18. Fair AI #1
Representational ranking
“representation re-ranking” makes
sure the proportion of female
candidates shown is the same as
the corresponding proportion of
profiles matching that query.
• Results in improvement in fairness
metrics, without statistically significant
change in the business metrics.
• Deployed to all LinkedIn Recruiter users
worldwide.

19. Human bias mitigation through (over)compensation
• Can we adjust rankings to “fix” human bias?
• Yes: balancing gender representation in
candidate slates can correct biases for
some professions.
• No: although doing so has no impact on
professions where human persistent
preferences are at play, e.g., nannies and
gynaecologists.
• Gender of decision-maker, complexity of the
decision-making task and over- and under-
representation of genders in the candidate
slate can all impact the final decision.
Fair AI #2

20. Topic 2:
⚖ Value-driven news recommendation

21. (Editorial) value-driven AI
Context
Het Financieele Dagblad
Goal
Article recommendations (for reader)
upholding editorial values (of provider)

22. Editorial values?
• Participated in a study by Bastian and Helberger [1]
• “[Conducted] semi-structured interviews with employees from different departments (journalists,
data scientists, product managers), it explores the costs and benefits of value articulation and a
mission-sensitive approach in algorithmic news distribution from the newsroom perspective.”
• Resulting values;
1. surprise readers
2. timely and fresh news
3. diverse reading behavior
4. cover more articles
[1] Bastian & Helberger. Safeguarding the journalistic DNA. Future of journalism 2019.
[2] Ge et al. Beyond Accuracy: Evaluating Recommender Systems by Coverage
and Serendipity. RecSys 2010
➡ Serendipity
➡ Dynamism
➡ Diversity
➡ Coverage

23. RQ1
Does FD’s recommender system steer users to useful recommendations?
• Compare usefulness between recommended and manually curated articles
• 115 users
• One month of rankings (August, 2019)

24. RQ1
Does FD’s recommender system steer users to useful recommendations?
• Compare usefulness between recommended and manually curated articles
• 115 users
• One month of rankings (August, 2019)
Serendipity, Dynamism, Diversity, Coverage
➡

25. Metric
• Intra-list diversity
• For four article attributes: Sections, Tags,
Authors, Word Embeddings
Results
• recommendations are more diverse in article
topic/content
• manual curation is more diverse in authors
• both are diverse in tags
Usefulness 1: Diversity

26. Metric
• Inter-list diversity
Results
• when recommendations change, they tend to
change more
Usefulness 2: Dynamism

27. Metric
• articles’ average dissimilarity to a reader’s historic
articles
• (same attributes as diversity)
Results
• Manual curation yields more serendipitous
rankings in terms of tags and authors
• Recommended articles are more serendipitous in
content
Usefulness 3: Serendipity

28. Metric
• percentage of daily published articles that are
served
Results
• per user the recommendations provide a narrow
set of articles
• across all users, the overall coverage of
recommended articles is much higher than the
manual curated articles
Usefulness 4: Coverage

29. RQ1
Does FD’s recommender system steer users to useful recommendations?
✅

30. RQ2
Can we effectively adjust our news recommender to steer our readers
towards more dynamic reading behavior, without loss of accuracy?
Approach: ⚠ Intervention study
• Single usefulness treatment: Dynamism
• avoid exposing readers to sub-optimal rankings
• constrained by technical requirements

31. RQ2
Can we effectively adjust our news recommender to steer our readers
towards more dynamic reading behavior, without loss of accuracy?
Method: Online A/B test
• Control: the original recommender system
• Variant: the adjusted recommender system, steered towards more dynamic recommendations
• 2 weeks (November 25 to December 4, 2019)
• 1,108 users
• Each randomly assigned to one of the two treatments

32. Metric
Dynamism

33. Results
Dynamism
Accuracy
Dynamism

34. RQ2
Can we effectively adjust our news recommender to steer our readers
towards more dynamic reading behavior, without loss of accuracy?
✅

35. Take homes
Recommendations can benefit both news providers and readers
•Fairness and ethics are time-, culture-, and context-dependent
•Organizational/editorial/ethical values can be ‘operationalized’ in algorithms
•Data scientist: don’t try to define fairness (it’s not your job, nor expertise)
•But talk to stakeholders in your organization!
•Come up with a shared definition (combining what you can achieve
technically + what you want to achieve “conceptually”)
•Build! 🦾
Fin
thank you for your attention

36. discuss.