Eliminating Machine Bias - Mary Ann Brennan - ML4ALL 2018
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Slides from Mary Ann Brennan's ML4ALL Machine Learning conference talk on Machine Bias. Machine Learning algorithms often exhibit systematic, unfair bias against certain groups of people, all while the supposed impartiality of machines dissolves accountability. I touch on definitions, criminal justice, and solutions and finish with a success story from NLP and a slide listing resources. Video here: https://www.youtube.com/watch?v=VksAAI60GEc
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SHOULD ALGORITHMS DECIDE YOUR FUTUREThis publication was .docx
SHOULD ALGORITHMS DECIDE YOUR FUTURE?
This publication was prepared by Kilian Vieth and
Joanna Bronowicka from Centre for Internet and
Human Rights at European University Viadrina. It was
prepared based on a publication “The Ethics of
Algorithms: from radical content to self-driving cars”
with contributions from Zeynep Tufekci, Jillian C. York,
Ben Wagner and Frederike Kaltheuner and an event
on the Ethics of Algorithms, which took place on
March 9-10, 2015 in Berlin. The research was support-
ed by the Dutch Ministry of Foreign Affairs.
Find out more: cihr.eu/ethics-of-algorithms/
Follow the discussion on Twitter: #EoA2015
Graphic design by Thiago Parizi
cihr.eu @cihr_eu
1 | ETHICS OF ALGORITHMS ETHICS OF ALGORITHMS | 2
WHAT IS AN ALGORITHM?
ALGORITHMS SHAPE OUR WORLD(S)!
Our everyday life is shaped by computers and our computers are shaped
by algorithms. Digital computation is constantly changing how we commu-
nicate, work, move, and learn. In short, digitally connected computers are
changing how we live our lives. This revolution is unlikely to stop any time
soon.
Digitalization produces increasing amounts of datasets known as ‘big
data’. So far, research focused on how ‘big data is produced and stored.
Now, we begin to scrutinize how algorithms make sense of this growing
amount of data
Algorithms are the brains of our computers, mobiles, Internet of Things.
Algorithms are increasingly used to make decisions for us, about us, or
with us – oftentimes without us realizing it. This raises many questions
about the ethical dimension of algorithms.
WHY DO ALGORITHMS RAISE ETHICAL
CONCERNS?
First, let's have a closer look at some of the critical features of algorithms.
What are typical functions they perform? What are negative impacts for
human rights? Here are some examples that probably affect you too.
THEY KEEP INFORMATION AWAY FROM US
Increasingly, algorithms decide what gets attention, and what is ignored;
and even what gets published at all, and what is censored. This is true for
all kinds of search rankings, for example the way your social media news-
feed looks. In other words, algorithms perform a gate-keeping function.
EXAMPLE
Hiring algorithms decide if you are invited for an interview.
• Algorithms, rather than managers, are more and more taking part in
hiring (and firing) of employees. Deciding who gets a job and who does
not, is among the most powerful gate-keeping function in society.
• Research shows that human managers display many different biases in
hiring decisions, for example based on social class, race and gender.
Clearly, human hiring systems are far from perfect.
• Nevertheless, we may not simply assume that algorithmic hiring can
easily overcome human biases. Algorithms might work more accurate
in some areas, but can also create new, sometimes unintended, prob-
lems depending on how they are programmed and what input data is
used.
Ethical.
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Eliminating Machine Bias - Mary Ann Brennan - ML4ALL 2018
- 1. Eliminating Machine Bias ML4ALL - May 29, 2018 Addressing
- 2. What is machine bias? • Machine Bias, or Algorithmic Bias, occurs when machine intelligence exhibits bias against groups of people, usually systematic and unfair • Algorithmic Fairness describes efforts to ensure people are treated equally by algorithms Not related to bias/ variance or model bias
- 3. Human bias Racial Bias Motherhood Penalty Gender Bias Ableism Religious Bias Unconscious Bias Xenophobia Ageism Positive Bias Fatherhood Bonus “People who wear kilts are the worst” Bernie Bros “Android users are fools”
- 4. Machines are seen as impartial Machine learning is like money laundering for bias. It's a clean, mathematical apparatus that gives the status quo the aura of logical inevitability. —Maciej Cegłowski http://idlewords.com/talks/sase_panel.htm
- 5. PredPol - Predictive policing Higher observed crime rates in an area Additional policing of the area Additional observation of crimes To predict and serve?, Volume: 13, Issue: 5, Pages: 14-19, First published: 07 October 2016, DOI: (10.1111/j.1740-9713.2016.00960.x) Drug arrests vs. estimated use To predict and serve?, Volume: 13, Issue: 5, Pages: 14-19, First published: 07 October 2016, DOI: (10.1111/j.1740-9713.2016.00960.x) Simulated effects of additional observed crime To predict and serve? Lum, Isacc 2016
- 6. COMPAS - Risk assessment White Black Higher risk, didn’t reoffend 23.5% 44.9% Low risk, did reoffend 47.7% 28.0% Machine Bias - ProPublica White Black Higher risk, re- offended 505 1369 Total higher risk 854 2174 Positive predictive value (PPV) 59% 63% Demo at http://bit.ly/BIAS4ALL
- 7. COMPAS - Risk assessment A Popular Algorithm Is No Better at Predicting Crimes Than Random People The Atlantic - January 17, 2018 The accuracy, fairness, and limits of predicting recidivism (Dressel and Farid 2018) COMPAS: 65.2% Average human: 62.8% Pooled humans: 67%
- 8. Protected attributes Gender Age College Major Favorite Movie Genre Female 23 English Romantic Comedy Male 78 Physics Science Fiction Neutral 48 Economics Action/ Adventure
- 10. More diverse teams • A more diverse team will likely test with a wider variety of data • Members of diverse groups are more likely to find biases in code • This is a good idea anyway
- 11. Other ways to uncover bias • Community Policing: allow users to report biased results • Algorithmic auditing: analyze performance on different inputs • Ask for help! Algorithmic Justice League offers to help detect bias, ORCAA consulting firm
- 12. Interpretability Deep Residual Learning for Image Recognition (He et al 2015)
- 13. Word embeddings apple orange grapefruit …apple juice… …orange juice… …grapefruit juice…
- 14. Word embeddings father motherking queen mother : father :: king : ?
- 15. Word embeddings father motherking queen mother - father + king ≈ ?
- 16. Word embeddings father motherdoctor nurse mother - father + doctor ≈ ?
- 17. Google Translate is sexist Turkish: • o bir doktor • o bir hemşire • o evli • o bekar English: • he is a doctor • she is a nurse • she is married • he is single
- 18. Word embeddings father motherdoctor nurse Demo at http://bit.ly/BIAS4ALL
- 19. What can we do? What happens when an algorithm cuts your health care The Verge - Mar 21, 2018 PEOPLE cut • Support tools, standards, and regulation! GDPR, algorithmic impact assessment, new NYC law • Talk about these issues with your communities!
- 20. Resources Organizations and conferences • Orgs: Algorithmic Justice League, AI Now, Algorithm Watch, AFOG - Berkeley, fairness.haverford.edu • Confs: FAT/ML, FAT Conference • Consulting: ORCAA • Data-focused: Data & Society, Human Rights Data Analysis Group Technical resources • Debiaswe - word embeddings and source from Bolukbasi • COMPAS Analysis on GitHub • Deep Learning on Coursera Human bias/justice • Project Implicit - Implicit bias tests • @pdxlawgrrrl, @shaunking, @Data4BlackLives, so many more • hiremorewomenintech.com (and all underrepresented people) Books • Weapons of Math Destruction by Cathy O’Neil • Automating Inequality by Virginia Eubanks