The document discusses big data ethics, data science ethics, and AI ethics, highlighting the importance of transparency, consent, and fairness in algorithms. It examines O'Neil's analysis of 'weapons of math destruction' and presents a case study on the COMPAS algorithm used for predicting recidivism, which disproportionately affects African-American individuals. Ethical concerns raised include data ownership, the scale and opacity of algorithms, and their potential damage to individuals' lives.

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Daniel Wilson (daniel.wilson@auckland.ac.nz)

2. 1. What is big data ethics / data science ethics / AI ethics?
Outline:
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4. How do we incorporate ethics into systems development?
2. O’Neil’s analysis of Weapons of Math Destruction (WMDs)
1. What is big data ethics / data science ethics / AI ethics?
3. Case study: COMPAS predictions of risk of recidivism

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Classic version AI version

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6. Some ethical questions in AI and data science:
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- Who owns data that is generated by you and/or about you
online?
- Why are transparent and explainable systems important?
- When should you get consent to run experiments on users?
- Under what conditions does your right to privacy
come into play?
- How do we judge whether an algorithm is fair?

7. Outline:
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1. What is big data ethics / data science ethics / AI ethics?
4. How do we incorporate ethics into systems development?
2. O’Neil’s analysis of Weapons of Math Destruction (WMDs)
3. Case study: COMPAS predictions of risk of recidivism

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The Truth About Algorithms | Cathy O'Neil
Audio extracted from a free talk given by O'Neil at the RSA in London, 2017.
https://www.youtube.com/watch?v=heQzqX35c9A

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Opacity: “Even if the participant is aware of
being modelled, or what the model is used for,
is the model opaque, or even invisible?”
Scale: Does the model have the capacity
to grow exponentially? Can it scale?
Damage: “Does the model work against
the subject’s interest? In short, is it
unfair? Does it damage or destroy lives?”

11. Outline:
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1. What is big data ethics / data science ethics / AI ethics?
4. How do we incorporate ethics into systems development?
2. O’Neil’s analysis of Weapons of Math Destruction (WMDs)
3. Case study: COMPAS predictions of risk of recidivism

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Opacity: The COMPAS algorithm is proprietary to
Northpointe (now Equivant).
Scale: In Broward County, everyone who is arrested must
be assessed using COMPAS.
Damage: The analysis indicates that African-Americans
are more likely than Caucasians to receive a false positive
for med/high risk of recidivism. This negatively impacts
bail decisions for them.
Verdict: based on the above characteristics, COMPAS arguably is a WMD.

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17. Diagnosing the disparity in false positive rates:
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Images: Nature, "Bias detectives: the researchers striving to make algorithms fair",
20 June 2018

18. Diagnosing the disparity in false positive rates:
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Images: Nature, "Bias detectives: the researchers striving to make algorithms fair",
20 June 2018

19. Diagnosing the disparity in false positive rates:
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Images: Nature, "Bias detectives: the researchers striving to make algorithms fair",
20 June 2018

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22. Outline:
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1. What is big data ethics / data science ethics / AI ethics?
3. Case study: COMPAS predictions of risk of recidivism
4. How do we incorporate ethics into systems development?
2. O’Neil’s analysis of Weapons of Math Destruction (WMDs)

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Pledges: E.g., the

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Guidelines and Principles (resources):

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Regulation

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Algorithmic audits / tools:
http://aequitas.dssg.io/
http://www.oneilrisk.com/

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