Privacy: The Last Stand for Fair Algorithms (Revisited)Data Rights for All

Katharine Jarmul - KIProtectQCon AI 2019

Left Photo: http://peterfleischer.blogspot.com

Peter Fleischer,

Google’s Global Privacy Counsel and longest serving privacy officer

Left Photo: http://peterfleischer.blogspot.com Right Photo: YouTube screengrab found via Google Search

Photo: YouTube screengrab found via Google Search

On the Right to be Forgotten

“... all of my empathy for wanting to let people edit-out some of the bad things of their past doesn't change my conviction that history should be remembered, not forgotten, even if it's painful. Culture is memory.”

Photo: YouTube screengrab found via Google Search Article: https://www.theguardian.com/technology/2015/jul/14/google-accidentally-reveals-right-to-be-forgotten-requests

On the Right to be Forgotten (Factual Edition from The Guardian)

95% of Google privacy requests are from citizens out to protect personal and private information – not criminals, politicians and public figures

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Privacy as Privilege

Loyalty Program

Your Name Here2342 432 4322432

Privacy as Power

Privacy as Security

Privacy as Privilege,PowerAnd Security

Source: https://www.sfchronicle.com/archive/item/A-decade-of-homelessness-Thousands-in-S-F-30431.php

Privacy and Fairness

Fairness Through Awareness

Dwork et al. Fairness Through Awareness, 2011.

Learning Fair RepresentationsMinimum Discrimination Max Delta (Accuracy - Discrimination)

Zemel et al. Learning Fair Representations, 2013

Implement Private and Fair Machine Learning

Source: https://blog.godatadriven.com/fairness-in-ml

Collect Data with Privacy Guarantees: Group Fairness, Individual Privacy

Source: https://www.nytimes.com/2017/09/08/technology/google-salaries-gender-disparity.html

Privacy By Design: Protect User Data in Software Design

Privacy as a Right:

EnablePrivacyFor All Users

Choose Fairness and Privacy Metrics Early and Often...

...But Please Avoid the Fallacy of Metrics

Photo: BoingBoing

Give Users:

- Agency in Defining Their Privacy

- Transparency in Defining Fairness

Photo: danah boyd’s Medium

Privacy and Privilege,

Revisited

Source: Tim Evanson (Flickr)

Thank you!

7scientists GmbHKIProtect

Bismarckstr. 10-1210625 Berlin

Questions? I’d love to hear them!

Or reach out anytime:

info@kiprotect.com@KIProtect (Twitter)https://github.com/kiprotect

Katharine Jarmulkatharine@kiprotect.com @kjam (Twitter)

Appendix: Private and Fair Representations

1. the mapping from X0 to Z satisfies statistical parity;

2. the mapping to Z-space retains information in X (except for membership in the protected set);

3. the induced mapping from X to Y (by first mapping each x probabilistically to Z-space, and then mapping Z to Y ) is close to f.

Zemel et al. Learning Fair Representations, 2013

Slide References- Right to be Forgotten Request Data Leak:

https://www.theguardian.com/technology/2015/jul/14/google-accidentally-reveals-right-to-be-forgotten-requests- Facebook IQ: https://www.facebook.com/business/products/ads/ad-targeting/ - Siri: https://www.apple.com/siri/ - Dwork et al., Fairness Through Awareness https://arxiv.org/abs/1104.3913 - Zemel et al., Learning Fair Representations http://www.cs.toronto.edu/~zemel/documents/fair-icml-final.pdf - GoDataDriven - Fairness in ML: https://blog.godatadriven.com/fairness-in-ml - Google Salary Chart: https://www.nytimes.com/2017/09/08/technology/google-salaries-gender-disparity.html - Green Party Privacy Poster: https://www.gruene-cochemzell.de/page/37/ - Fairness Metrics: https://algorithmicfairness.wordpress.com - BoingBoing Project Maven: https://boingboing.net/2018/05/15/drone-be-evil.html- Tim Evanson (Flikr): https://www.flickr.com/photos/timevanson/6974619755 - Microsoft Predictive Policing:

https://enterprise.microsoft.com/en-us/industries/government/predictive-policing-the-future-of-law-enforcement/