Ethical Machine Learning Taking Dont be Evil Literally Katharine - PowerPoint PPT Presentation

Nov 30, 2022 •275 likes •550 views

Ethical Machine Learning Taking Dont be Evil Literally Katharine Jarmul #QCONSP Kjamistan.com I Cant Breathe: The Killing of Eric Garner Joe Raedle/Getty Images Broken Windows Policing Disparate Impact Pr(C = YES|X = 0)

Ethical Machine Learning Taking “Don’t be Evil” Literally Katharine Jarmul #QCONSP Kjamistan.com
I Can’t Breathe: The Killing of Eric Garner Joe Raedle/Getty Images
“Broken Windows” Policing
Disparate Impact Pr(C = YES|X = 0) ≤ τ = 0.8 Pr(C = YES|X = 1)
Predictive Policing: Runaway Feedback Loops Ensign et al, 2017
If our models mimic current police behavior, are we creating a valid model?
If our models mimic social inequalities and prejudice, are we creating a valid model?
Are social inequalities and prejudice valid?
Breaking the Cycle: Determining if Your Data has Prejudice
FairTest: Evaluating Correlations to Sensitive Attributes
GenderShades: Creating Better Datasets GenderShades.org
NLP: Looking at Word Vector Correlations
NLP: Google News Vectors https://blog.kjamistan.com/embedded-isms-in-vector-based-natural-language-processing/
Debiasing Word Vectors https://github.com/tolga-b/debiaswe (Bolukbasi, Chang, Zou, Saligrama and Kalai, 2016)
Modeling Fairness: Evaluating Models for Prejudice
Defining Fair https://algorithmicfairness.wordpress.com/
Evaluating Fair https://blog.godatadriven.com/fairness-in-ml/
NLP: Testing Bias https://developers.googleblog.com/2018/04/text-embedding-models-contain-bias.html
Interpreting Our Models Show, Attend and Tell: Neural Image Caption Generation with Visual Attention Xu et al., 2016
Radical Transparency: Promoting Conversation & Accountability
Talking Fair https://www.fatml.org/
Acting Fair: Building Accountable Applications https://2017.ind.ie/ethical-design/
Ethical Machine Learning: Taking a Logical Stance against Oppression
Ethical ML Takeaways - Doing “nothing” assumes prejudice and unfair treatment is a valid action - We need better data - Diverse data which better reflects the real world - Stop using datasets which are non-representative - We need built-in ethics-driven evaluation criteria - Scikit-learn disparate impact? - Scikit-learn equal odds / opportunity? - You can contribute - open-source your work and datasets - volunteer with the Algorithmic Justice League or local organization
Thanks! Questions? - Now? - Later? - @kjam - katharine@kiprotect.com

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