Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 

Fair ML Reading Group 2021

🌏 Melbourne, Australia
🖥 Hosted remotely
🤖 Facilitated by Laura
📬 Listserv at groups.io/g/fair-ml
🗓 Google calendar

Description

A multi-disciplinary group reading papers on the topic of fairness and ethics in Machine Learning and Data Science.

2020 papers — https://github.com/summerscope/fair-ml-reading-group/blob/master/2020.md

2019 papers — https://github.com/summerscope/fair-ml-reading-group/blob/master/2019.md


Reading history

March 3
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
http://faculty.washington.edu/ebender/papers/Stochastic_Parrots.pdf

March 18
"This Whole Thing Smacks of Gender": Algorithmic Exclusion in Bioimpedance-based Body Composition Analysis
https://arxiv.org/abs/2101.08325

March 31
PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
https://www.researchgate.net/publication/330070686_PROBAST_A_Tool_to_Assess_the_Risk_of_Bias_and_Applicability_of_Prediction_Model_Studies

PROBAST in use
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
https://www.nature.com/articles/s42256-021-00307-0

April 15
A Unified Approach to Interpreting Model Predictions (SHAP paper) https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html
https://proceedings.neurips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf

April 28
Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?
https://arxiv.org/abs/1812.05239

May 13
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
https://arxiv.org/abs/2010.07487

May 26
Explaining Machine Learning Predictions: State-of-the-art, Challenges, and Opportunities
https://explainml-tutorial.github.io/aaai21

Bonus Paper
The Myth of Complete AI-Fairness
https://arxiv.org/abs/2104.12544

June 10
Fair Bayesian Optimization
https://arxiv.org/abs/2006.05109

June 23
Ethical considerations in multilabel text classifications
https://smartygrants.com.au/research/ethical-considerations-in-multilabel-text-classifications

July 8
It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks
https://arxiv.org/abs/2106.05498

July 21
Artificial Intelligence and the Purpose of Social Systems
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3850456

August 5
Leave-one-out Unfairness
https://arxiv.org/abs/2107.10171v1

About

Reading history for Fair ML Reading Group in Melbourne

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published