A collection of news articles & research papers directed towards Algorithmic Bias. It covers different areas like classification, regression, computer vision, NLP, recommender systems, etc. It is intended to act as a resource for fellow researchers and to spread awareness for curious minds.
Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them The above paper shows that people were more likely to use an algorithm and accept its errors if given the opportunity to modify the algorithm themselves, even if it meant making it perform imperfectly. This paper bolsters the need to have a human-in-the-loop process for decision making.
AI can be sexist and racist — it’s time to make it fair (Nature 2018)
Machine Bias(Propublica 2016)
What the debacle of Amazon’s sexist hiring algorithm reveals about how human bias works
ACM conference on fairness, Accountability & Transperancy - FAT*
Fairness, Accountability, and Transparency in Machine Learning - FAT/ML
Fairness, Accountability and Transparency in Recommender Systems - FATREC
Vision with Biased or Scarce Data - VBSD