An offline copy of this guide. Interactive components, such as the explorable, are left out. But it has been nicely formatted for you to print on sheets of dead tree matter.
Download the offline guide
Other Websites and Guides
Useful or interesting links related to algorithmic bias.
Organizations and Conferences
- The AI Now Institute is working actively on AI ethics and has many great publications
- FAT ML and ACM FAT* are two of the main conferences in AI ethics - check out the conference websites for related publications
Datasets for the more bias-aware.
- Gapminder’s Dollar Street images, which was used by DeVries et al. in Does Object Recognition Work for Everyone? and comprises over 16,000 images from 60 different countries across 138 categories - a downloadable set can be found via my GitHub repository
- Google’s Open Images Extended - Crowdsourced, - Google has also provided some notes on possible biases in this dataset
- Joy Buolamwini’s Gender Shades dataset can be requested here
Tools for diagnosing and mitigating algorithmic bias, complete with detailed tutorials.
Academic publications related to algorithmic bias that I found useful.
- Do Artifacts have Politics? (Winner, 1980)
- Bias in Computer Systems (Friedman and Nissenbaum, 1996)
- Technologies of Humility (Jasanoff, 2007)
- Big Data’s Disparate Impact (Barocas and Selbst, 2016)
- Inherent Trade-offs in the Fair Determination of Risk Scores (Kleinberg et al., 2016)
- Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment (Barabas et al., 2017)
- Fairness Definitions Explained (Verma et al., 2018)
- Fairness and Abstraction in Sociotechnical Systems (Selbst et al., 2019)
- A Framework for Understanding Unintended Consequences of Machine Learning (Suresh and Guttag, 2019)