Covers: theory of Algorithmic bias
Estimated time needed to finish: 15 minutes
Questions this item addresses:
- What do we mean when we say that algorithmic systems "biased"?
- Where does the bias in algorithmic systems come from?
How to use this item?
Read and interact with this interactive article to understand the basics of where bias in algorithmic systems comes from
Author(s) / creator(s) / reference(s)
Alice Feng, Shuyan Wu
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Bias in Facial Recognition Algorithms
Contributors
- Objectives
- To understand the social biases embedded in facial recognition technologies: why they arise, how they affect people, and what can be done to address them
- Potential Use Cases
- Facial recognition / processing technologies, image classification algorithms more broadly
- Who is This For ?
- BEGINNER
Click on each of the following annotated items to see details.
ARTICLE 1. The Myth of the Impartial Machine
- What do we mean when we say that algorithmic systems "biased"?
- Where does the bias in algorithmic systems come from?
15 minutes
ARTICLE 2. Facial Recognition Technologies: A Primer
- What are facial recognition technologies?
- How and where are facial recognition technologies used?
- How does a machine recognize an individual face?
- How accurate are facial recognition technologies?
20 minutes
VIDEO 3. Joy Buolamwini: "How I'm fighting bias in algorithms"
- What does bias in facial recognition algorithms look like?
- What can we do about bias in facial recognition algorithms?
8 minutes
PAPER 4. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
- How do we know that facial recognition algorithms can discriminate on the basis of race and gender?
30 minutes
PAPER 5. Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing
- Can we audit facial recognition algorithms for bias?
20 minutes
ARTICLE 6. "Wrongfully Accused by an Algorithm"
- How can facial racial technologies lead to actual harm?
10 minutes
Concepts Covered
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