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1
Introduction
2
Motivation
3
Background
4
Gender Classification
5
Benchmarks
6
Labeling
7
Benchmark Limitations
8
Overall Accuracy
9
Accuracy by Gender
10
Accuracy on Skin Type
11
Intersectional Evaluation for Gender Classification
12
Microsoft
13
Face Plus
14
IBM
15
Companies Response
16
Microsoft Response
17
IBM Response
18
Key takeaways
19
Intersectionality matters
20
The dangers of supremely white data
21
How is this technology used
22
Is this a good thing
23
How this technology is used
24
Quick question
25
Question
26
Question Fitzpatrick
27
Conclusion
Description:
Watch a thought-provoking conference talk from FAT* 2018 where Joy Buolamwini presents groundbreaking research on intersectional accuracy disparities in commercial gender classification systems. Explore the motivations behind the study, understand the benchmarking process, and delve into the evaluation of gender classification accuracy across different skin types and genders. Learn about the responses from major tech companies like Microsoft, Face Plus, and IBM to the findings. Gain insights into the importance of intersectionality in AI, the dangers of biased data, and the ethical implications of using such technology. Engage with key takeaways and participate in a Q&A session addressing critical questions about the Fitzpatrick scale and the broader implications of this research for the field of artificial intelligence and society at large.

Gender Shades - Intersectional Accuracy Disparities in Commercial Gender Classification

Association for Computing Machinery (ACM)
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