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A growing body of work shows that many problems in fairness, accountability, transparency, and ethics in machine learning systems are rooted in decisions surrounding the data collection and annotation process.
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Code of Ethics
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GPT-2 Model Card
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Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19) . ACM, New York, NY, USA, Article 600, 16 pages
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Facial Recognition’s ‘Dirty Little Secret’: Millions of Online Photos Scraped without Consent
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A Framework for Understanding Unintended Consequences of Machine Learning
Harini Suresh and John V. Guttag. 2019 · 2019
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