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Reading List
We are compiling this list for anyone who wants to learn more about the case for why and how we can make AI systems more inclusive. For a more comprehensive list of the work we came across, learned from, or bookmarked, go here.
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Machine Bias: An investigative piece by ProPublica on the racism and the use of algorithms for criminal sentencing decisions
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An Examination of Hiring Algorithms, Equity, and Bias: Report by Upturn on bias in automated hiring.
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'The Algorithms aren't Biased, We Are': An important biased that at the end of the day, this is a human problem - machines don't work on their own.
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AI Myths: A website busting common misconceptions about AI and bias.
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Against Black Inclusion in Facial Recognition: provides another perspective on advocating for greater representation in tech - what if it's a technology we don't want to be used at all?
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Norms for Beneficial A.I.: A Computational Analysis of the Societal Value Alignment Problem
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TED talk - Machine intelligence makes human morals more important: why we "cannot outsource our responsibilities to machines," and must "hold on ever tighter to human values and human ethics."
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OpenAI’s GPT is a Recruiter’s Dream Tool. Tests Show There’s Racial Bias by Bloomberg
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Want AI to Write Your Resume? Here’s What You Should Keep in Mind by Mozilla
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Ethical OS Toolkit - refer to page 43 for Risk Zone 4: Machine Ethics & Algorithmic Bias
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Big Brother's Blind Spot: highlights the importance of being aware of the nuance associated with bias and inclusion.
Feel free to reach out at survivalofthebestfit@gmail.com or tweet @sotbf_ with any thoughts