Black Loans Matter: fighting bias for AI fairness in lending

A detailed summary of research by Mark Weber, Mikhail Yurochkin, Sherif Botros and Vanio Markov, breaking down the lack of racial justice in the current US financial system which leads to the loss of the right to financial security, along with examination of solutions.

Evaluating neural toxic degeneration in language models

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This paper highlights how language models used to automatically generate text produce toxic, offensive and potentially harmful language. They describe various techniques that can be employed to avoid or limit this, but demonstrate that no current method is failsafe in preventing this entirely.

Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification

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According to this paper researchers from MIT and Stanford University, three commercially released facial-analysis programs from major technology companies demonstrate both skin-type and gender biases, The three programs’ error rates in determining the gender of light-skinned men were never worse than 0.8 percent. For darker-skinned women, however, the error rates ballooned — to more than […]

When your resume is (not) turning you down: Modelling ethnic bias in resume screening

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CVs are worldwide one of the most frequently used screening tools. CV screening is also the first hurdle applicants typically face when they apply for a job. They seem particularly vulnerable to hiring discrimination. Despite of decades of legislation on equality and HR professionals’ commitment to equal opportunities – ethnic minority applicants are still at […]

Racial disparities in automated speech recognition

a graph showing results for 5 ASR systems when used by black and white Americans

Analysis of five state-of-the-art automated speech recognition (ASR) systems—developed by Amazon, Apple, Google, IBM, and Microsoft—to transcribe structured interviews conducted with white and black speakers. Researchers found that all five ASR systems exhibited substantial racial disparities and highlight these disparities may actively harm African American communities. For example, when speech recognition software is used by […]