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Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions made by such models after a learning process may be considered unfair if they were based on variables considered sensitive (e.g., gender, ethnicity, sexual orientation, or disability).
Racial disparities in high school completion are a prominent reason for racial imbalances in STEM fields. While only 1.8% of Asian and 4.1% of White students drop out of high school, 5.6% of Black, 7.7% of Hispanic, 8.0% of Pacific Islander, and 9.6% of American Indian/Alaskan Native students drop out of high school. [6]
An ambitious new AI project has begun to take shape in Europe, with the aim of developing open-source AI models that support the region’s 24 official languages and more—while also complying as ...
Ruha Benjamin and her book, Race After Technology at the 2019 Black in AI event. Black in AI, formally called the Black in AI Workshop, is a technology research organization and affinity group, founded by computer scientists Timnit Gebru and Rediet Abebe in 2017. [1] [2] [3] It started as a conference workshop, later pivoting into an organization.
The problem of bias in machine learning is likely to become more significant as the technology spreads to critical areas like medicine and law, and as more people without a deep technical understanding are tasked with deploying it. [36] Some open-sourced tools are looking to bring more awareness to AI biases. [37]
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
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