Eliminating Algorithmic Bias Is Just the Beginning of Equitable AI
Por um escritor misterioso
Last updated 21 fevereiro 2025
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When it comes to artificial intelligence and inequality, algorithmic bias rightly receives a lot of attention. But it’s just one way that AI can lead to inequitable outcomes. To truly create equitable AI, we need to consider three forces through which it might make society more or less equal: technological forces, supply-side forces, and demand-side forces. The last of these is particularly underemphasized. The use of AI in a product can change how much customers value it — for example, patients who put less stock in an algorithmic diagnosis — which in turn can affect how that product is used and how those working alongside it are compensated.
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Biased Algorithms Exacerbate Racial Inequality in Health Care
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Bias In Machine Learning: Concepts, Causes, And How To Fix It
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How AI developers can assure algorithmic fairness
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Bias in AI is spreading and it's time to fix the problem
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Dealing With Bias in Artificial Intelligence - The New York Times
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Towards Equitable AI in HR: Designing a Fair, Reliable, and
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Navigating Bias and Fairness Challenges in AI/ML Development
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A Call to Action on Assessing and Mitigating Bias in Artificial
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The Age of AI has begun
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Addressing bias in health care
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Navigating Bias and Fairness Challenges in AI/ML Development
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