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Table Of Contents
Inclusive Design for Accessibility
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Algorithmic bias arises when AI systems produce skewed or unfair outcomes due to prejudiced assumptions embedded within the machine learning processes. These biases often mirror societal inequities present in the training data, leading to discriminatory results that disproportionately affect communities of color as well as other user groups.
An AI-powered speech recognition tool, trained predominantly on data limited to a specific demographic, for example, may underperform for users with speech impairments or those from linguistic backgrounds not reflected in the dataset.
Mitigating bias requires a comprehensive strategy that includes diverse data acquisition, transparent algorithm development, ongoing monitoring, and inclusive testing practices in order to enhance the equity of technology and also foster trust and inclusivity within communities.
AI models trained on biased data can inadvertently...
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