AI systems can perpetuate societal biases by learning from historical or skewed data. Key issues include inheriting societal prejudices, lack of diverse training data, selection bias, developers' implicit biases, confirmation bias in data annotation, socio-economic biases, language and cultural bias, and feedback loops that amplify biases. Moreover, overfitting to outliers and the absence of regulations exacerbate the issue, reinforcing the need for diverse data sets and fair practices in AI development.
Why Is Our AI Biased? The Hidden Influence of Training Data
AI systems can perpetuate societal biases by learning from historical or skewed data. Key issues include inheriting societal prejudices, lack of diverse training data, selection bias, developers' implicit biases, confirmation bias in data annotation, socio-economic biases, language and cultural bias, and feedback loops that amplify biases. Moreover, overfitting to outliers and the absence of regulations exacerbate the issue, reinforcing the need for diverse data sets and fair practices in AI development.
Contribute to three or more articles across any domain to qualify for the Contributor badge. Please check back tomorrow for updates on your progress.