AI's Promise and Pitfalls: Assessing When to Invest and How to Audit for Fairness
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Businesses should carefully evaluate if AI is the best solution for their problems before investing in it, and audit AI systems to avoid unfair outcomes.
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AI excels at providing recommendations and predictions when ample historical data is available, like shopping recommendations. It struggles with unique problems lacking data.
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Relying solely on historical data can propagate existing biases - AI needs auditing like clinical trials to identify discrimination.
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Europe leads in AI regulation but falls behind countries like the US in actually implementing audits and governance to ensure responsible AI use.
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Legally requiring AI audits, using synthetic data to provide more context, and removing unnecessary data filters can help make AI more fair and trustworthy.