Automated decision-making tools are becoming increasingly ubiquitous in our world. However, many of the machine learning (ML) models behind them — from facial recognition systems to online advertisements — show clear evidence of racial and gender biases. As ML models become more widely adopted, special care and expertise are needed to ensure that artificial intelligence (AI) improves the bottom line fairly.

ML models should target and eliminate biases rather than exacerbate discrimination. But in order to build fair AI models, we must first build better methods to identify the root causes of bias in AI. We must understand how a biased AI model learns a biased relationship between its inputs and outputs.

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Značky: #Rozne, #Analytics