Bias in machine learning and AI refers to the presence of systematic errors that can lead to unfair outcomes. It can arise from various sources, such as training data, flawed algorithms, or skewed assumptions mode during the model development process.
Bias failures can have significant negative impacts ranging from misrepresentations in search engine results and facial recognition systems misidentifying individuals to unfair hiring practices and lending decisions.
For these reasons, mitigating bias is crucial to ensure that AI systems are fair, transparent, and ethical, promoting inclusivity and accuracy in their applications. This involves implementing rigorous testing, using diverse and representative data sets, and continuously monitoring AI systems for biased outcomes.
A significant real-world example of bias in AI is Amazon’s hiring algorithm. In 2018, it was discovered that this AI tool, designed to automate hiring, exhibited gender bias. Trained on resumed from a ten-year period, predominantly from male applicants, the AI favored male candidates and pearlized resumes mentioning “women” or associated with female-dominated activities.