What does the term "bias audits" refer to in the context of AI systems?

Bias audits in the context of AI systems specifically focus on identifying and addressing any discrimination or unfair treatment that may arise from algorithms and their decisions. The primary goal of a bias audit is to evaluate how an AI system's outputs may differ across various demographic groups, ensuring that the technology operates fairly and equitably for all users.

By systematically examining the data and algorithms utilized in AI models, a bias audit can highlight areas where bias exists, potentially leading to discriminatory outcomes in areas such as hiring, lending, or law enforcement. This process is crucial for building trust and accountability in AI systems, as it helps organizations to mitigate risks associated with biased decision-making and align with ethical standards and legal requirements.

The other options, while relevant in broader contexts, do not adequately capture the specific focus of bias audits. For instance, financial analyses relate to the economic impact and viability of AI systems, rather than their ethical implications. Evaluating user opinions does not directly address inherent biases in algorithmic decision-making, nor does measuring system performance against competitors relate to fairness and equality in AI outputs.