Fairness in AI
Fairness in AI is a subfield of AI ethics focused on ensuring that artificial intelligence systems do not produce discriminatory or biased outcomes against individuals or groups based on protected attributes like race, gender, or age. It involves identifying, measuring, and mitigating biases in data, algorithms, and model predictions to promote equitable treatment. This concept is critical for building trustworthy and socially responsible AI applications.
Developers should learn about fairness in AI when building systems that impact people's lives, such as in hiring, lending, healthcare, or criminal justice, to avoid perpetuating societal inequalities and ensure legal compliance with anti-discrimination laws. It is essential for mitigating risks like reputational damage, regulatory penalties, and unfair outcomes, and is increasingly required in industries deploying high-stakes AI models.