Algorithmic Fairness
Algorithmic fairness is a field of study and practice focused on ensuring that algorithms, particularly those used in machine learning and artificial intelligence, do not produce discriminatory or biased outcomes against individuals or groups based on protected attributes like race, gender, or age. It involves developing techniques to detect, measure, and mitigate biases in data, models, and decision-making processes. The goal is to promote equitable and just outcomes in automated systems across domains such as hiring, lending, criminal justice, and healthcare.
Developers should learn algorithmic fairness when building or deploying AI/ML systems that impact people's lives, as biased algorithms can perpetuate societal inequalities, lead to legal and ethical violations, and damage organizational reputation. It is critical in high-stakes applications like credit scoring, job recruitment, and predictive policing to ensure compliance with anti-discrimination laws and foster trust. Understanding fairness helps in designing inclusive technologies that align with ethical AI principles and regulatory frameworks like the EU AI Act.