Model fairness
Model fairness refers to the concept of ensuring that Machine learning models and Artificial intelligence systems are free from bias and discrimination, particularly with regards to sensitive attributes such as race, gender, and age. This is crucial in applications where models are used to make decisions that affect people's lives, such as in Hiring, Lending, and Law enforcement. Data scientists and Researchers use various techniques, including Data preprocessing and Algorithmic auditing, to detect and mitigate bias in models. Regulatory bodies and Ethics committees also play a role in promoting model fairness by establishing guidelines and standards for the development and deployment of AI systems. Ensuring model fairness is essential for maintaining Trust and Accountability in the use of Technology.