Author(s): Rohan kumar Jawarkar
Algorithmic decision-making has gained widespread acceptance as an innovative way to addressing the claimed cognitive and perceptual constraints of human decision-makers by offering "objective" data-driven recommendations. Despite this, numerous incidents of algorithmic prejudice continue to emerge when firms deploy Algorithmic Decision-Making Systems (ADMS). In domains such as health, hiring, criminology, and education, harmful biases have been discovered in algorithmic decision-making systems, generating growing social concern about the influence these systems are having on people's well-being and livelihood. As a result, algorithmic fairness strategies try to figure out how ADMS treat different people and groups, with the goal of detecting and correcting detrimental biases.