Algorithmic Fairness and Bias in Machine Learning Systems

¹ Assistant Professor, Symbiosis Law School, Nagpur, Symbiosis International (Deemed University), Pune, India and (secondary affiliation of first author) Research Scholar, Gujarat National Law University, Gandhinagar, India
² Assistant Professor, Symbiosis Law School, Nagpur, Symbiosis International (Deemed University), Pune, India and (Secondary affiliation of 2nd author) Research Scholar, VIT School of Law, Vellore Institute of Technology, Chennai, India
³ Assistant Professor, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai – 127
⁴ College of pharmacy, The Islamic university, Najaf, Iraq
⁵ Tashkent State Pedagogical University, Tashkent, Uzbekistan
⁶ Assistant professor, Department of mechanical Engineering, K. Ramakrishnan college of technology, Tiruchirappalli

^* Corresponding Author rushilchandra@slsnagpur.edu.in
karunsanjaya@slsnagpur.edu
aravind.a.r_ece@psvpec.in
keybin.st@gmail.com
senthilk.kumar6@gmail.com

Abstract

In recent years, research into and concern over algorithmic fairness and bias in machine learning systems has grown significantly. It is vital to make sure that these systems are fair, impartial, and do not support discrimination or social injustices since machine learning algorithms are becoming more and more prevalent in decision-making processes across a variety of disciplines. This abstract gives a general explanation of the idea of algorithmic fairness, the difficulties posed by bias in machine learning systems, and different solutions to these problems. Algorithmic bias and fairness in machine learning systems are crucial issues in this regard that demand the attention of academics, practitioners, and policymakers. Building fair and unbiased machine learning systems that uphold equality and prevent discrimination requires addressing biases in training data, creating fairness-aware algorithms, encouraging transparency and interpretability, and encouraging diversity and inclusivity.