STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Information Security Engineering Review Article

Unveiling Fairness: A Quest for Ethical Artificial Intelligence and Bias Mitigation

  1. Ushaa Eswaran Department of ECE, Indira Institute of Technology and Sciences, Markapur
  2. Vivek Eswaran Tech Lead at Medallia, Austin
  3. Keerthna Murali Site Reliability Engineer II (SRE) at Dell EMC | CKAD |
  4. Vishal Eswaran CVS Health Centre, Dallas

Abstract

Artificial intelligence (AI) systems have become ubiquitous across areas like finance, healthcare, employment, and criminal justice. However, they suffer from issues of unfair bias, lack of transparency, and broad ethical implications impacting vulnerable societal groups disproportionately. This paper reviews key challenges around AI ethics and bias while proposing data-driven guidelines mitigating such algorithmic harms through rigorous statistical testing, predictive modeling ensembles adjusting distortion vectors and AI audits by domain experts analyzing source codes, training data curation and model card documentations ensuring responsible development. A tiered regulatory framework is envisioned spanning self-regulation, external audits, professional codes of ethics, and government oversight balancing innovation impacts with public safeguards.

Keywords

References (1)

  1. Smith CL, Blake JA, Kadin JA, Richardson JE, Bult CJ, Mouse Genome Database Group. Mouse Genome Database (MGD) – 2018: knowledgebase for the laboratory mouse. Nucleic Acids Res. 2018; 46 (D1): D836–D842. Ray V, Purifoy D. The colorblind organization. In: Wooten ME, editor. Race, Organizations, and the Organizing Process. Leeds, UK: Emerald Publishing; 2019. pp. 131–150. Voigt P, Von dem Bussche A. The EU General Data Protection Regulation (GDPR). A Practical Guide. 1st edition. Cham, Switzerland: Springer International Publishing; 2017. Jobin A, Ienca M, Vayena E. The global landscape of AI ethics guidelines. Nat Mach Intell. 2019; 1 (9): 389–399. Friedman B, Nissenbaum H. Bias in computer systems. ACM Trans Inform Syst. 1996; 14 (3): 330–347. Suresh H, Guttag J. A framework for understanding sources of harm throughout the machine learning life cycle. In: EAAMO’21: Equity and Access in Algorithms, Mechanisms, and Optimization, New York, NY, USA, October 5–9, 2021. pp. 1–9. Verma S, Rubin J. Fairness definitions explained. In: Proceedings of the International Workshop on Software Fairness, Gothenburg, Sweden, May 29, 2018. pp. 1–7. Raji ID, Smart A, White RN, Mitchell M, Gebru T, Hutchinson B, Smith-Loud J, Theron D, Barnes P. Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, Barcelona, Spain, January 27–30, 2020. pp. 33–44. Floridi L, Cowls J, Beltrametti M, Chatila R, Chazerand P, Dignum V, Luetge C, Madelin R, Pagallo U, Rossi F, Schafer B. AI4People—An ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds Machines. 2018; 28: 689–707. Mittelstadt B. Principles alone cannot guarantee ethical AI. Nat Mach Intell. 2019; 1 (11): 501–507. Ali M, Sapiezynski P, Bogen M, Korolova A, Mislove A, Rieke A. Discrimination through optimization: how Facebook's ad delivery can lead to biased outcomes. Proc ACM Human Computer Interact. 2019; 3 (CSCW): 1–30.