Unveiling Fairness: A Quest for Ethical AI and Bias Mitigation

Year : 2023 | Volume : 01 | Issue : 02 | Page : 32-35

    Ushaa Eswaran

  1. Vivek Eswaran

  2. Keerthna Murali

  3. Vishal Eswaran

  1. Principal and Professor, Department of ECE, Indira Institute of Technology and Sciences, Markapur, Andhra Pradesh, India
  2. Senior Software Engineer, Tech Lead at Medallia, Austin, Texas, United States
  3. Cybersecurity, Site Reliability Engineer II (SRE) at Dell EMC | CKAD |, AWS CSAA, United States
  4. Senior Data Engineer, CVS Health Centre, Dallas, Texas, United States


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: Algorithmic bias, AI ethics, mitigation techniques, model transparency, regulation

[This article belongs to International Journal of Information Security Engineering(ijise)]

How to cite this article: Ushaa Eswaran, Vivek Eswaran, Keerthna Murali, Vishal Eswaran Unveiling Fairness: A Quest for Ethical AI and Bias Mitigation ijise 2023; 01:32-35
How to cite this URL: Ushaa Eswaran, Vivek Eswaran, Keerthna Murali, Vishal Eswaran Unveiling Fairness: A Quest for Ethical AI and Bias Mitigation ijise 2023 {cited 2023 Dec 06};01:32-35. Available from: https://journals.stmjournals.com/ijise/article=2023/view=0

var fieldValue = “[user_role]”;
if (fieldValue == ‘indexingbodies’) {
document.write(‘Full Text PDF‘);
else if (fieldValue == ‘administrator’) { document.write(‘Full Text PDF‘); }
else if (fieldValue == ‘ijise’) { document.write(‘Full Text PDF‘); }
else { document.write(‘ ‘); }


  1. Floridi L, Cowls J, Beltrametti M, Chatila R, Chazerand P, Dignum V, Luetge C, Madelin R, Pagallo U, Rossi F, Schafer B. An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Ethics, governance, and policies in artificial intelligence. 2021:19-39.
  2. Friedman B, Nissenbaum H. Bias in computer systems. ACM Transactions on information systems (TOIS). 1996 Jul 1;14(3):330-47.
  3. Ray V, Purifoy D. The colorblind organization. InRace, organizations, and the organizing process 2019 May 20 (pp. 131-150). Emerald Publishing Limited.
  4. Jobin A, Ienca M, Vayena E. The global landscape of AI ethics guidelines. Nature machine intelligence. 2019 Sep;1(9):389-99.
  5. Mittelstadt B. Principles alone cannot guarantee ethical AI. Nature machine intelligence. 2019 Nov;1(11):501-7.
  6. 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. InProceedings of the 2020 conference on fairness, accountability, and transparency 2020 Jan 27 (pp. 33-44).
  7. 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. Proceedings of the ACM on human-computer interaction. 2019 Nov 7;3(CSCW):1-30.
  8. 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 research. 2018 Jan 4;46(D1):D836-42.
  9. Suresh H, Guttag J. A framework for understanding sources of harm throughout the machine learning life cycle. InEquity and access in algorithms, mechanisms, and optimization 2021 Oct 5 (pp. 1-9).
  10. Verma S, Rubin J. Fairness definitions explained. InProceedings of the international workshop on software fairness 2018 May 29 (pp. 1-7).
  11. Voigt P, Von dem Bussche A. The eu general data protection regulation (gdpr). A Practical Guide, 1st Ed., Cham: Springer International Publishing. 2017 Aug 10;10(3152676):10-5555.

Regular Issue Subscription Review Article
Volume 01
Issue 02
Received November 23, 2023
Accepted November 29, 2023
Published December 6, 2023

function myFunction2() {
var x = document.getElementById(“browsefigure”);
if (x.style.display === “block”) {
x.style.display = “none”;
else { x.style.display = “Block”; }
document.querySelector(“.prevBtn”).addEventListener(“click”, () => {
document.querySelector(“.nextBtn”).addEventListener(“click”, () => {
var slideIndex = 1;
function changeSlides(n) {
showSlides((slideIndex += n));
function currentSlide(n) {
showSlides((slideIndex = n));
function showSlides(n) {
var i;
var slides = document.getElementsByClassName(“Slide”);
var dots = document.getElementsByClassName(“Navdot”);
if (n > slides.length) { slideIndex = 1; }
if (n (item.style.display = “none”));
item => (item.className = item.className.replace(” selected”, “”))
slides[slideIndex – 1].style.display = “block”;
dots[slideIndex – 1].className += ” selected”;