Bias-Free AI for Sustainable Global Development: Engineering Intelligent Systems for Ethical Innovation by 2050

DOI

10.2991/978-2-38476-559-1_13How to use a DOI?

Keywords

Algorithmic Bias; Bias-Free AI; Ethical Innovation; Fairness-Oriented Design; Sustainable Development

Abstract

By automating data-driven judgments, artificial intelligence (AI) holds the possibility of bringing revolutionary, cutting-edge solutions to the domains of medicine, education, and financial empowerment. AI also holds promise for social good and global sustainable development. However, biases or skewed data can lead to weaknesses in AI systems, such as computational errors, the use of biased training datasets, and the reflection of past social injustices. Unfortunately, these biases already exist in many other applications, such as recruitment algorithms, predictive policing, and credit scoring, and they harm underprivileged populations. Instead of narrowing socioeconomic differences, this has the effect of strengthening them and occasionally making them worse. The study provides a fair analysis of bias in AI, covering its economic effects as well as the moral ramifications for AI policymakers and AI system developers. A multidisciplinary approach is used to solve the problem of bias in artificial intelligence, integrating the perspectives of computer science, ethics, jurisprudence, and finance. It also paves the way for the development of a paradigm of fairness in AI design, with foundations in fairness-aware and ethical stewardship. Federated learning and differential privacy are state-of-the-art approaches for privacy protection that are discussed in this paper, but which need improved practical deployment. It permits cooperative training of AI systems while maintaining stringent restrictions over sensitive data. The scope of the article encompasses the accountability and transparency of AI systems, and it strongly advocates for policy measures to combat discrimination in algorithmic decision-making systems, including algorithmic auditing, algorithmic fairness-aware model review, and legal tools and regulations. Furthermore, by using future scenario-building and counterfactual analysis—which entails comparing counterfactuals that highlight the risks of algorithmic bias remaining in place with future scenarios where AI is used responsibly to achieve social equality and global sustainable development the paper explores the long-term social implications of AI development that is conscious of bias. In an attempt to meaningfully contribute to the global discourse on ethical AI design and the advancement of fairness in technology, the paper also draws on existing real-world examples, such as those for fairness-oriented hiring algorithm development and fairness-aware AI in credit lending. In order to address algorithmic systems for social justice and actively challenge algorithmic bias in the systems, the study calls for a concerted effort including academics, industry, and government. In conclusion, the paper makes the case that a proactive and urgent action plan for interdisciplinary collaboration in AI governance is required, and that the creation of AI free from bias is a crucial element of 2050 ethical innovation and global sustainability.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Girish Chandra Bhatt
AU  - Manoj Kumar Gopaliya
PY  - 2026
DA  - 2026/04/19
TI  - Bias-Free AI for Sustainable Global Development: Engineering Intelligent Systems for Ethical Innovation by 2050
BT  - Proceedings of the Global Innovation and Technology Summit “AAROHAN 3.0”_HSS track (GITS-HSS 2025)
PB  - Atlantis Press
SP  - 180
EP  - 200
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-559-1_13
DO  - 10.2991/978-2-38476-559-1_13
ID  - Bhatt2026
ER  -