AI Bias and Social Justice: Ethical Challenges in Artificial Intelligence Governance

Indian Express - Opinion · 23 Feb 2026 · 2 min read
Prelims · Science and technology Mains · GS2 Governance High relevance

The India AI Impact Summit highlighted systemic biases in AI systems, particularly against marginalized communities, raising critical questions about equity, justice, and power distribution in AI development and deployment.

Key points

AI Bias: Research shows AI systems, including large language models (LLMs), reproduce biases around race, gender, and caste due to training on skewed datasets, leading to discriminatory outcomes in hiring, education, and credit scoring.

Representation vs. Redistribution: Urvashi Aneja of Digital Futures Lab argues that diverse data alone is insufficient; equitable AI requires redistributing power over system design, governance, and profit-sharing to prevent surveillance and exploitation.

Caste Bias in AI: The DECASTE study reveals LLMs associate caste-linked surnames with specific occupations, reflecting historical caste stratification embedded in digital text archives and social media.

Global South Perspective: The film 'Humans in the Loop' illustrates how AI tools trained on Western data fail to represent Indian Adivasi communities, highlighting the need for localized, inclusive AI development.

[GS2-Governance] The lack of regulatory frameworks for AI bias poses governance challenges, requiring policy interventions to ensure accountability and transparency in AI systems.

[GS3-Science and Technology] AI's encoding of social biases mirrors historical technological biases, such as racial disparities in medical devices, emphasizing the need for interdisciplinary solutions.

Way Forward: India should establish ethical AI guidelines mandating diverse training datasets, create regulatory bodies for AI oversight, and promote participatory design involving marginalized communities in AI development processes.

Key terms

Large Language Models (LLMs)

AI systems trained on vast text datasets to generate human-like language. For UPSC, their significance lies in their potential to perpetuate social biases at scale, impacting governance (GS2) and social justice (GS1).

Digital Futures Lab

A research organization focusing on equitable technology governance. Relevant for UPSC as it addresses intersectional issues of AI, power, and marginalization, linking GS3 (technology) with GS2 (governance).

DECASTE Study

Research exposing caste biases in AI systems. Important for UPSC as it highlights how technology can reinforce historical inequalities, connecting GS1 (society) with GS3 (technology policy).

Adivasi Representation in AI

The underrepresentation of tribal communities in AI datasets. Crucial for UPSC as it reflects broader issues of digital inclusion and social justice under GS2 (governance) and GS1 (society).