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Artificial Intelligence and Social Inequality: A Review of Algorithmic Bias, Digital Exclusion, and Governance Responses

Artificial intelligence (AI) is increasingly integrated into employment, education, healthcare, financial services, public administration, and social welfare systems, creating opportunities to improve efficiency, accessibility, and decision-making. However, the growing reliance on AI also raises concerns about social inequality, particularly when algorithmic systems reproduce existing patterns of discrimination, exclude marginalized populations, or concentrate technological benefits among already advantaged grou…

By the Truvace desk2 min read1 sourcePeer-reviewedTRV-2026-2028· cite & verify
Artificial Intelligence and Social Inequality: A Review of Algorithmic Bias, Digital Exclusion, and Governance Responses
Conditions pour IA digne de confiance conditions requirements for Trustworthy AI by Lamiot. CC BY 4.0 · https://creativecommons.org/licenses/by/4.0

In brief

This literature review, published in a peer-reviewed humanities and social sciences journal, examines how AI relates to social inequality. It synthesizes conceptual and empirical work on algorithmic bias, unequal access to digital infrastructure, disparities in AI literacy, and the socioeconomic effects of automated decision-making.

The review matters because AI is already used in employment, education, healthcare, finance, public administration and welfare, so biased or inaccessible systems could shape access to basic services. Its conclusions rest on a synthesis of existing literature and depend on how safeguards are implemented in practice.

Main points

  1. Inequalities tied to gender, race, socioeconomic status, disability, geography and language can influence how AI systems are designed, deployed and what outcomes they produce.
  2. Cited causes of algorithmic discrimination include unrepresentative datasets, historical inequalities in training data, inappropriate performance measures and insufficient institutional accountability.
  3. Digital exclusion stems from inadequate connectivity, limited digital skills, affordability constraints and linguistic barriers.
  4. Regulation, algorithmic audits, inclusive design and human oversight are described as important safeguards.

The problem

AI systems can reproduce discrimination and exclude marginalized groups, because of unrepresentative data, embedded historical inequalities, weak accountability and limited access to connectivity and digital skills.

The rundown

The review frames AI as offering opportunities to improve efficiency, accessibility and decision-making across employment, education, healthcare, financial services, public administration and social welfare, while warning that benefits may concentrate among already advantaged groups.

It identifies several drivers of unequal outcomes: discriminatory data practices, unequal access to digital infrastructure, and disparities in AI literacy. These factors limit how far disadvantaged populations can benefit from AI-enabled services.

On governance, the authors argue that safeguards work only with implementation, transparency and meaningful participation by affected communities. They call for combining technical fairness, digital inclusion, institutional accountability and rights-based governance, and for judging AI by its contribution to social justice and equitable access.

Sources

  1. Peer-reviewedBritish Journal of Humanities and Social Sciences2026-10-19

The debate