Abstract
Algorithmic decision-making has significantly transformed various industries, including hiring and healthcare. However, the use of machine learning and artificial intelligence (AI) models raises concerns about reinforcing existing societal biases. Hiring discrimination, employment bias, and violations of rights may stem from algorithmic bias based on past data marked by social imbalance. This essay compares the anti-discrimination and employment protection systems of the European Union (EU) and India. The Indian system is haphazard and based on constitutional safeguards and general labor law with no specific AI-oriented rules, while the EU has implemented a systematic body of law in the form of the General Data Protection Regulation (GDPR) and the upcoming Artificial Intelligence Act (AIA).The EU legal framework is prophylactic by incorporating principles of equity into law that directly and indirectly addresses discrimination. Algorithmic decision-making needs to be made transparent, and individuals can object to and require an explanation of automated decisions under the GDPR. Hire platforms based on AI and other high-risk application scenarios are subjected to strict requirements under the AI Act, and AI systems are classified as per risk factors.
However, problems remain, such as inadequate enforcement tools, making it difficult to prove prejudice, and inaccuracy when fighting algorithmic discrimination. India has no law specifically aimed at eliminating algorithmic prejudice, and its proposed Digital Personal Data Protection Bill (DPDPB) and National AI Strategy attempt to regulate data harvesting and moral AI but do not have obligatory enforcement authority and tangible safeguards against AI bias within the workplace. This study provides policy suggestions for both regimes to correct these imbalances. For the EU, harmonizing the GDPR with the Platform Work Directive, enhancing enforcement capabilities, and expanding rules to include under regulated sectors are necessary. India must create AI-specific labor law, sectoral privacy controls, dispute resolution guidelines, and encourage judicial education on the influence of AI on social justice and workers’ rights. An equitable and inclusive AI-driven work culture can be created in both countries by encouraging cooperation between companies, legislators, and AI developers.
Introduction
Algorithmic decision-making has revolutionized multiple industries, ranging from employment and finance to criminal justice. With the increasing reliance on artificial intelligence (AI) and machine learning (ML) models to automate and enhance decision-making, these technologies have become indispensable tools for efficiency and precision. However, as AI permeates critical societal functions, concerns about its potential to reinforce and perpetuate existing social biases have also gained prominence. The ability of algorithms to inadvertently replicate discrimination present in historical data sets, or even exacerbate such biases, has given rise to significant ethical, legal, and regulatory challenges. This issue is particularly acute in the employment sector, where algorithmic bias can result in unfair hiring practices, workplace discrimination, and violations of fundamental rights.
This paper aims to provide a comparative analysis of how the European Union (EU) and India address algorithmic bias within their respective anti-discrimination and employment protection laws. Both jurisdictions have established regulatory frameworks to ensure fair and non-discriminatory labor practices, but they differ significantly in their approaches to AI governance and bias mitigation. The EU, with its proactive legislative stance, has introduced comprehensive legal measures such as the General Data Protection Regulation (GDPR) and the proposed Artificial Intelligence Act (AIA), which specifically address the risks associated with AI-driven decision-making. In contrast, India’s approach is still evolving, with its legal framework primarily relying on broader constitutional guarantees of equality and anti-discrimination provisions under labor laws, without explicit AI-specific regulations.
Given the far-reaching implications of AI in employment, a critical assessment of the strengths and weaknesses of the regulatory frameworks in these two jurisdictions is necessary. By analyzing their approaches, this study seeks to identify areas where regulations need to be sharpened to prevent fundamental rights violations and ensure the equitable application of AI in employment decisions. In doing so, this paper also considers the ethical and legal responsibilities of employers, technology developers, and regulators in mitigating algorithmic bias.
A key concern in AI-driven employment decisions is the extent to which automated systems replicate systemic biases. AI models are trained on vast amounts of historical data, which often reflect social inequalities and prejudices. Without adequate oversight, these biases become embedded in hiring algorithms, leading to discriminatory outcomes against marginalized groups based on gender, caste, race, or disability. For example, past studies have demonstrated that AI-driven hiring systems used by major corporations inadvertently disadvantaged female applicants due to historical male dominance in certain industries. Such instances underscore the need for robust legal frameworks to ensure transparency, accountability, and fairness in algorithmic decision-making.
In the context of the EU, the existing legal instruments offer relatively strong protections against algorithmic bias through data protection laws, stringent anti-discrimination mandates, and employer obligations for fairness in automated hiring processes. India, on the other hand, lacks explicit AI-specific regulations, making it imperative to explore how existing labor laws and constitutional principles can be leveraged to address this challenge.
By comparing the legal approaches of the EU and India, this paper aims to highlight best practices, regulatory gaps, and potential reforms that can contribute to the development of a fair and equitable AI-driven employment landscape. The findings will help inform policymakers, businesses, and AI developers about the necessary legal safeguards to prevent algorithmic discrimination and uphold fundamental rights.