AUTOMATED EMPLOYABILITY SCORING SYSTEMS AND LABOUR INSECURITY: LEGAL FRAMEWORK AND JUDICIAL ANALYSIS

1. INTRODUCTION

Automated employability scoring systems are digital technologies that evaluate an individual's suitability, reliability, productivity, professional competence and future employment prospects through algorithmic assessments. Employers, recruitment agencies and digital labour platforms increasingly use artificial intelligence (AI), machine learning, psychometric assessments, biometric information and employment-history databases to generate employability scores.

Although these systems can improve recruitment efficiency and workforce planning, they also create significant labour insecurity. Employees and job applicants may face rejection, reduced working opportunities, lower compensation or dismissal because of automated assessments that are inaccurate, discriminatory or difficult to challenge. The central legal concern is that employment opportunities should not depend exclusively on opaque algorithmic decisions without meaningful human oversight, transparency and procedural safeguards.

2. MEANING AND OPERATION OF AUTOMATED EMPLOYABILITY SCORING

Automated employability scoring involves assigning numerical or categorical ratings to individuals based on information such as:

Educational qualifications and professional experience.

Previous employment and termination records.

Productivity and attendance information.

Communication patterns and behavioural assessments.

Psychometric and personality test results.

Customer feedback and performance ratings.

Biometric and workplace monitoring information.

These scores may determine recruitment eligibility, promotion opportunities, contract renewal, work allocation and continued employment.

For example, a digital employment platform may automatically reduce a worker's employability rating following repeated customer complaints. The reduced rating may prevent the worker from receiving future assignments, even where the complaints are inaccurate or discriminatory.

3. LEGAL FRAMEWORK GOVERNING AUTOMATED EMPLOYABILITY SCORING IN INDIA

A. CONSTITUTIONAL PROTECTIONS

Article 14 of the Constitution of India guarantees equality before the law and equal protection of laws. Arbitrary employment assessments and discriminatory scoring practices may raise equality concerns, particularly in public employment.

Article 16 guarantees equality of opportunity in matters of public employment. Algorithmic recruitment systems must therefore operate consistently with constitutional equality principles.

Article 21 protects life and personal liberty, including the broader protection of dignity and privacy recognised by the Supreme Court.

B. EMPLOYMENT AND LABOUR LEGISLATION

The Industrial Relations Code, 2020, where applicable and brought into force, provides an important framework concerning industrial employment, disciplinary action, retrenchment and industrial disputes.

The Code on Wages, 2019, the Occupational Safety, Health and Working Conditions Code, 2020, and the Social Security Code, 2020, are also relevant to employment conditions and worker protection, subject to their applicable commencement provisions.

The Industrial Employment (Standing Orders) Act, 1946, where still applicable, historically requires defined employment conditions and disciplinary procedures for covered establishments.

C. DATA PROTECTION FRAMEWORK

The Digital Personal Data Protection Act, 2023, establishes obligations concerning the processing of digital personal data, including lawful processing, notice, consent where applicable, security safeguards and individual rights, subject to its provisions and commencement arrangements.

Employers using employability scoring systems must consider whether personal information is collected and processed lawfully and whether appropriate safeguards protect employees against misuse.

4. HOW AUTOMATED SCORING CREATES LABOUR INSECURITY

A. ALGORITHMIC DISCRIMINATION

Scoring systems may reproduce historical discrimination based on gender, disability, age, caste-associated indicators or socioeconomic background. Even apparently neutral variables may operate as indirect proxies for protected or disadvantaged characteristics.

B. EMPLOYMENT OPPORTUNITY DEPRIVATION

Workers receiving low scores may automatically lose access to interviews, promotions, assignments or contract renewals without receiving a meaningful explanation.

C. LACK OF TRANSPARENCY

Proprietary algorithms may prevent employees from understanding how their scores are calculated. This creates difficulties in identifying errors and challenging unfair decisions.

D. DATA INACCURACY

Incorrect employment records, outdated qualifications and mistaken identity matching can produce unfair employability assessments.

E. ECONOMIC DEPENDENCY

Platform workers may become financially dependent on algorithmic ratings. A sudden reduction in their scores can eliminate their principal source of income.

5. RELEVANT CASE LAWS

1. E.P. ROYAPPA v. STATE OF TAMIL NADU (1974)

The Supreme Court explained that arbitrariness is incompatible with equality under Article 14. This principle is relevant where automated employment assessments produce arbitrary outcomes without rational justification.

Authority: Direct constitutional equality principle; analogous application to algorithmic scoring.

2. MANEKA GANDHI v. UNION OF INDIA (1978)

The Supreme Court established that State action affecting individual liberty must satisfy fairness, reasonableness and non-arbitrariness. Automated employment decisions involving public authorities should similarly comply with procedural fairness.

Authority: Direct procedural fairness principle; analogous employment application.

3. K.S. PUTTASWAMY v. UNION OF INDIA (2017)

The Supreme Court recognised privacy as a fundamental right under Article 21. The judgment is particularly relevant when employers collect biometric, behavioural, psychological or personal information for employability assessments.

Authority: Direct privacy authority; analogous application to workplace algorithmic profiling.

4. INDIRA SAWHNEY v. UNION OF INDIA (1992)

The Supreme Court examined equality and affirmative action in public employment. Its reasoning highlights the importance of preventing employment selection mechanisms from undermining constitutionally protected equality principles.

Authority: Direct public employment equality authority; analogous application to algorithmic recruitment.

5. AIR INDIA v. NARGESH MEERZA (1981)

The Supreme Court examined discriminatory service conditions imposed on female employees. The decision illustrates that employment policies cannot impose arbitrary or discriminatory disadvantages on workers.

Authority: Direct employment discrimination authority; analogous application to automated employability assessments.

6. VISHAKA v. STATE OF RAJASTHAN (1997)

The Supreme Court recognised the importance of protecting workplace dignity and equality and established binding guidelines against sexual harassment until legislative intervention.

The judgment supports the broader principle that employment practices must protect workers from discriminatory and hostile workplace conditions, including unfair practices associated with automated evaluation.

Authority: Direct workplace equality authority; analogous application to algorithmic employment systems.

7. OLGA TELLIS v. BOMBAY MUNICIPAL CORPORATION (1985)

The Supreme Court recognised the relationship between livelihood and the right to life under Article 21. The judgment is relevant where automated employment systems effectively deprive workers of their livelihood without fair procedures.

Authority: Direct livelihood authority; analogous application to automated work allocation and employability restrictions.

6. EMPLOYER LIABILITY AND LEGAL RESPONSIBILITIES

Employers and organisations deploying automated employability scoring systems should:

Establish objective and job-related scoring criteria.

Conduct regular algorithmic bias and discrimination assessments.

Maintain accurate and updated employee records.

Provide understandable explanations for significant adverse decisions.

Establish human review mechanisms for disputed scores.

Protect sensitive employment and personal information.

Maintain auditable records of automated decisions.

Provide accessible grievance and appeal procedures.

Ensure that contractual arrangements with technology providers do not eliminate employer accountability.

Avoid treating automated scores as conclusive evidence of professional incompetence.

Employers should also distinguish between legitimate performance evaluation and arbitrary restrictions on employment opportunities.

7. PRACTICAL COMPLIANCE AND REMEDIES

Employees affected by unfair employability scoring should request the relevant employment records, challenge inaccurate information and seek written explanations for adverse decisions.

Depending on the employment relationship, applicable legislation and circumstances, remedies may include internal grievance proceedings, labour authority complaints, industrial dispute mechanisms, data protection remedies or constitutional judicial review against unlawful State action.

Organisations should adopt transparent AI governance policies, independent assessments, documented human intervention and periodic reviews of scoring accuracy.

8. CONCLUSION

Automated employability scoring systems create significant legal and social challenges because they can transform employment opportunities into outcomes controlled by opaque technological assessments. Although algorithmic evaluation can improve recruitment and workforce management, it must not undermine equality, privacy, livelihood, dignity and procedural fairness.

Indian constitutional principles and judicial decisions provide important safeguards against arbitrary and discriminatory employment practices. Employers should therefore ensure transparency, accuracy, accountability and meaningful human review before allowing automated scores to influence recruitment, promotion, work allocation or termination.

Ultimately, technological efficiency cannot replace the fundamental requirement that employment decisions must remain fair, reasonable, non-discriminatory and legally accountable.