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Algorithmic Bias in AI: Risks in Welfare, Credit & Policing Systems Due to Flawed Training Data

Science & TechPolitySocial IssuesHIGH
Target:UPSC GS-IIIMPSCTeachingPrelims MediumMains HighStatic GK Link
25 Feb 2026
~2 min
Source: Indian Express
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What This Article Covers

1.Automated decision-making systems are increasingly used for critical public functions like welfare distribution, credit scoring, and traffic policing.

2.These systems can perpetuate historical discrimination through biased training data and 'proxy' indicators like postcode, creating 'feedback loops' that entrench disadvantage.

3.India's data protection framework currently lacks specific provisions to regulate algorithmic bias, creating a gap between technological deployment and constitutional equality principles.

The Big Picture
Prelims · MediumMains · High

Automated decision-making systems (ADMs) used in welfare, credit, and policing risk embedding historical societal biases into their algorithms, leading to systematic discrimination. This creates a critical governance challenge for India's data protection framework, which currently lacks safeguards against such algorithmic bias, raising significant constitutional equality concerns.

Exam Lens

Quick Exam Facts From News

Global ReferenceEuropean data protection law
Key Risk AreaWelfare, credit, insurance, traffic policing
Core IssueBias in historical training data
International CooperationIndia-France partnership on AI governance

1-Minute Revision

  • ›Global Reference: European data protection law
  • ›Key Risk Area: Welfare, credit, insurance, traffic policing
  • ›Target this Concept: 'Proxy Discrimination' in algorithmic systems.
  • ›Target this Gap: India's data protection law is silent on regulating Automated Decision-Making (ADM).
  • ›Target this International Reference: European data protection law's 'right against solely automated processing'.

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Q1Static LinkageEasy

The article discusses the normative foundations of AI governance in the context of a partnership between India and which other country?

Q2Statement-basedHard

Consider the following statements regarding bias in automated decision-making systems:

1. Bias can occur even when protected attributes like caste or religion are explicitly excluded from the algorithm's inputs.

2. Systems tend to favor statistically dominant groups and treat minorities as 'anomalies', creating feedback loops that entrench disadvantage.

3. India's data protection framework provides a comprehensive 'right to explanation' against decisions based on automated processing, similar to the European Union's General Data Protection Regulation (GDPR).

Which of the statements given above is/are correct?

Q3Data-centricMedium

According to the article, in which of the following domains are automated decision-making systems being used, as mentioned?

Q4Application/ImpactMedium

What is identified as a primary reason why data protection laws alone may be insufficient to address bias in automated decision-making?

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