Muslims 60% more likely to be flagged for "logical discrepancies" in Delhi electoral rolls
GS2Polity · Governance · IR· Vulnerable sections (SC/ST, minorities, disabled, elderly, children)· Prelims + Mains·
Algorithmic bias in electoral rolls: a GS2 case study on digital governance and inclusive representation.
Why in news
An analysis by The Hindu revealed that the Election Commission of India's ERONET software disproportionately flagged Muslims for 'logical discrepancies' in Delhi's draft electoral rolls.
Background
Out of 33.12 lakh electors flagged in Delhi's draft electoral rolls, Muslims accounted for 20.3%. The analysis found Muslims were 60% more likely to be flagged than non-Muslims for indicators such as 'Progeny >=6'.
Facts for Prelims
- BodyERONET: Software used by the Election Commission of India (ECI) for electoral roll management.
- FactMuslims accounted for 20.3% of the 33.12 lakh electors flagged in Delhi's draft electoral rolls.
- FactMuslims were 60% more likely to be flagged for 'logical discrepancies' compared to non-Muslims.
Prelims practice question
With reference to the ERONET software and Delhi's draft electoral rolls, consider the following statements:
- The ERONET software is used by the Election Commission of India for electoral roll management.
- Muslims accounted for 20.3% of the 33.12 lakh electors flagged in Delhi's draft electoral rolls.
- Muslims were 30% more likely to be flagged for logical discrepancies compared to non-Muslims.
Which of the statements given above is/are correct?
- (a)1 only
- (b)3 only
- (c)1 and 2 only
- (d)1 and 3 only
Show answer
Answer: (c) 1 and 2 only — Statements 1 and 2 are correct. Statement 3 is incorrect: The note states they were 60% more likely to be flagged.
For Mains
Q. Discuss the implications of algorithmic bias in electoral roll management on the democratic right to vote and the principle of inclusive representation.
Dimensions to cover in your answer
- Algorithmic bias: Risk of automated systems reinforcing socio-economic prejudices in voter list purification
- Transparency deficit: Lack of formal explanation or public disclosure regarding 'logical discrepancy' criteria
- Democratic erosion: Potential for disproportionate disenfranchisement of minority communities through technical filtering
Keywords: Algorithmic bias · Disenfranchisement · Data integrity · Inclusive representation · Digital governance
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