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AI incident ·

AI Deepfakes for Voter Outreach Flood Indian Elections

1 news report Snapshot 7 Sep 2026
On this page 8 sections

In brief

An AI system built by Divyendra Singh Jadoun, Polymath Synthetic Media Solutions and 4 others and deployed by Bharatiya Janata Party (Bjp), Indian National Congress (Inc) and 3 others allegedly harmed Indian Voters, General Public Misled By Deepfake Content and 3 others.

Risk domain
Misinformation False or misleading information
Occurred
Coverage
1 reportMay 2024

What happened

During the 2024 Indian elections, politicians used AI-generated deepfakes to reach voters, who might be unaware they're interacting with digital clones. Providers like Divyendra Singh Jadoun of Polymath Synthetic Media Solutions created deepfakes for personalized messages. This practice, used by various political parties, is not truthful, as voters may be misled by AI-generated content posing as genuine interactions with political figures.

Laws on record for this risk area

Policy angle: Classified under Misinformation (False or misleading information) in the MIT AI Risk Repository taxonomy; 5 instruments on record cover this risk area.

Matched from the record's risk category and country to the instruments in effect recorded here: a law listed covers the area, which is not a finding that it applies to this incident. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).

News reports (1)

Titles link to the original publisher; report text is not reproduced here.

  1. Indian Voters Are Being Bombarded With Millions of Deepfakes. Political Candidates Approve
    wired.com · Nilesh Christopher, Varsha Bansal

Who was involved

Alleged harmed party
Indian Voters, General Public Misled By Deepfake Content, Political Integrity And Election Fairness, Epistemic Integrity, Democratic Integrity

Classification (MIT AI Risk Repository taxonomy)

Risk domain
Misinformation
Causal entity
AI
Intent
Intentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
—

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 3.1.

  • Pursuing Consistent Context

    "LLMs have been demonstrated to pursue consistent context [129]–[132], which may lead to erroneous generation when the prefixes contain false information. Typical examples include sycophancy [129], [130], false demonstra...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Defective Decoding Process

    In general, LLMs employ the Transformer architecture [32] and generate content in an autoregressive manner, where the prediction of the next token is conditioned on the previously generated token sequence. Such a scheme...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Noisy Training Data

    "Another important source of hallucinations is the noise in training data, which introduces errors in the knowledge stored in model parameters [111]–[113]. Generally, the training data inherently harbors misinformation....

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Untruthful Content

    "The LLM-generated content could contain inaccurate information"

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Hallucinations

    "LLMs generate nonsensical, untruthful, and factual incorrect content"

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Faithfulness Errors

    "The LLM-generated content could contain inaccurate information" which is is not true to the source material or input used

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Factuality Errors

    "The LLM-generated content could contain inaccurate information" which is factually incorrect

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Knowledge Gaps

    "Since the training corpora of LLMs can not contain all possible world knowledge [114]–[119], and it is challenging for LLMs to grasp the long-tail knowledge within their training data [120], [121], LLMs inherently posse...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

Incidents in the same risk subdomain

All incidents in this subdomain

Source record: incident #730 on the AI Incident Database · all 1 report