AI incident #84 · 9 October 2020

Tiny Changes Let False Claims About COVID-19, Voting Evade Facebook Fact Checks

Open on the AI Incident Database 1 news report Snapshot 7 Sep 2026

What happened

Avaaz, an international advocacy group, released a review of Facebook's misinformation identifying software showing that the labeling process failed to label 42% of false information posts, most surrounding COVID-19 and the 2020 USA Presidential Election.

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News reports (1)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

9 Oct 2020
Tiny Changes Let False Claims About COVID-19, Voting Evade Facebook Fact Checks
npr.org · Shannon Bond · AIID #1384

Who was involved

Alleged deployer: Facebook
Alleged developer: Facebook
Alleged harmed party: Facebook Users, Facebook Users Interested In Covid Information, Facebook Users Interested In The Us Presidential Election

Classification (MIT AI Risk Repository taxonomy)

Risk domain: Misinformation
Risk subdomain: 3.1 False or misleading information
Causal entity: AI
Intent: Unintentional
Timing: Post-deployment
Harm level: none
Sectors: information and communication
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)
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)
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)
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)
Untruthful Content
"The LLM-generated content could contain inaccurate information"
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)
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)

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