Pith Number

pith:2LJHODZQ

pith:2021:2LJHODZQPFQSD3IUSVDNW4AZWA

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To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making

Krzysztof Z. Gajos, Maja Barbara Malaya, Zana Bu\c{c}inca

Cognitive forcing interventions reduce overreliance on incorrect AI suggestions by prompting deeper analysis.

arxiv:2102.09692 v1 · 2021-02-19 · cs.HC · cs.AI

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Claims

C1strongest claim

The results demonstrate that cognitive forcing significantly reduced overreliance compared to the simple explainable AI approaches.

C2weakest assumption

That the three cognitive forcing interventions actually compel deeper analytical engagement with AI explanations rather than simply adding friction or changing behavior through other mechanisms.

C3one line summary

Cognitive forcing interventions reduce overreliance on AI recommendations more than simple explanations, with effects moderated by individual need for cognition.

References

67 extracted · 67 resolved · 1 Pith anchors

[1] Gagan Bansal, Besmira Nushi, Ece Kamar, Walter S Lasecki, Daniel S Weld, and Eric Horvitz. 2019. Beyond accuracy: The role of mental models in human-AI team performance. In Proceedings of the AAAI Con 2019

[2] Gagan Bansal, Tongshuang Wu, Joyce Zhu, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro, and Daniel S Weld. 2021. Does the Whole Exceed its Parts? The Effect of AI Explanations on Complemen 2021

[3] Dale J Barr, Roger Levy, Christoph Scheepers, and Harry J Tily. 2013. Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of memory and language 68, 3 (2013), 255–27 2013

[4] Eta S Berner and Mark L Graber. 2008. Overconfidence as a cause of diagnostic error in medicine.The American journal of medicine 121, 5 (2008), S2–S23 2008

[5] doi: 10.24963/ijcai 2020 · doi:10.24963/ijcai

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Cited by

45 papers in Pith

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Explaining Too Much? Understanding How Large Language Model Reasoning Traces Influence Performance and Metacognition

Receipt and verification

First computed	2026-05-17T23:38:50.649175Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

d2d2770f30796121ed149546db7019b004d710e202a3b597e0d6a35e97f77938

Aliases

arxiv: 2102.09692 · arxiv_version: 2102.09692v1 · doi: 10.48550/arxiv.2102.09692 · pith_short_12: 2LJHODZQPFQS · pith_short_16: 2LJHODZQPFQSD3IU · pith_short_8: 2LJHODZQ

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/2LJHODZQPFQSD3IUSVDNW4AZWA \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: d2d2770f30796121ed149546db7019b004d710e202a3b597e0d6a35e97f77938

Canonical record JSON

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      "cs.AI"
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.HC",
    "submitted_at": "2021-02-19T00:38:53Z",
    "title_canon_sha256": "0ae941820402a0c948f4e9ad8174dacb0ffe7ce4a807b1ecd5a6267e6aebc679"
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