Evidence map›Paper›PMID 36560330›Full record

ArticleSensors (Basel, Switzerland)2022

Unconscious Other's Impression Changer: A Method to Manipulate Cognitive Biases That Subtly Change Others' Impressions Positively/Negatively by Making AI Bias in Emotion Estimation AI.

Kyosuke Futami, Sadahiro Yanase, Kazuya Murao, Tsutomu Terada

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Kyosuke FutamiGraduate School of Information Science and Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu 525-8577, Shiga, Japan.ORCID 0000-0002-4463-5863
Sadahiro YanaseGraduate School of Information Science and Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu 525-8577, Shiga, Japan.
Kazuya MuraoGraduate School of Information Science and Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu 525-8577, Shiga, Japan.ORCID 0000-0002-3777-8399
Tsutomu TeradaGraduate School of Engineering, Kobe University, Kobe 657-8501, Hyogo, Japan.ORCID 0000-0003-2260-3788

Funding

Japan Science and Technology Agency JPMJCR18A3Japan Society for the Promotion of Science JP19K20330
6 · The paper itself

Abstract

Artificial Intelligence (AI) for human emotion estimation, such as facial emotion estimation, has been actively studied. On the other hand, there has been little research on unconscious phenomena in cognition and psychology (i.e., cognitive biases) caused by viewing AI emotion estimation information. Therefore, this study verifies RQ "Do people have a cognitive bias in which impressions of others (i.e., how to see and feel about others) are changed by viewing biased AI's emotion estimation information? If it exists, can impression manipulation methods that intentionally use this cognitive bias be realized?" The proposed method for verification makes the emotion estimation system biased so as to estimate emotion more positively/negatively than AI without bias. A prototype system was implemented. Evaluation using video showed that the presentation of biased emotion estimation information causes a phenomenon that quickly and unconsciously changes the way people see and feel others' impressions, which supported the RQ. Specifically, viewing information that estimated others' emotions more positively/negatively caused the phenomenon in which the user's self-judgment was overridden and others' impressions of emotions, words, and actions were perceived more positively/negatively. The existence of this phenomenon and method indicates that biased emotion estimation AI has the potential to both cause adverse effects on people and support people for good purposes through the manipulation of their impressions. This study provides helpful insights for the design and use of emotion estimation AI considering cognitive biases.

Indexed as

Artificial IntelligenceEmotionsAttitudeBiasCognitionHumansbias in AIcognitive biasesemotion estimation AIhuman–computer interactionimpression of peopleinformation presentationpsychological effect

Identifiers

PMID36560330
PMCPMC9784191

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.