Human Trust Factors in Image Analysis

Williams, Anna; Sherman, Imani N.; Smarr, Simone; Posadas, Brianna; Gilbert, Juan E.

doi:10.1007/978-3-319-94391-6_1

Cited by 5 publications

(10 citation statements)

References 12 publications

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“…(2007) that examined DIF tools to detect tampering traces associated with resampling and/or identification of sources patterns in the origin of the images, using a pull of 300 initial photos. Likewise, Williams et al. (2018) investigated the human trust factor concerning the assistance of a graphics expert or an algorithm, using a set of 80 pictures.…”

Section: Discussionmentioning

confidence: 99%

“…In this direction, such firm and featured datasets seem to be fitting in the context, so they need to be updated with additional samples and their multiscale ratings. Another positive sight is that relevant works adopted similar categorization of subjects, i.e., experts in graphics and artificial intelligence ( Hulzebosch et al., 2020 ; Williams et al., 2018 ). Hence, the adopted categorization of participants appears to be on the right track.…”

Section: Discussionmentioning

confidence: 99%

“…It has to be pointed out that the main purpose of this research is not to monitor how subjects respond to the authenticity evaluation of multiple news-photos (especially for the first part of the repeated measures experiment conducted through the interviewing test of small groups of experts). Other studies deal with this matter, in various perspectives ( Gloe et al., 2007 ; Nightingale et al., 2017 ; Pantti and Sirén, 2015 ; Schetinger et al., 2017 ; Williams et al., 2018 ). In the current setup, stimulating images are treated as control variables that exhibit specific informatory and viralness features that are strongly linked to the studied authentication processes.…”

Section: Methodsmentioning

confidence: 99%

“…However, little effort is given to the adoption and evaluation of the associated tools and services from users' perspectives. In many views, this hysteresis of the practice over research deteriorates the true potentials of the algorithmic approaches ( Gloe et al., 2007 ; Katsaounidou & Dimoulas, 2018a , 2018b Katsaounidou, 2020 ; Williams et al., 2018 ). Hence, further insights concerning the perception and utilization of real-world DIF verification assistants are pursued within this work.…”

Section: Introductionmentioning

confidence: 99%

“…At the same time, the necessity of “explanation interfaces” for comprehending the DIF-outputs was highlighted, focusing on fitting User eXperience (UX) design practices that would enhance the experience (and the trust) between humans and machines. Williams, Sherman, Smarr, Posadas & Gilbert (2018) , for instance, analyzed the different ways that individuals perceive information coming from other people or machines (algorithms), thus aiming at identifying implicit biasing reactions. More particularly, a dataset of authentic and tampered images was evaluated by experts (Expert Graphic Artist) and machine (Image Processing Algorithm).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

News authentication and tampered images: evaluating the photo-truth impact through image verification algorithms

Katsaounidou¹,

Gardikiotis²,

Tsipas³

et al. 2020

Heliyon

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Photos have been used as evident material in news reporting almost since the beginning of Journalism. In this context, manipulated or tampered pictures are very common as part of informing articles, in today's misinformation crisis. The current paper investigates the ability of people to distinguish real from fake images. The presented data derive from two studies. Firstly, an online cross-sectional survey (N = 120) was conducted to analyze ordinary human skills in recognizing forgery attacks. The target was to evaluate individuals' perception in identifying manipulated visual content, therefore, to investigate the feasibility of “crowdsourced validation”. This last term refers to the process of gathering fact-checking feedback from multiple users, thus collaborating towards assembling pieces of evidence on an event. Secondly, given that contemporary veracity solutions are coupled with both journalistic principles and technology developments, an experiment in two phases was employed: a) A repeated measures experiment was conducted to quantify the associated abilities of Media and Image Experts (N = 5 + 5) in detecting tampering artifacts. In this latter case, image verification algorithms were put into the core of the analysis procedure to examine their impact on the authenticity assessment task. b) Apart from conducting interview sessions with the selected experts and their proper guidance in using the tools, a second experiment was also deployed on a larger scale through an online survey (N = 301), aiming at validating some of the initial findings. The primary intent of the deployed analysis and their combined interpretation was to evaluate image forensic services, offered as real-world tools, regarding their comprehension and utilization by ordinary people, involved in the everyday battle against misinformation. The outcomes confirmed the suspicion that only a few subjects had prior knowledge of the implicated algorithmic solutions. Although these assistive tools often lead to controversial or even contradictory conclusions, their experimental treatment with the systematic training in their proper use boosted the participants' performance. Overall, the research findings indicate that the scores of successful detections, relying exclusively on human observations, cannot be disregarded. Hence, the ultimate challenge for the “verification industry” should be to balance between forensic automations and the human experience, aiming at defending the audience from inaccurate information propagation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

News authentication and tampered images: evaluating the photo-truth impact through image verification algorithms

Katsaounidou¹,

Gardikiotis²,

Tsipas³

et al. 2020

Heliyon

View full text Add to dashboard Cite

show abstract

After opening the black box: Meta-dehumanization matters in algorithm recommendation aversion

Chen,

Dang,

Liu

2024

Computers in Human Behavior

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Artificial Intelligence and Groups: Effects of Attitudes and Discretion on Collaboration

Bezrukova

Spell

Rice

et al. 2023

Group & Organization Management

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We theorize about human-team collaboration with AI by drawing upon research in groups and teams, social psychology, information systems, engineering, and beyond. Based on our review, we focus on two main issues in the teams and AI arena. The first is whether the team generally views AI positively or negatively. The second is whether the decision to use AI is left up to the team members (voluntary use of AI) or mandated by top management or other policy-setters in the organization. These two aspects guide our creation of a team-level conceptual framework modeling how AI introduced as a mandated addition to the team can have asymmetric effects on collaboration level depending on the team’s attitudes about AI. When AI is viewed positively by the team, the effect of mandatory use suppresses collaboration in the team. But when a team has negative attitudes toward AI, mandatory use elevates team collaboration. Our model emphasizes the need for managing team attitudes and discretion strategies and promoting new research directions regarding AI’s implications for teamwork.

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Human Trust Factors in Image Analysis

Cited by 5 publications

References 12 publications

News authentication and tampered images: evaluating the photo-truth impact through image verification algorithms

News authentication and tampered images: evaluating the photo-truth impact through image verification algorithms

After opening the black box: Meta-dehumanization matters in algorithm recommendation aversion

Artificial Intelligence and Groups: Effects of Attitudes and Discretion on Collaboration

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