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Introduction to the Artificial Intelligence in Government Minitrack

2023 | HICSS | Citations: 0
Authors: Carter, L.; Liu, D.; Gasco-Hernandez, M.

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Semantic filters: argumentation theory

Topics: artificial intelligence privacy decision support system

Methods: structured literature research federated learning bibliometric analysis qualitative content analysis design artifact

Theories: argumentation theory
Search & Verify: Misinformation and source evaluations in Internet search results
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2023 | Decision Support Systems | Citations: 0
Authors: Dennis, Alan R.; Moravec, Patricia L.; Kim, Antino

Abstract: In response to online misinformation, source rating services evaluate thousands ... Expand

Abstract: In response to online misinformation, source rating services evaluate thousands of popular websites, mark them as reputable or disreputable, and provide detailed information explaining their evaluations. In this paper, we examine the effects of news source evaluations by experts as well as crowd-sourced information (i.e., Wikipedia) on beliefs in political news that appears in Internet search results. Through two online experiments, we inves tigate the effects of providing a simple source-evaluation flag or a flag with more detailed supporting information explaining the evaluation. We found that negative flags from expert fact-checkers reduced users' beliefs, and supporting information had no additional effect; in contrast, positive flags required information beyond the simple flag to have maximum effectiveness. Positive and negative crowd-sourced information had similar effects but was weaker than evaluations from experts. Thus, we conclude that providing additional information to support source evaluation flags is important to give users confidence in reputable websites. Collapse

Semantic filters: argumentation theory

Topics: Wikipedia internet technology crowdsourcing fake news blog

Methods: theory development linear regression analysis experiment post-hoc analysis survey

Theories: argumentation theory
A Design Theory for Certification Presentations
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2023 | ACM SIGMIS Database | Citations: 0
Authors: Lins, Sebastian; Becker, Jan-Michael; Lyytinen, Kalle; Sunyaev, Ali

Abstract: Prior information system research remains inconsistent of the effects of system ... Expand

Abstract: Prior information system research remains inconsistent of the effects of system certifications. In their current use, certifications are often reduced to graphical seals. This approach fails to incorporate detailed assurance information emanating from the certification process. To address this gap, we adopt a design science approach and deploy a four-phase research design to clarify how to design impactful IS certification presentations. First, we identify sources of users' limited understanding of seals and formulate a design proposal for a certification presentation by drawing upon the elaboration likelihood model. In the second phase, we formulate and validate a set of design meta-requirements and guidelines to improve certification presentation, using cognitive load theory and Toulmin's model of argumentation as kernel theories. In the third phase, new certification presentations that comply with the proposed guidelines are developed and evaluated for their effectiveness. We show that presentations that augment seal-based certification presentations with richer assurance information improve certification effectiveness. This increases users' assurance and trust perceptions when the presentations align with the users' cognitive information processing needs in ways that reduce their cognitive load and enhance argument quality of assurance information. Collapse

Semantic filters: argumentation theory

Topics: cloud computing website information privacy concern cloud storage data quality

Methods: experiment design guideline experimental group survey design artifact

Theories: argumentation theory dual-system theory cognitive load theory elaboration likelihood model
The argumentative salience of technology frames of reference: An analysis of argumentative discourse in the development of a health information exchange initiative
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2023 | Information and Organization | Citations: 0
Authors: Murungi, David M.; Káganer, Evgeny

Abstract: This study examines the impact that the argumentative salience of technology fra ... Expand

Abstract: This study examines the impact that the argumentative salience of technology frames of refer ences has on the execution of complex IS implementation projects. It employs Toulmin's argument model to develop argument maps that depict the structure of argumentation that took place during the development and implementation of an interorganizational health information ex change initiative (HIE) that took place in southeast USA. Toulmin's argument model faciliated the portrayal of frame salience in terms of three structural properties (i.e., blindness, indifference, and ownership). The study used these properties to show how the breadth, depth and conspicuity of frame structures changed during the course of the project and demonstrated how these changes impacted both the level of contestation observed in the project as well as project outcomes. In addition to lending insights that are specific to this case, our conceptualization of frame structure lends itself to cross-case comparisons and future theory building as the impact of these argument structures can be evaluated in a multitude of different contexts. Collapse

Semantic filters: argumentation theory

Topics: IT alignment IT project systems implementation health information system information exchange

Methods: qualitative interview theory development qualitative coding case study personal interview

Theories: argumentation theory
Using Toulmin's Argumentation Model to Enhance Trust in Analytics-Based Advice Giving Systems
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2023 | ACM Transactions on Management Information Systems | Citations: 0
Authors: Rubin, Eran; Benbasat, Izak

Abstract: Ecommerce websites increasingly provide predictive analytics-based advice (PAA), ... Expand

Abstract: Ecommerce websites increasingly provide predictive analytics-based advice (PAA), such as advice about future potential price reductions. Establishing consumer-trust in these advice-giving systems imposes unique and novel challenges. First, PAA about future alternatives that can benefit the consumer appears to inherently contradict the business goal of selling a product quickly and at high profit margins. Second, PAA is based on mathematical models that are non-transparent to the user. Third, PAA advice is inherently uncertain, and can be perceived as subjectively imposed in algorithms. Utilizing Toulmin's argumentation-model, we investigate the influence of advice-justification statements in overcoming these difficulties. Based on three experimental studies, in which respondents are provided with the advice of PAA systems, we show evidence for the different roles Toulmin's statement-types play in enhancing various trusting-beliefs in PAA systems. Provision of warrants is mostly associated with enhanced competence beliefs; rebuttals with integrity beliefs; backings both competence and benevolence; and data statements enhance competence, integrity, and benevolence beliefs. Implications of the findings for research and practice are provided. Collapse

Semantic filters: argumentation theory

Topics: website systems design electronic commerce accounting ergonomics

Methods: experiment multivariate analysis of variance parametric test factor analysis statistical hypothesis test

Theories: argumentation theory
The Role of Process and Outcome Accountability Claims for Shaping AI Developers’ Perceived Accountability

2023 | International Conference on Information Systems | Citations: 0
Authors: Schmidt, Jan-Hendrik; Bartsch, Sebastian; Adam, Martin

Abstract: As accountability becomes increasingly important for developers of artificial i ... Expand

Abstract: As accountability becomes increasingly important for developers of artificial intelligence (AI)-based systems, governance mechanisms such as AI principles or audits are often criticized for not sufficiently influencing AI developers. Therefore, we examine how visualized arguments in user interfaces (UIs) of integrated development environments (IDEs) can increase AI developers' perceived accountability. Combining construal level theory and Toulmin's model of argumentation, four UI design artifacts were developed, each containing a claim of process or outcome accountability with or without monitoring and evaluation tools that act as claim-supporting data. Results of an online experiment with 164 AI developers show that claiming process accountability increases AI developers' perceived accountability more than claiming outcome accountability, both without supporting data. However, when supporting data are available, both claims increase AI developers' perceived accountability comparably effectively. The study's results highlight the theoretical and practical usefulness of visualized arguments in UIs of IDEs to promote AI developers' accountability. Collapse

Semantic filters: argumentation theory

Topics: artificial intelligence individual characteristic data quality crowdsourcing job satisfaction

Methods: design artifact experimental group experiment qualitative interview survey design

Theories: argumentation theory construal level theory
Design and Evaluation of an AI-based Learning System to Foster Students' Structural and Persuasive Writing in Law Courses

2023 | International Conference on Information Systems | Citations: 0
Authors: Weber, Florian; Wambsganss, Thiemo; Soellner, Matthias

Abstract: Structured and persuasive writing is essential for effective communication, con ... Expand

Abstract: Structured and persuasive writing is essential for effective communication, convincing readers of argument validity, and inspiring action. However, studies indicate a decline in students' proficiency in this area. This decline poses challenges in disciplines like law, where success relies on structured and persuasive writing skills. To address these issues, we present the results of our design science research project to develop an AI-based learning system that helps students learn legal writing. Our results from two different experiments with 104 students demonstrate the usefulness of our AI-based learning system to support law students independent of a human tutor, location, and time. Apart from furnishing our integrated software artifact, we also document our assessed design knowledge in the form of a design theory. This marks the first step toward a nascent design theory for the development of AI-based learning systems for legal writing. Collapse

Semantic filters: argumentation theory

Topics: online learning system artificial intelligence usability perceived ease of use user experience

Methods: experimental group design principle qualitative interview field experiment experiment

Theories: learning theory argumentation theory
Exploring AI supported Citizen Argumentation on Urban Participation Platforms

2023 | HICSS | Citations: 0
Authors: Borchers, M.; Tavanapour, N.; Bittner, E.

Abstract: The paradigm shift in urban planning toward citizen participation originates fr ... Expand

Abstract: The paradigm shift in urban planning toward citizen participation originates from the Smart City concept, as politicians and scientists argue that citizens should be included in the design of their environment. This led to the development of urban participation platforms and was enhanced by the COVID-19 pandemic as on-site participation was unavailable. Past projects showed that urban participation platforms can reach thousands of citizens, but it became apparent that citizens' contributions vary widely and are sometimes not understandable and comprehensible which limits their value for urban projects. Therefore, we examined how an AI-based feedback system can increase citizens' argumentation on urban platforms. For this, an explorative comparison of two prototypes was conducted by applying Argumentation Theory and Mayring's qualitative content analysis to empirically analyze collected data. The findings highlight that the developed AI-based feedback system supports citizens and leads to more argumentative and comprehensible argumentations on urban participation platforms. Collapse

Semantic filters: argumentation theory

Topics: artificial intelligence system support laptop computer Java user interface

Methods: design artifact qualitative content analysis group interview experiment design science

Theories: argumentation theory
Towards a Trust Reliance Paradox? Exploring the Gap Between Perceived Trust in and Reliance on Algorithmic Advice

2021 | International Conference on Information Systems | Citations: 12
Authors: Schmitt, Anuschka; Wambsganss, Thiemo; Soellner, Matthias; Janson, Andreas

Abstract: Beyond AI-based systems' potential to augment decision-making, reduce organizat ... Expand

Abstract: Beyond AI-based systems' potential to augment decision-making, reduce organizational resources, and counter human biases, unintended consequences of such systems have been largely neglected so far. Researchers are undecided on whether erroneous advice acts as an impediment to system use or is blindly relied upon. As part of an experimental study, we turn towards the impact of incorrect system advice and how to design for failure-prone AI. In an experiment with 156 subjects we find that, although incorrect algorithmic advice is trusted less, users adapt their answers to a system's incorrect recommendations. While transparency on a system's accuracy levels fosters trust and reliance in the context of incorrect advice, an opposite effect is found for users exposed to correct advice. Our findings point towards a paradoxical gap between stated trust and actual behavior. Furthermore, transparency mechanisms should be deployed with caution as their effectiveness is intertwined with system performance. Collapse

Semantic filters: argumentation theory

Topics: artificial intelligence IT productivity usability human computer interaction conversational agent

Methods: experiment survey design artifact experimental group computational algorithm

Theories: big five model argumentation theory
ArgueBot: A Conversational Agent for Adaptive Argumentation Feedback

2021 | International Conference on Business Informatics | Citations: 0
Authors: Wambsganss, Thiemo; Guggisberg, Sebastian; Söllner, Matthias

Abstract: By combining recent advances in Natural Language Processing and Conversational A ... Expand

Abstract: By combining recent advances in Natural Language Processing and Conversational Agent (CAs), we suggest a new form of human-computer interaction for individuals to receive formative feedback on their argumentation to help them to foster their logical reasoning skills. Hence, we introduce ArgueBot, a conversational agent, that provides adaptive feedback on students' logical argumentation. We, therefore, 1) leveraged a corpus of argumentative student-written peer-reviews in German, 2) trained, tuned, and benchmarked a model that identifies claims, premises and non-argumentative sections of a given text, and 3) built a conversational feedback tool. We evaluated ArgueBot in a proof-of-concept evaluation with students. The evaluation results regarding technology acceptance, the performance of our trained model, and the qualitative feedback indicate the potential of leveraging recent advances in Natural Language Processing for new human-computer interaction use cases for scalable educational feedback. Collapse

Semantic filters: argumentation theory

Topics: Slack social media usability human computer interaction conversational agent

Methods: BERT natural language processing design artifact testing Cross-Industry Standard Process for Data Mining structured literature research

Theories: cognitive dissonance theory argumentation theory technology acceptance model

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Found 52 articles

Introduction to the Artificial Intelligence in Government Minitrack

Search & Verify: Misinformation and source evaluations in Internet search results

A Design Theory for Certification Presentations

The argumentative salience of technology frames of reference: An analysis of argumentative discourse in the development of a health information exchange initiative

Using Toulmin's Argumentation Model to Enhance Trust in Analytics-Based Advice Giving Systems

The Role of Process and Outcome Accountability Claims for Shaping AI Developers’ Perceived Accountability

Design and Evaluation of an AI-based Learning System to Foster Students' Structural and Persuasive Writing in Law Courses

Exploring AI supported Citizen Argumentation on Urban Participation Platforms

Towards a Trust Reliance Paradox? Exploring the Gap Between Perceived Trust in and Reliance on Algorithmic Advice

ArgueBot: A Conversational Agent for Adaptive Argumentation Feedback