Using Toulmin's Argumentation Model to Enhance Trust in Analytics-Based Advice Giving Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it