Does explanation improve the acceptance of decision support for product release planning?
Bibliographic record
Abstract
Objective: Decision support provided to users is often lack of acceptance. One of the reasons is a deficit in understanding where the suggestions come from and how they come. This essentially is not a technical problem, but a technology adoption problem. This situation was also analyzed as a result of former empirical studies conducted on ReleasePlannerTM, a decision support tool for planning product releases. To overcome this situation, three machine learning techniques have been applied to mine the tool's solutions, and the mining results are presented to the tool users as explanations. This paper presents the evaluation on the generated explanations as a means to improve the user acceptance of the tool. Method: A three-stage controlled experiment was designed and carried out with a group of ten graduate students at the University of Calgary and another group of five project managers from the IT industry. Two research goals were addressed to (i) evaluate the impact of the explanations generated from these three applied techniques, and (ii) compare some of the findings from this study with the ones from our previous experiments. Results: Our findings for the first research goal indicated that the explanations generated from the three techniques contributed to the improvement of the subjects' confidence in the tool solutions and trust of the tool, and therefore an overall better user acceptance of the tool. Meanwhile, no significant differences were found among the impacts of the three techniques. For the second research goal, we found that some of the findings from this study were consistent with the ones from our previous experiments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".