Understanding Uncertainty: How Lay Decision-makers Perceive and Interpret Uncertainty in Human-AI Decision Making
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
Decision Support Systems (DSS) based on Machine Learning (ML) often aim to assist lay decision-makers, who are not math-savvy, in making high-stakes decisions. However, existing ML-based DSS are not always transparent about the probabilistic nature of ML predictions and how uncertain each prediction is. This lack of transparency could give lay decision-makers a false sense of reliability. Growing calls for AI transparency have led to increasing efforts to quantify and communicate model uncertainty. However, there are still gaps in knowledge regarding how and why the decision-makers utilize ML uncertainty information in their decision process. Here, we conducted a qualitative, think-aloud user study with 17 lay decision-makers who interacted with three different DSS: 1) interactive visualization, 2) DSS based on an ML model that provides predictions without uncertainty information, and 3) the same DSS with uncertainty information. Our qualitative analysis found that communicating uncertainty about ML predictions forced participants to slow down and think analytically about their decisions. This in turn made participants more vigilant, resulting in reduction in over-reliance on ML-based DSS. Our work contributes empirical knowledge on how lay decision-makers perceive, interpret, and make use of uncertainty information when interacting with DSS. Such foundational knowledge informs the design of future ML-based DSS that embrace transparent uncertainty communication.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it