An Overview of Multidisciplinary Research on Explainability: Concepts, Challenges, and Design Considerations
Bibliographic record
Abstract
Intelligent systems, including those powered by Artificial Intelligence (AI), are being increasingly used in our everyday life and there is a growing demand for making them transparent, understandable, predictable, and ultimately, acceptable. This can be partially achieved through the integration of explanation capabilities. However, practically speaking, the implementation of useful explanation capabilities for end-users has always been a difficult task, and the difficulty grows as intelligent systems use more complex reasoning and learning procedures. This paper provides a brief overview of studies on explanations and describes important explanation concepts across different disciplines and through the history of their integration in knowledge-based and later Machine Leaning (ML)-based systems. It discusses the general challenges of explanation design, and those that are unique to dynamic and/or distributed environments. Finally, it argues for a human-centered perspective for explanations where characteristics of good explanations and design considerations are discussed.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".