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 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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".