How Humans and Machines Make Sense Together: Characteristics and Outcomes of a Case Survey Approach
Bibliographic record
Abstract
There is a growing realization of Artificial Intelligence (AI)’s importance, including its ability to provide competitive advantage and change work for the better. Indeed, organizations are investing in various AI applications in the hope to automate or augment human judgment. Despite the promise of AI, many organizations’ efforts with it are falling short. Therefore, adopting the sensemaking theory as a theoretical lens, this study investigates under which conditions and how human and machine collaborations should be structured, to enhance each other’s capabilities and facilitate optimal strategical decision-making and operational effectiveness. A framework is developed based on the level of complexity of the context and the severity of wrong decisions and four types of human-machine sensemaking processes are proposed. The framework is validated through a qualitative meta-analysis of 48 case studies of AI and highlights the characteristics of the interaction process as well as its outcomes. Besides providing a new instrument for the analysis and assessment of human-AI interactions and controls, this research aids the development of guidelines and facilitates the move towards explainable AI (XAI) design, development, and practices.
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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 | 0.026 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".