The interaction between rationality, politics and artificial intelligence in the decision-making process
Bibliographic record
Abstract
This review paper delves deeply into the intricate correlation between rational and political strategies in the decision-making process of information technology governance (ITG). The core focus is to understand how advanced technologies like artificial intelligence (AI), machine learning, and decision intelligence, when juxtaposed with traditional political decision-making methods and rational conceptualization (Cohen & Comesaña, 2023), coalesce within the ITG framework. The authors posit that while ITG’s decision-making can be influenced by AI, rationality, or politics, there’s a discernible alignment of managerial actions leveraging big data and machine learning with rational models, rather than political ones. Furthermore, the paper touches upon the power dynamics and strategic decision-making processes that often underpin ITG decisions. This research not only deepens the theoretical understanding but also provides pragmatic recommendations, making it invaluable for informed resource management in business management and ITG (Filgueiras, 2023). Through this exploration, stakeholders can better navigate the complexities of ITG, ensuring that technology aligns with organizational goals and strategies. As this paper identifies the power dynamics and strategic decision-making processes that often underpin ITG decisions, we can state that there was a discernible alignment of managerial actions leveraging big data and machine learning with rational models, rather than political ones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".