Ethical AI Design and Implementation: A Systematic Literature Review
Bibliographic record
Abstract
This study analyzes to what extent information systems (IS) research has investigated artificial intelligence (AI) applications and the ethical concerns that these applications pose in light of the EU AI Act and the recommendations and guidelines provided by other institutions, including the White House, UNESCO, OECD, and Université de Montréal. A systematic literature review methodology and a semantic text similarity analysis will be employed to conduct this investigation. The results of such investigation will lead to contributions to IS researchers by synthesizing previous IS studies on ethical AI design and implementation and proposing an agenda and future directions for IS research to make it more oriented toward the compliance of AI systems with current ethical provisions and considerations. This study will also help practitioners to be more aware of AI ethics and foster technical and managerial solutions that could be developed in compliance with current institutional ethical demands.
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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 | 0.124 | 0.364 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.043 | 0.030 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".