Exploitation of expert system in identifying organizational ethics through controlling decision making process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper examines how expert system may negatively or positively influence ethical based decision making process in an organization. Expert system ethical characteristics are chosen; including lack of human intelligence, lack of emotions, accidental bias and lack of values. Depending on quantitative approach; and through distributing a questionnaire on (132) GM, Deputy GM, project manager and officer, it appeared that expert systems' characteristics negatively influenced on the degree of ethics within the organizational setting. According to analysis, it appears that lack of values and human intelligence were among the characteristics that hinder the ethical stream adoption within an organization leading to ethics problems and malfunctioning on the decision making process. On the other hand, lack of emotions appeared to have good impact on the ethical efforts within an organization. Researchers recommended taking extra measures of surveillance in terms of ethics for individuals who are supposed to develop and monitor expert systems.
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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 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 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 it