Ensuring Transparency and Fairness in AI DecisionMaking Processes Influenced by large language Models
Bibliographic record
Abstract
This paper tackles the essential problem of assuring openness and justice in AI decision-making processes driven by huge language models. While large language models like GPT-3 have shown impressive capabilities, they have also prompted questions of fairness, transparency, and ethics. The “FairTransLing” approach contains features to improve interpretability, bias reduction, fairness evaluation, transparency, regulatory compliance, and real-time monitoring, and is proposed as a solution to these problems. The state-of-the-art and time-tested approaches “Interpret ML,” “Fair ML,” “BERT Viz,” “FairGAN,” “LIME,” and “AIF360” were tested against FairTransLing in great detail. We compared these strategies based on six criteria: improved interpretability, reduced bias, increased fairness, improved transparency and fairness, more regulatory compliance, and continuous monitoring. The suggested strategy performed better than the conventional approaches in every respect. Our results demonstrate that FairTransLing provides a complete approach to increase interpretability, eliminate biases, and measure fairness in AI decision-making. Ethical problems and possible biases are reduced because of its emphasis on openness, regulatory compliance, and real-time monitoring. This study is a major advance toward the transparent and equitable implementation of massive language models used in AI systems.
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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 | 0.059 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".