Mitigating Age-Related Bias in Large Language Models: Strategies for Responsible Artificial Intelligence Development
Bibliographic record
Abstract
The increasing popularity of large language models (LLMs) in digital platforms elevates the urgency to address inherent biases, particularly age-related biases, which can significantly skew the model’s fairness and performance. This paper introduces a novel two-stage bias mitigation approach utilizing LLM’s empathy ability, reinforcement learning, and human-in-the-loop mechanisms to identify and correct age-related biases without altering model parameters. There are two modes for our bias mitigation strategy. Self-bias mitigation in the loop allows LLMs to self-assess and adjust their outputs autonomously, promoting inherent bias awareness and correction. Alternatively, cooperative bias mitigation in the loop leverages collaborative filtering among multiple LLMs to debate and mitigate biases through consensus. Furthermore, we introduce the empathetic perspective exchange strategy, which can further refine the answers by changing the perspective in the context information given to the LLM. In this way, more suitable responses applicable to different ages are generated. Our comprehensive evaluation across several data sets demonstrates that our trained model, FairLLM, significantly reduces age bias, outperforming existing techniques in fairness metrics. These findings underscore the effectiveness of our proposed framework in fostering the development of more equitable artificial intelligence systems, potentially benefiting a broader demographic spectrum by reducing digital ageism. History: This paper has been accepted by Kaushik Dutta for the Special Issue on Responsible AI and Data Science for Social Good. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71971046, 72172029, 72403033, 72272028, and 72442025]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0645 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0645 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
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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 | 0.019 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".