A framework of diversity, equity, and inclusion safeguards for chatbots
Bibliographic record
Abstract
Chatbots such as ChatGPT are conversational agents that play an important role in many sectors. They are highly sophisticated artificial intelligence (AI) agents increasingly integrated into organizational functions ranging from customer support to recruitment and professional development. Alongside the increasing adoption of chatbots is an increasing focus on diversity, equity, and inclusion (DEI). This article presents a series of chatbot examples and discusses their associated DEI implications, such as those related to AI bias. From the cases reviewed in this article, we extract a framework of DEI safeguards, which includes input safeguards, design safeguards, and functional safeguards. This framework can be used by those involved in developing AI based chatbots or managing their use to ensure that chatbots support, rather than weaken, organizational DEI initiatives and strategies.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".