Using Artificial Intelligence (AI) to Implement Diversity, Equity and Inclusion (DEI) into Marketing Materials: The ‘CONSIDER’ Framework
Bibliographic record
Abstract
Diversity, equity and inclusion (DEI) in marketing– defined as the composition of an organisation’s marketing reflects diverse, equitable representation of its consumer base, especially with respect to the use of inclusive, bias-free imagery, language and messaging among underrepresented, underserved and marginalised consumer segments – has led to the advancement of AI-enabled technologies to aid marketers improve the DEI of their marketing materials. To ensure DEI marketing strategies are fully considered and that the use of AI is implemented effectively, we suggest marketers to utilise our CONSIDER framework (comprehend current state, operationalise with openness, nurture dynamic relevance, set standards, involve stakeholders, diversify data, elevate literacy and regular monitoring). We then highlight the pros and cons for using AI to implement DEI into marketing materials and provide several AI-enabled metrics (accessibility, allyship, cultural sensitivity, diversity, gender parity, inclusivity intersectionality and representation) that offer a more objective and quantitative approach for marketers to assess how well they are meeting their DEI goals and identifying gaps in representation to make changes to improve the DEI of their marketing materials.
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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 | 0.032 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".