Using Artificial Intelligence (AI) to Implement Diversity, Equity and Inclusion (DEI) into Marketing Materials: The ‘CONSIDER’ Framework
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.028 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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