Key Takeaways
- 25% of people with disabilities in the U.S. report discrimination in employment
- 61% of U.S. workers say they have seen harassment at work but did not report it
- 33% of people of color report they do not see themselves represented in leadership roles in their workplace
- 24% of job applicants with a disability report discrimination in hiring
- 37% of job seekers say they were asked about their race, ethnicity, or gender during hiring interviews
- 28% of recruiters say AI hiring tools introduce bias risks
- 18% of U.S. consumers report they have been targeted with ads they considered discriminatory
- 35% of U.S. adults say they have little or no control over what data is collected about them for ads
- 35 jurisdictions: U.S. states and municipalities with privacy or automated decision-making laws (as of 2024) that affect targeting and profiling
- 72% of marketing technology leaders expect increasing regulation on privacy and profiling in the next 3 years
- 3.2x: growth in global spending on marketing analytics and measurement technologies from 2019 to 2023 (CAGR impact)
- 25% of marketers plan to use generative AI for ad creation in 2024, according to survey data
- 61% of marketers use at least one automated decision system for ad delivery or optimization
- 14% of companies report having no documented process for DEI-related risk reviews
- 3.3 million: U.S. residents reached by digital advertising compliance outreach (annual figure) in the FTC's Do Not Call and data-use guidance programs
Marketing teams should act on DEI and accessibility now, as discrimination persists and diverse representation boosts results.
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Workforce Representation
Workforce Representation Interpretation
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Recruiting & Hiring
Recruiting & Hiring Interpretation
Data, Targeting & Fairness
Data, Targeting & Fairness Interpretation
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Industry Trends
Industry Trends Interpretation
Compliance & Risk Controls
Compliance & Risk Controls Interpretation
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Measurement & Outcomes
Measurement & Outcomes Interpretation
Consumer Attitudes
Consumer Attitudes Interpretation
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Performance Metrics
Performance Metrics Interpretation
How We Rate Confidence
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
David Kowalski. (2026, February 13). Diversity Equity And Inclusion In The Digital Marketing Industry Statistics. Gitnux. https://gitnux.org/diversity-equity-and-inclusion-in-the-digital-marketing-industry-statistics
David Kowalski. "Diversity Equity And Inclusion In The Digital Marketing Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/diversity-equity-and-inclusion-in-the-digital-marketing-industry-statistics.
David Kowalski. 2026. "Diversity Equity And Inclusion In The Digital Marketing Industry Statistics." Gitnux. https://gitnux.org/diversity-equity-and-inclusion-in-the-digital-marketing-industry-statistics.
References
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