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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11In October 2023, Mercado Libre began a pilot using VidMob’s Diversity and Inclusion Scoring tool to review some advertising campaigns before publication. The system was described as assessing the apparent age range, gender and skin tone of people shown in the creative. It was a way to audit visible representation—not proof that an ad was inclusive, that stereotypes had been removed or that campaign outcomes improved.
What Mercado Libre tested
Exame reported on October 16, 2023, that Mercado Libre was piloting VidMob’s Diversity and Inclusion Scoring product on a portion of its campaigns. The company’s stated aim was to make diversity, equity and inclusion considerations more practical in marketing and to help identify stereotypes and dominant viewpoints in advertising. Exame’s account of the pilot describes it as a pre-publication check, not an autonomous decision about whether an ad was socially or culturally diverse.
The reported process put Mercado Libre in charge of setting the criteria. Creatives were then analyzed, with a report or score intended to help marketing teams decide whether to revise an ad before it went live. VidMob said that human curation could be used where a case required more detailed judgment. VidMob’s account of the project also described the first phase as beginning in Brazil, with a proposed expansion to 18 other Latin American countries. That was a stated plan; the available reporting does not verify that the broader rollout was completed.
What the system assessed—and what it did not
The initial attributes publicly described were apparent age range, gender and skin tone. These are visual inferences about people depicted in an advertisement, not verified demographic details or self-identified information. The reporting does not establish that the pilot measured race, ethnicity, disability, sexuality, body type, religion or socioeconomic status.
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The distinction between creative representation and other kinds of advertising measurement is important:
- Creative representation: who appears in the ad and how they are depicted. This is the area the pilot was described as examining.
- Audience delivery: who actually receives an ad. A creative audit does not establish whether an advertising platform distributes impressions equitably.
- Business response: who clicks, buys or converts. The pilot reporting does not show that the score measured these outcomes.
- Social effect: whether an ad changes stereotypes or improves inclusion. Visible representation alone cannot demonstrate that.
Research on algorithmic ad delivery treats distribution fairness as a separate issue from who appears in an advertisement; see this study of demographic disparities in online advertising delivery. A creative-scoring tool and an ad-delivery audit answer different questions.
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Why use an AI-assisted review?
For a company managing campaigns across markets and formats, a repeatable pre-flight check could help teams screen more assets, apply brand-defined criteria consistently and build an internal baseline for visible representation. It may also flag omissions while teams can still edit a creative. Those are plausible operational benefits of the proposed workflow, not published results of Mercado Libre’s pilot.
A score is only useful if teams understand what generated it and what action it supports. Counting apparent traits can prompt a review, but it cannot decide whether a person has agency in the story, whether the language is respectful, or whether a portrayal makes sense in the local context. Human review was therefore an important part of the described process, not evidence that the software could settle those questions by itself.
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Why a representation score is not an inclusion verdict
An ad can show a varied cast yet still assign people stereotyped roles, use a group as a visual prop, or frame a community in an exoticizing way. A numerical score can create false reassurance if it makes teams treat a visible count as a completed DEI review. The reverse is also possible: a narrowly cast ad may be appropriate to a particular product, audience or story. Context matters.
Automated visual classification also has limits. Apparent age, gender and skin tone are not objective identity records. Lighting, makeup, image quality, camera angle, occlusion, animation and generated imagery can complicate classification. Performance may vary across people and circumstances, and the public accounts do not provide accuracy, calibration or subgroup-error data for this system.
Regional standards need particular care. Brazil, Mexico, Argentina and other markets do not share one uniform vocabulary or cultural context for identity, colorism, Indigenous representation or disability. Applying a common framework across markets would require local expertise rather than assuming one score has the same meaning everywhere.
The available reporting also does not explain what happened to analyzed imagery: whether face imagery was stored, where processing took place, how long data was retained, whether it was used to train models, or what consent practices applied. Those are material questions for any organization evaluating a visual-analysis vendor, but the cited accounts do not answer them.
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Accessibility was proposed for a later phase
VidMob said it was working toward adding accessibility-related analysis in a future version, with examples involving color and font choices. Exame reported VidMob’s claim that accessibility improvements could increase campaign reach by as much as 20%. That figure was presented as a potential-reach claim from the vendor, not as a measured Mercado Libre result or an independently validated estimate. Accessibility is also broader than color and typography: captions, audio description, sign-language interpretation and flashing content may matter depending on the format.
What remains unproven
The public accounts describe the pilot and its intended method, but do not report a final evaluation. They do not establish how many ads were analyzed, how scores changed, how often teams revised campaigns, how frequently human reviewers overrode a recommendation, or whether stereotyping, accessibility, conversion or brand outcomes improved. Nor do they publish validation results such as false-positive and false-negative rates.
VidMob described Mercado Libre as the first company globally to adopt the tool. That is the vendor’s characterization, not an independently verified industry-wide finding. Likewise, the proposed rollout beyond Brazil should not be mistaken for confirmed deployment across 19 markets.
A separate Mercado Libre AI project
A 2026 Mutt Data case study describes Brand ID, a separate Mercado Libre project for reviewing brand compliance across image and video assets using multimodal AI. It is not the 2023 diversity-scoring pilot and does not establish that the earlier system succeeded or remains in use. See the Brand ID case study for that distinct use case.
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