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Advertising

Unilever and IBM Watson peer into advertising’s unwanted bias and stereotyping issues

One of the issues was the portrayal of negative stereotypes of underrepresented communities.

Consumer goods giant Unilever is worried that cancel culture may choose to embrace advertising pretty soon. IBM Watson Advertising has announced a research initiative that will apply open-source AI technology, developed by IBM, to better understand how prevalent unwanted bias is in advertising. IBM Watson Advertising is technology giant IBM’s AI-powered advertising solutions.

An online survey of 1,500 UK and US adults was commissioned by Unilever and conducted by market research company Kantar. It was found that nearly one in two people from marginalised communities – people with disabilities, and Black, Hispanic, Asian and LGBTQ+ people – feel they have been stereotyped in some way through advertising.

As per Unilever’s press release, those from under-represented communities are impacted the most, and are up to 30 per cent more likely to be stereotyped than the general population. A staggering 55 per cent of women of Asian heritage believe that stereotypes in advertising don’t represent them.

Forty-six per cent of men with a disability say they often see negative portrayals of people like them in ads. Sixty-six per cent of LGBTQ+ aged 18-34 believe people from diverse backgrounds feature in ads just to make up the numbers.

To rectify this situation, Unilever has announced it is broadening its 2016 commitment to Unstereotype Alliance. It is an action platform that seeks to eradicate harmful gender-based stereotypes in all media and advertising content via ‘Act 2 Unstereotype’.

  • To provoke inclusive thinking across the end-to-end marketing process, from consumer insight, brand DNA and proposition, marketing mix development, creative development, behind the camera and on-screen portrayals.

  • To ensure an Unstereotype Charter for every Unilever brand, outlining the ED&I commitments the brand will deliver through its marketing.

  • To work with more diverse and under-represented groups on screen and behind the camera.

  • To eradicate any digital alterations to photography – a 100 per cent ban on changing models’ body shape, size, proportion or skin colour.

Aline Santos, chief brand officer and chief diversity and inclusion officer at Unilever, said, “If we want to see systemic change in society, we need to see systemic change in our industry. Act 2 Unstereotype helps brands create a generation free from prejudice. Inclusive marketing is not a choice anymore; we must act now.”

On the other hand, IBM Watson Advertising feels the timing is ideal to address the persistent problem of unwanted bias in advertising. It referenced a study conducted by the Geena Davis Institute on Gender in Media, which found male characters appeared in advertisements 12 per cent more than females. This while women-led and gender-balanced videos yielded 30 per cent more views than other videos, revealing demand for more inclusive content.

Bob Lord, senior vice president, worldwide ecosystems at IBM, said, "Our hope is that AI can be the catalyst for reducing unwanted bias in advertising, just as it is helping to transform the advertising industry as it rebuilds for an era without third party cookies. Through this research, we are taking an important first step toward that goal, applying scientific rigor to determine just how big of an impact AI can have."

IBM Watson Advertising will work with researchers from IBM Research to conduct this study in partnership with the Ad Council and potentially other leaders across industry and academia. Areas of exploration will include:

  • Incidence of bias in advertising – The prevalence and frequency of bias in campaigns through the analysis of performance data. For instance, using the AI Fairness 360 toolkit, a suite of open-source AI tools developed by IBM and donated to the Linux Foundation, the study will look at how certain audiences of past and active campaigns are being targeted with creative content to assess whether bias was present.

  • Role of signals in determining bias – How heavily signals, which refer to the context in which an advertisement is delivered, impact bias. As an example, if a creative message is deemed to be unbiased on its own, yet is delivered on a digital channel alongside an inherently biased signal, the advertisement may be perceived as biased.

  • Capabilities of AI to potentially mitigate bias – How useful AI can be in identifying instances of bias, and what can be done to fully capture the power of AI to potentially reduce occurrences of bias in advertisements.