Addressing Bias in GenAI-Led Content Production

Technology providers such as Amazon, Google, Meta and Microsoft, have long sought to address concerns about bias in Artificial Intelligence (AI) datasets. However, the sudden, rapid adoption of the latest wave of AI tools that use large language models (LLMs) to generate text and artwork for marketers presents a new class of challenges. As the leading and most visible adopters of genAI in most organisations, marketers must develop best practices to avoid damage to their brands and organisations.

The use of genAI in content production for personalized experiences multiplies the opportunities for bias to escape review and detection. This is due to both the surge in new content creation and the various combinations of messaging and images that could be presented to a consumer. To mitigate this, organisations must develop operating models, frameworks and employee engagement to detect and address bias.

When algorithms inadvertently disfavor customer segments with disproportionate gender, ethnic or racial characteristics, the result is often described as “allocative harm”. Meanwhile, “representational harm” refers to stereotypical associations that appear in recommendations, search results, images, speech and text. To address this, organisations must incorporate oversight into their regular operations and formalise principles of diversity and inclusion.

Marketing leaders should also advise communications and HR on how to enhance diversity, equity and inclusion training programs to include AI-related topics. In addition, they should ensure that the test data includes examples that could potentially trigger bias. Finally, curate diverse and representative datasets using both data science tools and human feedback at all stages of model development and deployment.

By taking these steps, marketers can protect their brand and pay dividends by avoiding blindspots with their genAI-led projects.

Originally reported by Martech: https://martech.org/how-marketers-can-mitigate-bias-in-generative-ai/
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