<p>Schamp, C., Hartmann, J., & Herhausen, D. (2024). Bye-bye Bias: What to Consider When Training Generative AI Models on Subjective Marketing Metrics. NIM Marketing Intelligence Review, 16(1) 42-48. https://doi.org/10.2478/nimmir-2024-0007</p>
Bye-bye Bias: What to Consider When Training Generative AI Models on Subjective Marketing Metrics
Abstract:
Biased training data can distort the outputs of GenAI models and should therefore be a core concern when the models are developed. Ideally, potential biases should be assessed and addressed before the model training and complement the current practice of error-analysis in post-training. This will make the AI model training not only more effective but also more cost-efficient. Being aware of the most relevant biases—sampling bias, measurement bias, social desirability bias and response bias—is therefore essential. Setting up diverse research teams with both technical and market research skills helps to combine these perspectives and allows for the development of successful use cases, especially for subjective training tasks that aim to capture relevant marketing metrics in a specific marketing context.
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