Algorithms are at the heart of marketing and martech and are used for data analysis, data collection, audience segmentation and more. AI systems are built on them, but this comes with its own risks. Bias can be built into artificial intelligence through the values of its creator and the data it is trained on. For example, facial recognition systems are trained on sets of images of mostly lighter-skinned people, leading to a lack of recognition of darker-skinned people. ChatGPT, Google’s Bard and other language models use deep learning and are trained on huge data sets, which may contain error, disinformation and bias.
Mitigating bias is essential for marketers who want to work with the best possible data. Marketers and martech companies should focus on applying this to the training data that goes in so that the model has fewer biases to start with that need to be mitigated later. There are tools to help with this, such as What-If from Google, AI Fairness 360 from IBM, Fairlearn from Microsoft, Local Interpretable Model-Agnostic Explanations (LIME) and FairML from MIT’s Julius Adebayo.
Organisations should also ensure their DEI initiatives inspect the outputs of the models to ensure there is no bias. This can be done by having the diversity team give their stamp of approval to the model. How companies define and mitigate against bias in these systems will be significant markers of its culture, and each organisation must develop their own principles about how they develop and use this technology.
Originally reported by Martech: https://martech.org/bias-in-ai-chatgpt-marketing-data/
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