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Artificial Intelligence (AI) and Machine Learning (ML) algorithms and decisions have many ways to be woven despite the best efforts to identify and root out bias. It can be buried in the data used to generate algorithms, arising from the training process itself or how the algorithm is used to make decisions.
In 2018, IBM launched AI Fairness 360, an open-source toolkit for checking and minimizing bias in datasets and ML models, and later added support for measuring uncertainty. The tool has improved the fairness of housing loans, insurance and medical decisions.
IBM’s new open-source advertising toolkit for AI Fairness hopes to do the same for the advertising industry. Consumers do not fight for approval for better advertisements in the same way they would for better mortgage rates or medical procedures.
“It’s all about integrating bias detection and mitigation tools into real meat core marketing and advertising technology,” said Bob Lord, VentureBeat, IBM senior vice president of weather company and affiliation.
Statista estimates that companies spent $ 764 billion on advertising in 2021, and expects that figure to exceed $ 1 trillion by 2026. Finding and reducing better biases can help companies, nonprofits, and governments get more value from their advertising costs across different groups. It can also help improve social health decisions.
Advertising gets tech
“The bias that exists in advertising is historically based on how we market,” Lord said. It starts with how the data scientist models the data segment and model customers. Now the ad industry is going through the convergence of marketing and technology. “We’ve gotten really good at the advertising industry targeting people,” Lord said. But in the process of targeting people with new ML algorithms, advertisers have also sub-optimized results for specific groups.
For example, IBM worked with the Ad Council on a project to understand the impact of bias in the algorithm-driven COVID-19 vaccine education campaign. The system, consisting of 108 different creative variations selected by algorithms, dynamically generated more than 10 million ad impressions. Over time, the system optimized ads for women aged 45-65 who clicked through 32 times more than the average.
This may be the best outcome for the new handbag accessory, but it was the best for improving the COVID-19 awareness for other demographics. “Prejudice is not intentional,” God explained. “It’s hidden in technology, and we can’t see it because we don’t have the bias-detection technology in machines yet.”
Lord’s team has already integrated this technology into AI and ML development workflows for mortgage applications and insurance underwriting. Today they are working with some fast-service companies to analyze after-sales marketing campaigns. “I hope that one year from now, we will be able to build this technology from scratch,” Lord said.
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