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IBM's new open source tools help companies spot bias in advertising

It's easy for bias to permeate marketing campaigns when algorithms are used to target certain audiences; IBM is urging corporations to do something about it.
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Written by Stephanie Condon, Senior Staff Writer on
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Algorithmic bias is a widely acknowledged problem -- unfair human assumptions and judgments are often encoded into algorithms that result in unfair targeting of certain groups. It's no surprise, then, that IBM was able to confirm with recent research that unwanted bias is a problem in digital advertising. 

Using the IBM-developed AI Fairness 360 (AIF360) toolkit, which Big Blue contributed to a Linux Foundation project in 2020, IBM researchers studied the problem of bias in advertising and ways to mitigate the problem. Now, the company is releasing its free Advertising Toolkit for AI Fairness 360, an open-source tool with 75 fairness metrics and 13 algorithms to help identify and mitigate biases in discrete data sets. It also includes a playbook and sample code for ease of use. The toolkit is designed to help organizations gain a better understanding of the presence and impact of bias on their ad campaigns, as well as the makeup of their audiences.

As part of its larger efforts to tackle the issue, IBM also is urging companies and organizations to sign its Advertising Fairness Pledge. On Monday, Delta Air Lines, along with the marketing giant Mindshare and its parent company, WPP, committed to join the cause. The American Association of Advertising Agencies (4As), IAB (Interactive Advertising Bureau) and Ad Council also committed to take action. 

"Used correctly, data can help brands personalize consumer engagement and identify the most relevant touchpoints," WPP CEO Mark Read said in a statement. "However, we know that bias can exist in algorithms or technology, and that's why we're helping our clients to evaluate how and when to use data in a meaningful way. ... Consumers rightly expect brands to use their information in a fair way and for the industry to tackle data bias collectively, which can ultimately result in increased engagement and commercial outcomes."

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