IBM’s New Open-Source Tool Assist Businesses In Identifying Advertising Bias

Algorithmic bias is a well-known issue in which unjust human assumptions and judgments are frequently encoded into algorithms, resulting in unfair targeting of specific populations. It’s no surprise, however, that IBM proved that unwanted bias is an issue in digital advertising through a recent study.

IBM researchers investigated the topic of bias in advertising and approaches to minimize it using the IBM-developed AI Fairness 360 (AIF360) toolset, which Big Blue submitted to a Linux Foundation initiative in 2020. The AI Fairness 360 toolkit is an open-source, extendable library that contains strategies created by the research community to detect and eliminate bias in machine learning models at all stages of the AI application lifecycle.

In addition, the company is making available its free Advertising Toolkit for AI Fairness 360, with 75 fairness measures and 13 algorithms for detecting and mitigating biases in discrete data sets. For simplicity of use, it also contains a playbook and example code. The toolkit is intended to assist businesses in better assessing the prevalence and impact of prejudice in their advertising efforts and the demographics of their target consumers.

IBM is also encouraging firms and groups to sign its Advertising Fairness Pledge as part of its more prominent efforts to address the problem. The American Association of Advertising Agencies (4As), the Interactive Advertising Bureau (IAB), and the Ad Council have all pledged to do something.

As the research team stated, data is an incredibly powerful tool that can help businesses tailor customer interactions and uncover the most relevant touchpoints. However, they are also aware of the potential for bias to exist within algorithms or technology. That’s why they work closely with their clients to help them determine how and when to use data effectively. Consumers have every right to expect businesses to treat their data fairly. And it’s up to the entire industry to come together and combat data bias. Doing so will ultimately lead to higher engagement and commercial outcomes.