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Understanding Bias in AI: What Is Your Role, and Should You Care?

Understanding Bias in AI: What Is Your Role, and Should You Care?

There are billions of people around the world whose online experience has been shaped by algorithms that utilize artificial intelligence (AI) and machine learning (ML). Some form of AI and ML is  employed almost every time people go online, whether they are searching for content, watching a video, or shopping for a product. Not only do these technologies increase the efficiency and accuracy of consumption but, in the online ecosystem, service providers innovate upon and monetize behavioral data that is captured either directly from a user’s device, a website visit or by third parties.

Advertisers are increasingly dependent on this data and the algorithms that adtech and martech employ to understand where their ads should be placed, which ads consumers are likely to engage with, which audiences are most likely to convert, and which publisher should get credit for conversions

Additionally, the collection and better utilization of data helps publishers generate revenue, minimize data risks and costs, and provide relevant consumer-preference-based audiences for brands.

However, consumers and policymakers have been concerned in recent years about how AI algorithms and the biases they often contain might impact society. As such, advertisers and publishers are concerned about risk exposure, the ability to reach and scale audiences, potential negative impact on reputation, sales and revenue, as well as the possibility of legal action due to non-compliance with laws and regulations, data privacy issues, or lack of AI governance.

For AI systems to perform efficiently and effectively, a fastidious approach to design, development, deployment, and maintenance must be led by a diverse set of subject matter experts. Identifying and addressing unwanted or unintentional bias is mission critical. The key is to deploy successful AI systems while increasing our understanding of which biases impact models or are embodied in algorithms. By not addressing the potential for systemic advantage or disadvantage, unwanted or unintentional bias could affect performance, potentially exacerbating societal inequities and eroding trust.

The advertising industry will continue to transform for the foreseeable future, including third-party cookies and identifiers disappearing and new regulations taking effect. This disruption has invited AI as a technology to help define the next generation of advertising. To achieve success, we must establish practices that help reduce bias in the technologies used across the digital advertising and marketing landscape.

A great deal more than just trust is at stake for the industry; including the companies involved, the engaged consumers and the totality of global commerce. Thus, it is crucial now more than ever to define best practices to minimize bias in AI-driven solutions while continuing to expand on diversity, equity, and inclusion across our practices.

The Interactive Advertising Bureau (IAB), the national trade association for the digital media and marketing industries, has long acknowledged that it’s crucial to develop AI standards, best practices, use cases, and terminologies in an effort to scale AI and enable the industry to its full potential.

In its second editorial release of 2021, the IAB’s AI Standards Working Group published “Understanding Bias in AI for Marketing: A Comprehensive Guide to Avoiding Negative Consequences with Artificial Intelligence”. AI and ML thought leaders and expert practitioners from top publishers, advertising agencies, and ad tech companies have collaborated to produce this paper to provide insight for business executives, technologists, legal and compliance officers, and platform users to understand their responsibilities in process development, deployment, and ongoing management of an AI-driven solution.

The guide’s unique insights are derived from conversations about real-world challenges faced daily by top-tier companies. Today’s future-thinking marketing and advertising technology leaders should be leaning into the development of their own processes and approaches.

Use this guide to get started and make advertising better.

Download the Guide

Authors

Author
Angelina Eng
VP, Measurement & Attribution, IAB Programmatic+Data Center
at IAB

Author
David Olesnevich
Head of Ad Product
at IBM Watson Advertising