Avoiding Bias Traps in Gen AI with Red Teaming

Bias in AI models is increasingly recognized as a complex issue with broad implications for businesses and their customers. As AI technologies become commonplace in sectors from retail to public services, inherent biases can lead to skewed analytics, inappropriate decisions and reinforced societal disparities.

An effective way to uncover and mitigate biases before they cause harm is a practice known as red teaming. Originally employed in cybersecurity, red teaming aims to expose potential system failures before they become real problems. It involves a group of testers mimicking hacker behavior to uncover points of weakness. Recently, the approach has been copied by AI developers as a method for uncovering bias in large language models (LLMs).

Understanding AI Bias

Bias in AI is not normally caused by flaws in algorithms. It is caused by the data used to train the models, which inherently harbors the unconscious societal biases of the humans who created it. Often, this bias is exacerbated by poor data sampling strategies that fail to reflect the diversity of the customers the AI is intended to serve. As Meta’s Chief Scientist, Yann LeCun, famously pointed out on Twitter (now X), if you train a model based on images of people of a specific race, then the model will be biased in favor of this race. It is that simple.

Facial recognition technologies are a poignant example. These often misidentify individuals from certain demographic groups at disproportionately high rates because they are trained primarily using images of hegemonic groups. Such biases not only undermine public trust but can also lead to significant legal and legal repercussions for companies that deploy biased AI systems.

Our Precarious Relationship with Trust

Customer trust is a fundamental pillar of any business that is hard to build and easy to break. This vulnerability underscores the essential need for the intentional testing and validation of AI applications to inspire customer confidence in their fairness and reliability.

Red teaming can be an effective technique for testing bias in generative large language models. Traditionally, red teaming’s focus in cybersecurity was breach prevention, i.e. proactively surfacing system vulnerabilities. When applied to Gen AI, red teaming experts probe vulnerabilities such as, but not limited to, bias in applications and foundation models that could result in unfair or even harmful outcomes for users. As Gen AI technologies increasingly influence critical decision-making processes in sectors such as healthcare, finance, government and law, the work of red teams will be indispensable.

Diversity and Scale: Key Components of Effective Red Teaming

Social bias is often defined as discrimination either in favor of or against a person, group or set of beliefs in a way that is prejudicial or unfair. Discrimination due to race, religion and gender are common examples of social bias, though it can also be directed at groups traditionally stigmatized by society, such as the elderly or people suffering from illnesses.

A crucial requirement of effective AI red teaming focused on surfacing bias is diversity among the team members. Teams comprising individuals from varied backgrounds have a higher chance of identifying biases that might not be evident to a more homogenous group. Testers should be recruited based on demographic characteristics in addition to testing expertise. This is the only way companies developing Gen AI can ensure their product provides helpful outputs for a broad user base.

The scale of testing is equally as important. Due to the probabilistic nature of many AI models, extensive testing across a wide range of scenarios is necessary to catch outliers and ensure that systems perform well under diverse conditions. Again, red teams are well-placed to meet this need, as more testers can be recruited depending on the project scope.

Case Studies and Real-World Applications

Industries ranging from automotive to telecommunications have adopted red teaming to refine their AI systems. Applause’s 2023 State of Digital Quality in Media & Telco report, for example, found that telecommunications companies regularly deploy diverse testing teams to uncover and address defects across digital platforms.

From our work with various companies developing Gen AI, it is clear that effective red teaming combines automated tools with human oversight. Automation can provide red teams with a range of inputs to test AI responses at a scale in a way that would be unfeasible for humans alone. That said, human intuition and understanding are irreplaceable when it comes to evaluating the subtleties of AI behavior and language outputs.

Challenges and Considerations

Implementing a robust red teaming strategy brings challenges. These range from logistical issues, such as assembling teams with the right mix of skills and backgrounds, to technical barriers in simulating realistic and diverse scenarios. There are also legal considerations, such as ensuring private or sensitive data is handled responsibly in line with local regulations.

For many companies developing Gen AI, the logistical challenges can be a barrier to getting started. The greatest hurdle is often recruiting a group of diverse testers large enough to ensure adequate representation of the entire user base, especially for global products.

Red teaming is an indispensable strategy for organizations aiming to deploy unbiased and trustworthy AI systems. By devising a thorough red teaming approach that takes team diversity seriously, companies can better anticipate and mitigate potential biases, thereby safeguarding their reputation and ensuring that their AI systems serve all users equitably.

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