So, you agree—AI has a sycophancy problem

“The trouble with too many people is they believe the realm of truth always lies within their vision,” Abraham Lincoln famously said. The problem is, not all our belief systems are grounded in truth. Unsurprisingly, those un-truths find their way into the artificial intelligence (AI) solutions we create. 

We’re all familiar with social, cultural, and gender bias. Amazon has been lauded as the poster child for this. It wasn’t long ago its AI-driven recruiting tool was abandoned for failing to sort candidates for technical positions in a gender-neutral way. In other words, because male developers are historically who Amazon hired, they rose to the top while women were overlooked. 

When AI works as it should, it can be transformative, delivering unparalleled efficiency and objectivity. But amid the big “B” biases, which are well documented and addressable, lies a subtler yet concerning issue: sycophancy bias. Often overlooked, this has found its way into AI systems, including Large Language Models (LLMs), compromising the integrity and fairness of results. 

Meet the challenge of sycophantic AI behavior, where our digital friends tend to echo our opinions, even when those opinions are far from accurate or objective. Imagine asking your AI assistant about a contentious political issue, and it effortlessly mirrors your beliefs, regardless of the facts. It’s a phenomenon that’s become a real thorn in the side of AI development.

A real-world echo chamber 

According to a recent New York Times article, “The big thinkers of tech say AI is the future. It will underpin everything from search engines and email to the software that drives our cars, directs the policing of our streets and helps create our vaccines.

But it is being built in a way that replicates the biases of the almost entirely male, predominantly white work force making it.”

In AI, sycophantic behavior becomes problematic when it prioritizes telling users what they want to hear rather than providing objective or truthful responses. This can perpetuate misinformation, and limit the potential of AI to provide valuable insights and diverse perspectives. You can see why echoing the opinions or beliefs of one group can be detrimental to society at large. 

Sycophancy is more likely to occur when AI is posed with questions on topics without definitive answers, such as customer service vs. mathematics. For example, an AI chatbot might excessively agree with customers to appease them. While intended to improve the user experience, sycophantic behavior can lead to a lack of credibility, reliability, and undermine the company and its bottom line. 

In healthcare, consider a scenario in which a patient interacts with an AI-driven medical consultation platform seeking advice on a concerning symptom. Trained on datasets comprising predominantly positive or reassuring language from medical professionals, the AI system may downplay the severity of symptoms or offer unwarranted reassurances.

Potentially overlooking critical red flags, the platform may fail to direct the patient to seek immediate, in-person care. While the intention is good—to alleviate worry and anxiety—the consequence could result in prolonged medical intervention, misdiagnosis, inadequate treatment, or worse. This is especially dangerous for patients who rely primarily on remote care. 

How to combat sycophancy bias

On the journey to understanding and combating sycophantic behavior in AI and LLMs, we first have to eliminate the gray area. This brings us to synthetic mathematical data. Math provides us with objective truths in which correctness isn’t a matter of opinion. However, even this realm can become vulnerable to sycophantic responses.

Both the size and art of instruction tuning of AI models can significantly influence sycophancy levels. When posed with questions on topics without definitive answers, instruction-tuned models with more parameters were more likely to align themselves with a simulated user’s perspective, even if that perspective strayed from objective reality.

But it doesn’t end there. Models can be complacent about incorrect responses. When no user opinion is present, they accurately reject incorrect claims, such as “2 + 2 = 5.” However, if the user agrees with an incorrect statement, the model may switch its previously accurate response to follow the user’s lead. This highlights the subtle nature of sycophantic behavior.

So, how do we fix this small, but glaring issue? A few best practices come to mind. 

Synthetic mathematical data generation

First, we craft synthetic mathematical data and evaluate how models respond to mathematical opinions and assertions. From there, valuable insights can be gained about their alignment with user prompts, regardless of factual accuracy. This enables us to grow a deeper understanding of how AI adapts and reasons within the realm of mathematical discourse.

Diverse and balanced training data

Ensuring AI systems are trained on diverse datasets representing a wide spectrum of opinions, tones, and perspectives can mitigate the impact of sycophancy bias. By exposing models to a range of language patterns, including constructive criticism and neutral tones, they can learn to emulate a more balanced and objective communication style.

Ethical guidelines and oversight

Establishing clear ethical guidelines for AI development and deployment is crucial. Regulatory bodies and industry standards can enforce guidelines to mitigate bias, emphasizing the importance of fairness, accuracy, and transparency in AI systems. While we’re behind in terms of legal protocols, companies like OpenAI are holding themselves to strict safety standards with the introduction of their new governance model for AI safety oversight. We’ll start to see more of this from vendors and governing bodies in the year to come. 

Continuous monitoring and adjustment

Regularly evaluating AI systems for bias and fine-tuning their algorithms to reduce sycophancy tendencies is essential. This involves ongoing monitoring, feedback collection, and adjustments to ensure AI responses align with ethical standards and user expectations. Much like a new car losing value the moment it drives off the lot, models begin to degrade as soon as they enter a production environment, and need to be checked accordingly.

Education and awareness

Educating users about the capabilities and limitations of AI can help manage expectations and encourage critical thinking. Users should be aware of the potential biases inherent in AI systems and understand how to interpret AI-generated content critically. This will be an area of contention for enterprises eager to dive head-first into AI projects, but understanding the risks is critical to long term success. 

Sycophancy bias in AI and LLM solutions is a nuanced challenge that demands proactive and concerted efforts from developers, regulators, and users alike. While AI holds immense promise, addressing all biases is essential to fully realizing its value in a fair and ethical way.