AI Knows More Than You. But Can You Trust It?

The Psychology of Algorithm Aversion and How AI Can Enhance Judgmental Decision-Making

Artificial intelligence (AI) is transforming business decision-making, offering unprecedented speed and accuracy in areas like forecasting, risk assessment, and operational planning. Yet, when faced with a choice between an AI-generated prediction and human judgment, many decision-makers still favor human expertise, even when the AI is demonstrably more accurate.

This paradox raises an important question: 

If AI consistently outperforms human intuition in many analytical tasks, why do we instinctively distrust it when it makes even a small mistake? 

The answer lies in algorithm aversion, a well-documented psychological phenomenon that affects how people perceive and react to AI-driven recommendations.

The AI Trust Paradox

Algorithm aversion occurs because people apply different standards of reliability to AI and human decision-making. When humans make mistakes, we tend to rationalize them as understandable, attributable to fatigue, lack of information, or the complexity of the situation. In contrast, when AI makes an error, it is often perceived as a fundamental failure of the entire system.

In their brilliant article in the renowned International Journal of Forecasting, Dr Nigel Harvey and Dr Shari De Baets, demonstrate that people initially trust AI over human judgment when no feedback is given. However, as soon as they start seeing AI's mistakes (even if they are infrequent) they quickly shift their preference back to human judgment, even when AI continues to perform better on average. This response suggests that people hold AI to an unrealistic standard of perfection. Another key factor influencing trust in AI is labeling bias, as demonstrated in their experiment: when the same forecast was presented as being generated by an algorithm, people trusted it less than when they believed it came from a human expert. This means that AI adoption in business is not just a technical challenge but also a communication and perception challenge.

The Role of Big Data and AI in Judgmental Decision-Making

While AI has proven to be highly effective in generating forecasts and predictions, human judgment still plays a critical role in interpreting and refining those insights. My recent study explores how integrating social media sentiment analysis with human judgment can significantly improve forecasting accuracy for new products. Traditional forecasting methods rely heavily on historical sales data, which makes them incomplete to use for forecasting new product launches or under dynamic market conditions. By analyzing data from social media and other external sources, AI could potentially help businesses detect these patterns in real time, allowing for more informed forecast adjustments. This approach could be particularly valuable for industries with short product life cycles, such as fashion, consumer electronics, and food and beverage.

AI is Powerful, But Humans Should Always Be in Control

Every new technology goes through a transition phase. We test it, learn its strengths, forgive its errors, but also ensure that we don’t enable it more than we should. AI is no different. It is an incredible tool, one that can process vast amounts of data, identify patterns we might overlook, and help us see blind spots in our decision-making. But in the end, who should always have the final say? Humans.

AI’s greatest value isn’t in making decisions for us—it’s in helping us make better ones. 

It can provide a global perspective where ours might be narrow. It can challenge our intuition when we rely too much on gut feeling. It catches patterns and inconsistencies that human bias might overlook. AI has the potential to elevate decision-making, but only when we remain actively involved in the process. The real danger isn’t that AI will take over decision-making. The real danger is that we, as humans, may not fully understand how to use it properly. AI is only as effective as the humans interpreting its output. It can provide an analytical advantage, but understanding when to trust it, when to challenge it, and when to override it is a skill we need to develop.

The key question is not whether AI will replace human decision-making, but whether we will take the time to understand it well enough to use it effectively, ethically, and strategically. Those who do will lead the future. Those who don’t risk being led by the very tools they created.

As AI continues to shape industries, what do you think is the biggest challenge in ensuring it remains a tool for better decision-making rather than a replacement for human judgment?