AI Systems Fail to Meet Interpretability Claims, According to MIT Researchers
A team of MIT Lincoln Laboratory researchers has dealt a blow to claims that formal specifications can make AI systems more interpretable to humans. In an experiment, participants were tasked with validating an AI agent’s plan in a virtual game using formal specifications. However, the participants were correct less than half of the time, leading the researchers to conclude that formal methods do not lend interpretability to systems. The researchers warn that claims of interpretability need to be subject to more scrutiny.
Interpretability is crucial for humans to trust AI systems in real-world applications. If an AI system can explain its decisions, humans can determine whether it needs adjustments or can be trusted to make fair choices. However, interpretability has long been a challenge in the field of AI and autonomy. Machine learning processes often occur in a “black box,” making it difficult for developers to explain why a system made a particular decision.
The experiment conducted by the MIT researchers aimed to determine whether formal specifications could enhance the interpretability of a system’s behavior. Participants were given formal specifications and were asked to validate whether the system met the user’s goals. However, regardless of how the specifications were presented – as raw logical formulas, translated natural language, or decision-tree formats – the validation performance was poor, with an accuracy rate of about 45%.
The results showed that people tended to over-trust the correctness of the specifications placed before them, indicating a confirmation bias. Even experts in formal specifications only slightly outperformed novices. The researchers emphasize that while formal specifications should not be abandoned as an explanation tool for system behaviors, more work is needed to design how they are presented to people and how people use them.
The MIT team hopes their findings will prompt the formal logic community to consider what may be missing when claiming interpretability and how such claims can play out in the real world. They stress the importance of human evaluations of autonomy and AI concepts to avoid making unfounded claims about their utility.
What are your thoughts on the interpretability of AI systems? Do you believe it’s necessary for AI to explain its decisions? Share your opinions in the comments!
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