Artificial intelligence must know when to ask for human help

Sarah Scheffler, Clare Boothe Luce Graduate Fellow in the Department of Computer Science at Boston University, studies the intersection of law and technology and co-authored an article on artificial intelligence that describes how “an AI algorithm working together with a human can reap the benefits and efficiency of the AI’s good decisions, without being locked into its bad ones.”


Artificial intelligence systems are powerful tools for businesses and governments to process data and respond to changing situations, whether on the stock market or on a battlefield. But there are still some things AI isn’t ready for.

We are scholars of computer science working to understand and improve the ways in which algorithms interact with society. AI systems perform best when the goal is clear and there is high-quality data, like when they are asked to distinguish between different faces after learning from many pictures of correctly identified people.

Sometimes AI systems do so well that users and observers are surprised at how perceptive the technology is. However, sometimes success is difficult to measure or defined incorrectly, or the training data does not match the task at hand. In these cases, AI algorithms tend to fail in unpredictable and spectacular ways, though it’s not always immediately obvious that something has even gone wrong. As a result, it’s important to be wary of the hype and excitement about what AI can do, and not assume the solution it finds is always correct.

When algorithms are at work, there should be a human safety net to prevent harming people. Our research demonstrated that in some situations algorithms can recognize problems in how they’re operating, and ask for human help. Specifically, we show, asking for human help can help alleviate algorithmic bias in some settings.

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