In a busy hospital, a radiologist is using an artificial intelligence system to help her diagnose medical conditions based on patients’ X-ray images. Using the AI system can help her make faster diagnoses, but how does she know when to trust the AI’s predictions?
She doesn’t. Instead, she may rely on her expertise, a confidence level provided by the system itself, or an explanation of how the algorithm made its prediction — which may look convincing but still be wrong — to make an estimation.
To help people better understand when to trust an AI “teammate,” MIT researchers created an onboarding technique that guides humans to develop a more accurate understanding of those situations in which a machine makes correct predictions and those in which it makes incorrect predictions.
By showing people how the AI complements their abilities, the training technique could help humans make better decisions or come to conclusions faster when working with AI agents.
Continue reading: https://news.mit.edu/2022/ai-predictions-human-trust-0119
She doesn’t. Instead, she may rely on her expertise, a confidence level provided by the system itself, or an explanation of how the algorithm made its prediction — which may look convincing but still be wrong — to make an estimation.
To help people better understand when to trust an AI “teammate,” MIT researchers created an onboarding technique that guides humans to develop a more accurate understanding of those situations in which a machine makes correct predictions and those in which it makes incorrect predictions.
By showing people how the AI complements their abilities, the training technique could help humans make better decisions or come to conclusions faster when working with AI agents.
Continue reading: https://news.mit.edu/2022/ai-predictions-human-trust-0119
