Previous work showed that despite claims of interpretability, humans are unable to use formal specifications presented in a variety of ways to validate even simple robot behaviors.

Previous work showed that despite claims of interpretability, humans are unable to use formal specifications presented in a variety of ways to validate even simple robot behaviors.

As learned control policies become increasingly common in autonomous systems, there is increasing need to ensure that they are interpretable and can be checked by human stakeholders. Formal specifications have been proposed as ways to produce human-interpretable policies for autonomous systems that can still be learned from examples. Previous work showed that despite claims of interpretability, humans are unable to use formal specifications presented in a variety of ways to validate even simple robot behaviors. This work uses active learning, a standard pedagogical method, to attempt to improve humans' ability to validate policies in signal temporal logic (STL). Results show that overall validation accuracy is not high, at 65\% \pm 15% (mean \pm standard deviation), and that the three conditions of no active learning, active learning, and active learning with feedback do not significantly differ from each other.

A few years ago, we witnessed the heralding of the imminent rise of artificial intelligence (AI) to support medical information services provided through industry-based contact centers. And, in some cases, the message Lonnie Corant Jaman Shuka Rashid Lynn (better known as Common) shares in recent Microsoft commercials is our reality: we do have more power at our fingertips than generations before us. But witnessing the rise of AI and actually experiencing it are two different realities. In retrospect, for medical information services these emerging technologies were overhyped when first introduced to the market in terms of how feasibly they could be applied in practical applications capable of creating a positive return on investment. Many larger companies invested in machine learning'pilot' programs that appeared to produce less than positive outcomes, and the term "over innovating" entered the corporate lexicon.