Explanatory debugging: Supporting end-user debugging of machine-learned programs

Many machine-learning algorithms learn rules of behavior from individual end users, such as task-oriented desktop organizers and handwriting recognizers. These rules form a “program” that tells the computer what to do when future inputs arrive. Little research has explored how an end user can debug these programs when they make mistakes. We present our progress toward enabling end users to debug these learned programs via a Natural Programming methodology. We began with a formative study exploring how users reason about and correct a text-classification program. From the results, we derived and prototyped a concept based on “explanatory debugging”, then empirically evaluated it. Our results contribute methods for exposing a learned program's logic to end users and for eliciting user corrections to improve the program's predictions

You are the only possible oracle: Effective test selection for end users of interactive machine learning systems

How do you test a program when only a single user, with no expertise in software testing, is able to determine if the program is performing correctly? Such programs are common today in the form of machine-learned classifiers. We consider the problem of testing this common kind of machine-generated program when the only oracle is an end user: e.g., only you can determine if your email is properly filed. We present test selection methods that provide very good failure rates even for small test suites, and show that these methods work in both large-scale random experiments using a “gold standard” and in studies with real users. Our methods are inexpensive and largely algorithm-independent. Key to our methods is an exploitation of properties of classifiers that is not possible in traditional software testing. Our results suggest that it is plausible for time-pressured end users to interactively detect failures—even very hard-to-find failures—without wading through a large number of successful (and thus less useful) tests. We additionally show that some methods are able to find the arguably most difficult-to-detect faults of classifiers: cases where machine learning algorithms have high confidence in an incorrect result

An exploratory study to design constrained engagement in smart heating systems

Smart heating systems that leverage complex models of user preferences and energy consumption within the home and the wider network in order to make intelligent heating decisions have started to be adopted in homes. While heating systems that allow the user to directly manipulate the heating schedule and temperature have been investigated in some detail, little is known about how to strike a balance between encouraging users to interact with the system but not to demand too much of their attention, what research has termed "constrained engagement" with calm technology. In this exploratory study, we investigated how participants responded to a number of scenarios involving a novel smart heating system in order to support controllability, intelligibility and user experience as part of a constrained engagement approach. We focused in particular on when participants wanted to engage with the smart heating system and how explanations from the system could influence user engagement. Our study contributes a better understanding of users' expectations towards smart heating systems that can form the basis of improved user interfaces