Explainable Artificial Intelligence (XAI) has re-emerged in response to the development of modern AI and ML systems. These systems are complex and sometimes biased, but they nevertheless make decisions that impact our lives. XAI systems are frequently algorithm-focused; starting and ending with an algorithm that implements a basic untested idea about explainability. These systems are often not tested to determine whether the algorithm helps users accomplish any goals, and so their explainability remains unproven. We propose an alternative: to start with human-focused principles for the design, testing, and implementation of XAI systems, and implement algorithms to serve that purpose. In this paper, we review some of the basic concepts that have been used for user-centered XAI systems over the past 40 years of research. Based on these, we describe the "Self-Explanation Scorecard", which can help developers understand how they can empower users by enabling self-explanation. Finally, we present a set of empirically-grounded, user-centered design principles that may guide developers to create successful explainable systems.

Current discussions of "Explainable AI" (XAI) do not much consider the role of abduction in explanatory reasoning (see Mueller, et al., 2018). It might be worthwhile to pursue this, to develop intelligent systems that allow for the observation and analysis of abductive reasoning and the assessment of abductive reasoning as a learnable skill. Abductive inference has been defined in many ways. For example, it has been defined as the achievement of insight. Most often abduction is taken as a single, punctuated act of syllogistic reasoning, like making a deductive or inductive inference from given premises. In contrast, the originator of the concept of abduction---the American scientist/philosopher Charles Sanders Peirce---regarded abduction as an exploratory activity. In this regard, Peirce's insights about reasoning align with conclusions from modern psychological research. Since abduction is often defined as "inferring the best explanation," the challenge of implementing abductive reasoning and the challenge of automating the explanation process are closely linked. We explore these linkages in this report. This analysis provides a theoretical framework for understanding what the XAI researchers are already doing, it explains why some XAI projects are succeeding (or might succeed), and it leads to design advice.

This issue of AI Magazine includes six articles on cognitive orthoses, which we broadly conceive as technological approaches that amplify or enhance individual or team cognition across a wide range of goals and activities. The articles are grouped by how they relate to orthoses enhanced socio-technical team intelligence at three different cognitive levels--sensorimotor physical, professional learning, and networked knowledge.

This issue of AI Magazine includes six articles on cognitive orthoses, which we broadly conceive as technological approaches that amplify or enhance individual or team cognition across a wide range of goals and activities. The articles are grouped by how they relate to orthoses enhanced socio-technical team intelligence at three different cognitive levels—sensorimotor physical, professional learning, and networked knowledge.

We present an emerging research project in our laboratory to extend ambient intelligence (AmI) by what we refer to as “extreme personalization” meaning that an instance of ambient intelligence is focused on one or at most a few individuals over a very long period of time. Over a lifetime of co-activity, it senses and adapts to a person’s preferences and experiences, and crucially, his or her (changing) special needs; needs that differ significantly from the normal baseline. We refer to our agent-based cyber-physical system as Ambient Personal Environment eXperiment (APEX). It aims to serve as a Companion , a Coach , and a Caregiver : crucial support for individuals with mental, physical, and age-related disabilities and those other people who help them. We propose that an instance of APEX, interacting socially with each of these people, is both a social actor as well as a cyber-human prosthetic device . APEX is an ambitious integration of multiple technologies from Artificial Intelligence (AI) and other disciplines. Its successful development can be viewed as a grand challenge for AI. We discuss in this paper three research thrusts that lead toward our vision:  robust intelligent agents, semantically rich human-machine interaction, and reasoning from comprehensive multi-modal behavior data.

Teamwork requires mutual trust among team members. Establishing and maintaining trust depends upon alignment of mental models, an aspect of shared awareness. We present a theory of how maintenance of model alignment is integral to fluid changes in relative control authority (i.e., adaptive autonomy) in human-robot teamwork.

Good books, well conceived, well written, and well presented, can do much to promote the science of AI and the AAAI organization. The AAAI Press edited collections, from which the articles of this issue are excerpted, are designed to reach out to an audience that wants to learn more about AAAI and AI.

The Spring Symposium on Knowledge-based Environments for Teaching and Learning focused on the use of technology to facilitate learning, training, teaching, counseling, coaxing and coaching.

The Spring Symposium on Knowledge-based Environments for Teaching and Learning focused on the use of technology to facilitate learning, training, teaching, counseling, coaxing and coaching. Sixty participants from academia and industry assessed progress made to date and speculated on new tools for building second generation systems.