Designing AI Support for Human Involvement in AI-assisted Decision Making: A Taxonomy of Human-AI Interactions from a Systematic Review

Efforts in levering Artificial Intelligence (AI) in decision support systems have disproportionately focused on technological advancements, often overlooking the alignment between algorithmic outputs and human expectations. To address this, explainable AI promotes AI development from a more human-centered perspective. Determining what information AI should provide to aid humans is vital, however, how the information is presented, e. g., the sequence of recommendations and the solicitation of interpretations, is equally crucial. This motivates the need to more precisely study Human-AI interaction as a pivotal component of AI-based decision support. While several empirical studies have evaluated Human-AI interactions in multiple application domains in which interactions can take many forms, there is not yet a common vocabulary to describe human-AI interaction protocols. To address this gap, we describe the results of a systematic review of the AI-assisted decision making literature, analyzing 105 selected articles, which grounds the introduction of a taxonomy of interaction patterns that delineate various modes of human-AI interactivity. We find that current interactions are dominated by simplistic collaboration paradigms and report comparatively little support for truly interactive functionality. Our taxonomy serves as a valuable tool to understand how interactivity with AI is currently supported in decision-making contexts and foster deliberate choices of interaction designs

Characterizing argumentation structure within the asynchronous, online communication of novice engineering design students

textPracticing argumentation in secondary school classrooms benefits students both in terms of learning how to argue and learning the course material at hand. Amidst the onset and growth of engineering design courses in secondary schools, this dissertation is an exploratory case study to characterize the use of argumentation among novice student engineering designers. The setting is a high school robotics class. Specifically, a group of students from one class section teamed up with a group of students from a separate class section to design and build a single robot. The team members communicated online via a shared, editable document. That text is the primary data set for my analysis. I looked for indications of argumentation structure that emerged from the online discussion, given that, to my knowledge, the students had not been taught argumentation strategies, per se. Engineering design is relatively new to secondary school, so I thought it appropriate to develop a baseline—a case study that reveals how students communicate about their designs when left largely to their own devices. This study may inform the development argumentation scaffolds that support the students’ existing strengths while ameliorating their weaknesses. My analytical supposition was that argumentation in design will take the form of resolving differences of opinion toward the creation of a single design. Hence, I used Pragma-dialectic theory as my analytical framework. It is a broad theory, based upon resolving differences of opinion in everyday conversation. As such, Pragma-dialectic theory may also be able to encompass the idiosyncrasies of team design, such as reliance on intuition and experience, as well as the important roles that designed objects play throughout the process. Taken together, the importance of intuition, experience, and objects suggests multiple modes of communication that ought to be considered arguments within design deliberations. Results suggest that the students worked to resolve differences of design opinions. In doing so, the students relied heavily on their designed objects to make their arguments meaningful. I classified five object-based claims which emerged from the students’ discussions: keystone, tinkering, visual, tactile, and counterfactual. These form the beginnings of a theory of object-based argumentation.Science, Technology, Engineering, and Mathematics Educatio

Usability Study of a Control Framework for an Intelligent Wheelchair

We describe the development and assessment of a computer controlled wheelchair called the SMARTCHAIR. A shared control framework with different levels of autonomy allows the human operator to stay in complete control of the chair at each level while ensuring her safety. The framework incorporates deliberative motion plans or controllers, reactive behaviors, and human user inputs. At every instant in time, control inputs from these three different sources are blended continuously to provide a safe trajectory to the destination, while allowing the human to maintain control and safely override the autonomous behavior. In this paper, we present usability experiments with 50 participants and demonstrate quantitatively the benefits of human-robot augmentation

Principles and Guidelines for Evaluating Social Robot Navigation Algorithms

A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation has advanced tremendously in recent years, the fair evaluation of algorithms that tackle social navigation remains hard because it involves not just robotic agents moving in static environments but also dynamic human agents and their perceptions of the appropriateness of robot behavior. In contrast, clear, repeatable, and accessible benchmarks have accelerated progress in fields like computer vision, natural language processing and traditional robot navigation by enabling researchers to fairly compare algorithms, revealing limitations of existing solutions and illuminating promising new directions. We believe the same approach can benefit social navigation. In this paper, we pave the road towards common, widely accessible, and repeatable benchmarking criteria to evaluate social robot navigation. Our contributions include (a) a definition of a socially navigating robot as one that respects the principles of safety, comfort, legibility, politeness, social competency, agent understanding, proactivity, and responsiveness to context, (b) guidelines for the use of metrics, development of scenarios, benchmarks, datasets, and simulators to evaluate social navigation, and (c) a design of a social navigation metrics framework to make it easier to compare results from different simulators, robots and datasets.Comment: 43 pages, 11 figures, 6 table

Interactional Slingshots: Providing Support Structure to User Interactions in Hybrid Intelligence Systems

The proliferation of artificial intelligence (AI) systems has enabled us to engage more deeply and powerfully with our digital and physical environments, from chatbots to autonomous vehicles to robotic assistive technology. Unfortunately, these state-of-the-art systems often fail in contexts that require human understanding, are never-before-seen, or complex. In such cases, though the AI-only approaches cannot solve the full task, their ability to solve a piece of the task can be combined with human effort to become more robust to handling complexity and uncertainty. A hybrid intelligence system—one that combines human and machine skill sets—can make intelligent systems more operable in real-world settings. In this dissertation, we propose the idea of using interactional slingshots as a means of providing support structure to user interactions in hybrid intelligence systems. Much like how gravitational slingshots provide boosts to spacecraft en route to their final destinations, so do interactional slingshots provide boosts to user interactions en route to solving tasks. Several challenges arise: What does this support structure look like? How much freedom does the user have in their interactions? How is user expertise paired with that of the machine’s? To do this as a tractable socio-technical problem, we explore this idea in the context of data annotation problems, especially in those domains where AI methods fail to solve the overall task. Getting annotated (labeled) data is crucial for successful AI methods, and becomes especially more difficult in domains where AI fails, since problems in such domains require human understanding to fully solve, but also present challenges related to annotator expertise, annotation freedom, and context curation from the data. To explore data annotation problems in this space, we develop techniques and workflows whose interactional slingshot support structure harnesses the user’s interaction with data. First, we explore providing support in the form of nudging non-expert users’ interactions as they annotate text data for the task of creating conversational memory. Second, we add support structure in the form of assisting non-expert users during the annotation process itself for the task of grounding natural language references to objects in 3D point clouds. Finally, we supply support in the form of guiding expert and non-expert users both before and during their annotations for the task of conversational disentanglement across multiple domains. We demonstrate that building hybrid intelligence systems with each of these interactional slingshot support mechanisms—nudging, assisting, and guiding a user’s interaction with data—improves annotation outcomes, such as annotation speed, accuracy, effort level, even when annotators’ expertise and skill levels vary. Thesis Statement: By providing support structure that nudges, assists, and guides user interactions, it is possible to create hybrid intelligence systems that enable more efficient (faster and/or more accurate) data annotation.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163138/1/sairohit_1.pd

Towards a framework for architecting heterogeneous teams of humans and robots for space exploration

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2006.Includes bibliographical references (p. 113-121).Human-robotic systems will play a critical role in space exploration, should NASA embark on missions to the Moon and Mars. A unified framework to optimally leverage the capabilities of humans and robots in space exploration will be an invaluable tool for mission planning. Although there is a growing body of literature on human robotic interactions (HRI), there is not yet a framework that lends itself both to a formal representation of heterogeneous teams of humans and robots, and to an evaluation of such teams across a series of common, task-based metrics. My objective in this thesis is to lay the foundations of a unified framework for architecting human-robotic systems for optimal task performance given a set of metrics. First, I review literature from different fields including HRI and human-computer interaction, and synthesize multiple considerations for architecting heterogeneous teams of humans and robots. I then present methods to systematically and formally capture the characteristics that describe a human-robotic system to provide a basis for evaluating human-robotic systems against a common set of metrics.(cont.) I propose an analytical formulation of common metrics to guide the design and evaluate the performance of human-robot systems, and I then apply the analytical formulation to a case study of a multi-agent human-robot system developed at NASA. Finally, I discuss directions for further research aimed at developing this framework.by Julie Ann Arnold.S.M

2020 NASA Technology Taxonomy

This document is an update (new photos used) of the PDF version of the 2020 NASA Technology Taxonomy that will be available to download on the OCT Public Website. The updated 2020 NASA Technology Taxonomy, or "technology dictionary", uses a technology discipline based approach that realigns like-technologies independent of their application within the NASA mission portfolio. This tool is meant to serve as a common technology discipline-based communication tool across the agency and with its partners in other government agencies, academia, industry, and across the world