Man and Machine: Questions of Risk, Trust and Accountability in Today's AI Technology

Artificial Intelligence began as a field probing some of the most fundamental questions of science - the nature of intelligence and the design of intelligent artifacts. But it has grown into a discipline that is deeply entwined with commerce and society. Today's AI technology, such as expert systems and intelligent assistants, pose some difficult questions of risk, trust and accountability. In this paper, we present these concerns, examining them in the context of historical developments that have shaped the nature and direction of AI research. We also suggest the exploration and further development of two paradigms, human intelligence-machine cooperation, and a sociological view of intelligence, which might help address some of these concerns.Comment: Preprin

Assistive robotics: research challenges and ethics education initiatives

Assistive robotics is a fast growing field aimed at helping healthcarers in hospitals, rehabilitation centers and nursery homes, as well as empowering people with reduced mobility at home, so that they can autonomously fulfill their daily living activities. The need to function in dynamic human-centered environments poses new research challenges: robotic assistants need to have friendly interfaces, be highly adaptable and customizable, very compliant and intrinsically safe to people, as well as able to handle deformable materials. Besides technical challenges, assistive robotics raises also ethical defies, which have led to the emergence of a new discipline: Roboethics. Several institutions are developing regulations and standards, and many ethics education initiatives include contents on human-robot interaction and human dignity in assistive situations. In this paper, the state of the art in assistive robotics is briefly reviewed, and educational materials from a university course on Ethics in Social Robotics and AI focusing on the assistive context are presented.Peer ReviewedPostprint (author's final draft

Why we Ask Why: The Ways in Which Control and Stereotyping Biases Affect Internal Attributions

Since the idea of attributions was famously discussed by Fritz Heider (1958), a wide array of empirical research has focused on the phenomenon. Included within the sphere of attributional theories are internal attributions, which have been of particular interest to the psychological community for decades. Although there is no comprehensive theory for why people make these attributions, literature points to establishing control as a possible motivator. In addition, research suggests that people may make more extreme internal attributions about minorities, particularly when they are not aware they are relying on stereotypes. Participants (N = 377) observed a modified version of the quizmaster paradigm (Ross, Amabile & Steinmetz, 1977), which relies on the Fundamental Attribution Error. They first completed a control manipulation that either deprived their sense of person control or left it unaffected. Then, they watched a video depicting the quizmaster paradigm with either a black contestant or a white contestant. After the video, they rated quizmasters, contestants and themselves based on intelligence. Although the quizmaster paradigm proved to be robust, neither Race nor Control affected the strength of the internal attributions participants made. The lack of significant findings suggest that further research needs to be conducted to ascertain the causality of internal attributions

Agent-oriented Modeling for Collaborative Learning Environments: A Peer-to-Peer Helpdesk Case Study

In this paper, we present the analysis and modelling of Help&Learn, an agent-based peer-to-peer helpdesk system to support extra-class interactions among students and teachers. Help&Learn expands the student’s possibility of solving problems, getting involved in a cooperative learning experience that transcends the limits of classrooms. To model Help&Learn, we have used Agent-Object-Relationship Modeling Language (AORML), an UML extension for agent-oriented information systems modeling. The aim of this research is two-fold. On one hand, we aim at modeling the variety of roles and the complexity of their interactions and activities within the Help&Learn system. On the other hand, we aim at showing the expressive power and the modeling strengths of AORML

Evaluating the development of wearable devices, personal data assistants and the use of other mobile devices in further and higher education institutions

This report presents technical evaluation and case studies of the use of wearable and mobile computing mobile devices in further and higher education. The first section provides technical evaluation of the current state of the art in wearable and mobile technologies and reviews several innovative wearable products that have been developed in recent years. The second section examines three scenarios for further and higher education where wearable and mobile devices are currently being used. The three scenarios include: (i) the delivery of lectures over mobile devices, (ii) the augmentation of the physical campus with a virtual and mobile component, and (iii) the use of PDAs and mobile devices in field studies. The first scenario explores the use of web lectures including an evaluation of IBM's Web Lecture Services and 3Com's learning assistant. The second scenario explores models for a campus without walls evaluating the Handsprings to Learning projects at East Carolina University and ActiveCampus at the University of California San Diego . The third scenario explores the use of wearable and mobile devices for field trips examining San Francisco Exploratorium's tool for capturing museum visits and the Cybertracker field computer. The third section of the report explores the uses and purposes for wearable and mobile devices in tertiary education, identifying key trends and issues to be considered when piloting the use of these devices in educational contexts

Simulating naturalistic instruction: the case for a voice mediated interface for assistive technology for cognition

A variety of brain pathologies can result in difficulties performing complex behavioural sequences. Assistive technology for cognition (ATC) attempts support of complex sequences with the aim of reducing disability. Traditional ATCs are cognitively demanding to use and thus have had poor uptake. A more intuitive interface may allow ATCs to reach their potential. Insights from psychological science may be useful to technologists in this area. We propose that an auditory-verbal interface is more intuitive than a visual interface and reduces cognitive demands on users. Two experiments demonstrate a novel ATC, the General User Interface for Disorders of Execution (GUIDE). GUIDE is novel because it simulates normal conversational prompting to support task performance. GUIDE provides verbal prompts and questions and voice recognition allows the user to interact with the GUIDE. Research with non-cognitively impaired participants and a single participant experiment involving a person with vascular dementia provide support for using interactive auditory-verbal interfaces. Suggestions for the future development of auditory-verbal interfaces are discussed