The Race Between Cognitive and Artificial Intelligence: Examining Socio-Ethical Collaborative Robots Through Anthropomorphism and Xenocentrism in Human-Robot Interaction

The Race Between Cognitive and Artificial Intelligence: Examining Socio-Ethical Collaborative Robots Through Anthropomorphism and Xenocentrism in Human-Robot Interaction

Anshu Saxena Arora (University of the District of Columbia, Washington, D.C., USA) and Amit Arora (University of the District of Columbia, Washington, D.C., USA)
Copyright: © 2020 |Pages: 16
DOI: 10.4018/IJIIT.2020010101

Abstract

Research on human-robot interaction (HRI) is growing; however, focus on the congruent socio-behavioral HRI research fields of social cognition, socio-behavioral intentions, and code of ethics is lacking. Humans possess an inherent ability of integrating perception, cognition, and action; while robots may have limitations as they may not recognize an object or a being, navigate a terrain, and/or comprehend written or verbal language and instructions. This HRI research focuses on issues and challenges for both humans and robots from social, behavioral, technical, and ethical perspectives. The human ability to anthropomorphize robots and adoption of ‘intentional mindset' toward robots through xenocentrism have added new dimensions to HRI. Robotic anthropomorphism plays a significant role in how humans can be successful companions of robots. This research explores social cognitive intelligence versus artificial intelligence with a focus on privacy protections and ethical implications of HRI while designing robots that are ethical, cognitively and artificially intelligent, and social human-like agents.
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Introduction

Human-Robot Interaction (HRI) is a growing area of interest in science, technology, engineering, and mathematics (STEM); and now researchers are expanding their research to investigate the business aspects of HRI. Humanoid robots are increasingly becoming a part of our daily lives since these artificially intelligent human-like agents are incorporated as ‘social agents’ in various human spheres like healthcare, education, heavy lifting, home cleaning, repetitive jobs, precision handling tasks, jobs requiring continuously high levels of concentration, and working in hazardous, contaminated and inaccessible environments. Even though HRI is a growing field of artificial intelligence, so far it has either been neglected as a substantial area of research or it has been studied in conjunction with human-computer interaction (Sheridan, 2016). In either scenario, the field of HRI offers a tremendous research potential in understanding robotic interactions with humans, human-technology behavior, human factors, dynamics, control, and computer science (artificial intelligence) (IEEE Robotics and Automation Society, 2019; Schellen and Wykowska, 2019; Damiano and Dumouchel, 2018; Sheridan, 2016; Goodrich and Schultz, 2008; Dumouchel and Damiano, 2017).

HRI research can be classified into four categories: (a) telerobotics in hazardous or inaccessible environments, (b) human supervision / control of industrial robots in routine tasks, (c) automated vehicles including automated highway and rail vehicles and commercial aircrafts, and (d) human-robot social interactions. The first category of telerobotics / teleoperation, which is described as humans controlling space, airborne, terrestrial, and undersea vehicles for nonroutine tasks in hazardous environments; whereby progress can be seen in undersea robotic vehicles, unmanned spacecraft, and/or unmanned aerial vehicles (UAVs) (Skaar and Ruoff, 1994; Sheridan and Verplank, 1978; Burke and Murphy, 2010). Second category of HRI research deals with situations that require human supervision and control of robots for routine tasks, such as factory assembly line operations, delivery of parts and packages, medical and hospital supplies and deliveries, floor cleaning, and automated agricultural tasks. Third HRI research category focuses on automated vehicles including automated highway and rail vehicles, and commercial aircrafts, even though there is some/high degree of human supervision and control required to operate these systems as teleoperators / telerobots (Rasmussen, 1986; Sheridan 2016). Google’s self-driving car technology and Tesla’s autonomous vehicle fit in this category with several automobile manufacturers vying to enter this market and developing human-driver technology enhancements like radar-augmented cruise control, alerts signals, off-the-road alarms, and vehicle-to-vehicle communication for avoiding/predicting impending collisions.

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