The ethics of robotic caregivers

2017 ◽  
Vol 18 (2) ◽  
pp. 174-190 ◽  
Author(s):  
Amitai Etzioni ◽  
Oren Etzioni
Keyword(s):  
Artificial Intelligence ◽  
Learning Systems ◽  
High Demand ◽  
Content Type ◽  
Ethical Concerns ◽  
Artificial Intelligence Technology ◽  
Oversight System ◽  
Societal Dynamics

As Artificial Intelligence technology seems poised for a major take-off and changing societal dynamics are creating a high demand for caregivers for elders, children, and those infirmed, robotic caregivers may well be used much more often. This article examines the ethical concerns raised by the use of AI caregivers and concludes that many of these concerns are avoided when AI caregivers operate as partners rather than substitutes. Furthermore, most of the remaining concerns are minor and are faced by human caregivers as well. Nonetheless, because AI caregivers’ systems are learning systems, an AI caregiver could stray from its initial guidelines. Therefore, subjecting AI caregivers to an AI-based oversight system is proposed to ensure that their actions remain both legal and ethical.

US tech regulation will develop locally

2019 ◽  
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Racial Discrimination ◽  
The United States ◽  
Privacy And Security ◽  
Content Type ◽  
Ethical Concerns ◽  
Advanced Technologies ◽  
Facial Identification ◽  
Recognition Software

Subject Tech regulation in the United States. Significance San Francisco’s Board of Supervisors on December 10 amended its ban on facial recognition software to allow for Apple iPhones that use facial identification for unlocking the phone. The move reflects the difficulty cities and states face in regulating artificial intelligence (AI) and other advanced technologies that have privacy and security implications: while they are legally permitted to regulate, the efficacy of localities’ reforms runs into challenges in a largely unregulated national scene. Impacts AI in policing will attract attention, since municipalities are the jurisdiction for most US police. AI adoption will accentuate ethical concerns about racial discrimination in policing as reports of bias in algorithms proliferate. AI has a low chance of becoming polarised; the tech sector has Republican and Democratic friends.

scholarly journals Optimization model: the innovation and future of e-ecotourism for sustainability

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fauziah Eddyono ◽  
Dudung Darusman ◽  
Ujang Sumarwan ◽  
Fauziah Sunarminto
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Sustainable Tourism ◽  
System Modeling ◽  
Secondary Data ◽  
Simulation Models ◽  
Content Type ◽  
Causal Loop ◽  
Artificial Intelligence Technology ◽  
Intelligent Technology

PurposeThis study aims to find a dynamic model in an effort to optimize tourism performance in ecotourism destinations. The model structure is built based on competitive performance in geographic areas and the application of ecotourism elements that are integrated with big data innovation through artificial intelligence technology.Design/methodology/approachData analysis is performed through dynamic system modeling. Simulations are carried out in three models: First, existing simulation models. Second, Scenario 1 is carried out by utilizing a causal loop through innovation of big data-based artificial intelligence technology to ecotourism elements. Third, Scenario 2 is carried out by utilizing a causal loop through big data-based artificial intelligence technology on aspects of ecotourism elements and destination competitiveness.FindingsThis study provides empirical insight into the competitiveness performance of destinations and the performance of implementing ecotourism elements if integrated with big data innovations that will be able to massively demonstrate the growth of sustainable tourism performance.Research limitations/implicationsThis study does not use a primary database, but uses secondary data from official sources that can be accessed by the public.Practical implicationsThe paper includes implications for the development of intelligent technology based on big data and also requires policy innovation.Social implicationsSustainable tourism development.Originality/valueThis study finds the expansion of new theory competitiveness of ecotourism destinations.

Artificial intelligence in the fashion industry: consumer responses to generative adversarial network (GAN) technology

2020 ◽  
Vol 49 (1) ◽  
pp. 61-80
Author(s):  
Kwonsang Sohn ◽  
Christine Eunyoung Sung ◽  
Gukwon Koo ◽  
Ohbyung Kwon
Keyword(s):  
Artificial Intelligence ◽  
Willingness To Pay ◽  
Fashion Industry ◽  
Generative Adversarial Network ◽  
Experimental Conditions ◽  
Content Type ◽  
Consumer Responses ◽  
Adversarial Network ◽  
Consumption Values ◽  
Artificial Intelligence Technology

PurposeThis study examines consumers' evaluations of product consumption values, purchase intentions and willingness to pay for fashion products designed using generative adversarial network (GAN), an artificial intelligence technology. This research investigates differences between consumers' evaluations of a GAN-generated product and a non-GAN-generated product and tests whether disclosing the use of GAN technology affects consumers' evaluations.Design/methodology/approachSample products were developed as experimental stimuli using cycleGAN. Data were collected from 163 members of Generation Y. Participants were assigned to one of the three experimental conditions (i.e. non-GAN-generated images, GAN-generated images with disclosure and GAN-generated images without disclosure). Regression analysis and ANOVA were used to test the hypotheses.FindingsFunctional, social and epistemic consumption values positively affect willingness to pay in the GAN-generated products. Relative to non-GAN-generated products, willingness to pay is significantly higher for GAN-generated products. Moreover, evaluations of functional value, emotional value and willingness to pay are highest when GAN technology is used, but not disclosed.Originality/valueThis study evaluates the utility of GANs from consumers' perspective based on the perceived value of GAN-generated product designs. Findings have practical implications for firms that are considering using GANs to develop products for the retail fashion market.

Early detection of SARS-CoV-2 without human intervention to combat COVID-19 using drone technology

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sekar Manigandan ◽  
Praveen Kumar Thaloor Ramesh ◽  
Nguyen Thuy Lan Chi ◽  
Kathirvel Brindhadevi
Keyword(s):  
Artificial Intelligence ◽  
Education Institution ◽  
Shopping Mall ◽  
Human Intervention ◽  
Infectious Individual ◽  
Content Type ◽  
Outdoor Environments ◽  
Artificial Intelligence Technology ◽  
Indoor And Outdoor Environments ◽  
The Right

Purpose The focus of the current study to combat the ongoing pandemic by preventing the transmission using the Unmanned aerial vehicle system. The transmission of the COVID-19 pandemic can be avoided only by finding the infectious person at the right time. Despite the thermal scanning camera and artificial intelligence technology, finding the infectious individual at many occasions has become questionable. Design/methodology/approach The drones are equipped with the thermal vision camera to detect the human body temperature. In addition, they are equipped with the disinfect tank to sanitize the indoor and outdoor environments based on the requirement. Findings Once the lockdown eased, the experts fear that the infection rate can increase in the high-density population countries such as India. The drone with thermal screening and day vision camera can detect the infection of the person without any human intervention. Further, they can also be used to disinfect the public places by aerial spraying. Practical implications Using the drones to monitor the work places, shopping mall and education institution to identify the mask through artificial intelligence is viable without human intervention in short span of time. Originality/value COVID-19 impact on the global was awful. Finding a suitable technology to combat the COVID-19 is much necessary. This conceptual study proposed to use drone technology to identify the infection at right time even on densely populated streets. Further, artificial technology can be used to detect the person who was not wearing mask. Added to above, disinfect tank can be mounted to sanitize the area in the required places.

Robot Teaching, Pedagogy, and Policy

2020 ◽  
pp. 787-803
Author(s):  
Elana Zeide
Keyword(s):  
Artificial Intelligence ◽  
Analytical Methods ◽  
Personalized Learning ◽  
Educational Process ◽  
Learning Systems ◽  
Policy Decisions ◽  
Public Discussion ◽  
Teaching Pedagogy ◽  
Do So

This chapter looks at the use of artificial intelligence (AI) in education, which immediately conjures the fantasy of robot teachers, as well as fears that robot teachers will replace their human counterparts. However, AI tools impact much more than instructional choices. Personalized learning systems take on a whole host of other educational roles as well, fundamentally reconfiguring education in the process. They not only perform the functions of robot teachers but also make pedagogical and policy decisions typically left to teachers and policymakers. Their design, affordances, analytical methods, and visualization dashboards construct a technological, computational, and statistical infrastructure that literally codifies what students learn, how they are assessed, and what standards they must meet. However, school procurement and implementation of these systems are rarely part of public discussion. If they are to remain relevant to the educational process itself, as opposed to just its packaging and context, schools and their stakeholders must be more proactive in demanding information from technology providers and setting internal protocols to ensure effective and consistent implementation. Those who choose to outsource instructional functions should do so with sufficient transparency mechanisms in place to ensure professional oversight guided by well-informed debate.

Welcome to the marriage of human capability and artificial intelligence

2019 ◽  
Vol 19 (1) ◽  
pp. 10-14
Author(s):  
Ryan Scott ◽  
Malcolm Le Lievre
Keyword(s):  
Artificial Intelligence ◽  
Team Building ◽  
Workplace Change ◽  
Content Type ◽  
The Future ◽  
Human Capability ◽  
Mind Set ◽  
Future Work ◽  
Crucial Part ◽  
Practical Implications

Purpose The purpose of this paper is to explore insights methodology and technology by using behavioral to create a mind-set change in the way people work, especially in the age of artificial intelligence (AI). Design/methodology/approach The approach is to examine how AI is driving workplace change, introduce the idea that most organizations have untapped analytics, add the idea of what we know future work will look like and look at how greater, data-driven human behavioral insights will help prepare future human-to-human work and inform people’s work with and alongside AI. Findings Human (behavioral) intelligence will be an increasingly crucial part of behaviorally smart organizations, from hiring to placement to adaptation to team building, compliance and more. These human capability insights will, among other things, better prepare people and organizations for changing work roles, including working with and alongside AI and similar tech innovation. Research limitations/implications No doubt researchers across the private, public and nonprofit sectors will want to further study the nexus of human capability, behavioral insights technology and AI, but it is clear that such work is already underway and can prove even more valuable if adopted on a broader, deeper level. Practical implications Much “people data” inside organizations is currently not being harvested. Validated, scalable processes exist to mine that data and leverage it to help organizations of all types and sizes be ready for the future, particularly in regard to the marriage of human capability and AI. Social implications In terms of human capability and AI, individuals, teams, organizations, customers and other stakeholders will all benefit. The investment of time and other resources is minimal, but must include C-suite buy in. Originality/value Much exists on the softer aspects of the marriage of human capability and AI and other workplace advancements. What has been lacking – until now – is a 1) practical, 2) validated and 3) scalable behavioral insights tech form that quantifiably informs how people and AI will work in the future, especially side by side.

scholarly journals A Study of Automatic Judgment of Food Color and Cooking Conditions with Artificial Intelligence Technology

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1128
Author(s):  
Chern-Sheng Lin ◽  
Yu-Ching Pan ◽  
Yu-Xin Kuo ◽  
Ching-Kun Chen ◽  
Chuen-Lin Tien
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Color Space ◽  
Color Difference ◽  
Color Space Conversion ◽  
Before And After ◽  
Artificial Intelligence Technology ◽  
Artificial Intelligence Algorithm ◽  
Time Image ◽  
Cooking Conditions

In this study, the machine vision and artificial intelligence algorithms were used to rapidly check the degree of cooking of foods and avoid the over-cooking of foods. Using a smart induction cooker for heating, the image processing program automatically recognizes the color of the food before and after cooking. The new cooking parameters were used to identify the cooking conditions of the food when it is undercooked, cooked, and overcooked. In the research, the camera was used in combination with the software for development, and the real-time image processing technology was used to obtain the information of the color of the food, and through calculation parameters, the cooking status of the food was monitored. In the second year, using the color space conversion, a novel algorithm, and artificial intelligence, the foreground segmentation was used to separate the vegetables from the background, and the cooking ripeness, cooking unevenness, oil glossiness, and sauce absorption were calculated. The image color difference and the distribution were used to judge the cooking conditions of the food, so that the cooking system can identify whether or not to adopt partial tumbling, or to end a cooking operation. A novel artificial intelligence algorithm is used in the relative field, and the error rate can be reduced to 3%. This work will significantly help researchers working in the advanced cooking devices.

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