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Public interest technologies (PIT) serve to address social needs and challenges in society People working in this space ask communities what their needs are first, without presuming they know what is best for them and generally use a participatory approach to innovation with values in mind and cultural awareness PIT pertain to technologies that may aid not for profits and non government organizations and private industry to achieve their goals These technologies might leverage open source software for collaboration, and open data initiatives to overcome societal challenges, may exercise crowdsourcing and crowd sensing techniques toward collective awareness, and provide open workforce exchanges where Information and Communication Technologies and engineering volunteerism is encouraged towards satisfying the needs of under resourced organizations and citizenry
The conference seeks to attract participation from a wide variety of technical fields, given the broad scope of its overall theme Technological Stewardship and Responsible Innovation In addition, this theme seeks to engage participants in current debates about the status of ethics related topics within the tech industry In recent years, a slew of manifestos and other tech for good declarations have been published, and there is growing acknowledgement that tackling the issues and questions surrounding technology s complex social implications will require input from across multiple disciplines and approaches (applied, hard, and social sciences, as well as humanities) By bringing together scholars and professionals who are actively involved in developing approaches that embrace principles related to technological stewardship and responsible innovation, we hope to generate valuable contributions to many sub fields within the IEEE community
Policy Makers Business Industry (particularly in the Asia Pacific Region) NGOs Researchers Scientists Academicians Technocrats Students
Proceedings of the 5th International Conference on Intelligent Human Systems Integration (IHSI 2022): Integrating People and Intelligent Systems, February 22–24, 2022, Venice, Italy
Advanced computational intelligence techniques have been designed and developed in recent years to cope with various big data challenges and provide fast and efficient analytics that assist in making critical decisions. With the rapid evolution and development of internet-based services and applications, this technology is receiving attention from researchers, industries, and academic communities and requires additional study. Convergence of Big Data Technologies and Computational Intelligent Techniques considers recent advancements in big data and computational intelligence across fields and disciplines and discusses the various opportunities and challenges of adoption. Covering topics such as deep learning, data mining, smart environments, and high-performance computing, this reference work is crucial for computer scientists, engineers, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.
This book offers a unique interdisciplinary perspective on the ethics of 'artificial intelligence' – autonomous, intelligent, (and connected) systems, or AISs, applying principles of social cognition to understand the social and ethical issues associated with the creation, adoption, and implementation of AISs. As humans become entangled in sociotechnical systems defined by human and artificial agents, there is a pressing need to understand how trust is created, used, and abused. Compounding the difficulty in answering these questions, stakeholders directly or indirectly affected by these systems differ in their motivations, understanding, and values. This volume provides a comprehensive resource to help stakeholders understand ethical issues of designing and implementing AISs using an ethical sensemaking approach. Starting with the general technical affordances of AIS, Dr. Jordan Richard Schoenherr considers the features of system design relating data integrity, selection and interpretation of algorithms, and the evolution processes that drive AISs innovation as a sociotechnological system. The poles of technophobia (algorithmic aversion) and technophilia (algorithmic preference) in the public perception of AISs are then described and considered against existing evidence, including issues ranging from the displacement and re-education needs of the human workforce, the impact of use of technology on interpersonal accord, and surveillance and cybersecurity. Ethical frameworks that provide tools for evaluating the values and outcomes of AISs are then reviewed, and how they can be aligned with ethical sensemaking processes identified by psychological science is explored. Finally, these disparate threads are brought together in a design framework. Also including sections on policies and guideline, gaming and social media, and Eastern philosophical frameworks, this is fascinating reading for students and academics in psychology, computer science, philosophy, and related areas, as well as professionals such as policy makers and those working with AI systems.