Human-centered computing

Jaimes, Alejandro; Sebe, Nicu; Gática-Pérez, Daniel

doi:10.1145/1180639.1180829

Cited by 96 publications

(5 citation statements)

References 40 publications

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“…This quote fits perfectly also in some of the paradigms related to the cooperation between humans and machines like HCI or Human-Centered Computing (HCC) [82]. In short, the HCC paradigm overlaps HCI except for the fact that in HCC, not only the user and its context are observed, but the users' intentions and motives are estimated from the observed behavior (using interaction models and other techniques) [56].…”

Section: Context Of This Researchsupporting

confidence: 57%

On data-driven systems analyzing, supporting and enhancing users’ interaction and experience

Cruz-Benito¹

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The research areas of Human-Computer Interaction and Software Architectures have been traditionally treated separately, but in the literature, many authors made efforts to merge them to build better software systems. One of the common gaps between software engineering and usability is the lack of strategies to apply usability principles in the initial design of software architectures. Including these principles since the early phases of software design would help to avoid later architectural changes to include user experience requirements. The combination of both fields (software architectures and Human-Computer Interaction) would contribute to building better interactive software that should include the best from both the systems and user-centered designs. In that combination, the software architectures should enclose the fundamental structure and ideas of the system to offer the desired quality based on sound design decisions.Moreover, the information kept within a system is an opportunity to extract knowledge about the system itself, its components, the software included, the users or the interaction occurring inside. The knowledge gained from the information generated in a software environment can be used to improve the system itself, its software, the users' experience, and the results. So, the combination of the areas of Knowledge Discovery and Human-Computer Interaction offers ideal conditions to address Human-Computer-Interaction-related challenges. The Human-Computer Interaction focuses on human intelligence, the Knowledge Discovery in computational intelligence, and the combination of both can raise the support of human intelligence with machine intelligence to discover new insights in a world crowded of data. This Ph.D. Thesis deals with these kinds of challenges: how approaches like data-driven software architectures (using Knowledge Discovery techniques) can help to improve the users' interaction and experience within an interactive system. Specifically, it deals with how to improve the human-computer interaction processes of different kind of stakeholders to improve different aspects such as the user experience or the easiness to accomplish a specific task.Several research actions and experiments support this investigation. These research actions included performing a systematic literature review and mapping of the literature that was aimed at finding how the software architectures in the literature have been used to support, analyze or enhance the human-computer interaction. Also, the actions included work on four different research scenarios that presented common challenges in the Human-Computer Interaction knowledge area. The case studies that fit into the scenarios selected were chosen based on the Human-Computer Interaction challenges they present, and on the authors' accessibility to them. The four case studies were: an educational laboratory virtual world, a Massive Open Online Course and the social networks where the students discuss and learn, a system that includes very large web forms, an...

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Section: Context Of This Researchsupporting

confidence: 57%

On data-driven systems analyzing, supporting and enhancing users’ interaction and experience

Cruz-Benito¹

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“…In this approach, intelligence is viewed as an attribute of the combination of humanmachine-context (Ford, Hayes, Glymour, & Allen, 2015;Hoffman, Hayes, & Ford, 2001). This HCD research approach requires integrating diverse views including computer science, psychology, and design to understand the context and situated cognition and action of people using AI systems (Brezillon, 2003;Ford et al, 2015;Hoffman, Roesler, & Moon, 2004;Jaimes, Sebe, & Gatica-Perez, 2006). This approach produces better social outcomes than traditional conceptions of AI as it complements rather than substitutes human intelligence in systems (Hoffman, Hayes, Ford, & Bradshaw, 2012).…”

Section: Human-centered Computing and Artificial Intelligence Researchmentioning

confidence: 99%

“…Interface and interaction design to provide a useful and usable AI system Examination of usability, accessibility of the AI system and potentially harmful people's behavior and experience in the interaction with the AI system (Norman, 2013;Norman & Draper, 1986;Winograd, 1996Winograd, , 2006Xu, 2019) Persuasive Technology (PT) AI systems that persuade people towards an intended behavior Identification of ethical questionable behavioral triggers of the AI system (Berdichevsky & Neuenschwander, 1999;Fogg, 1998Fogg, , 2003 Humancentered Computing (HCC) Adaptation and organization of everyday life around AI systems that produces a capability between human and computer system Examination of interspaces enabled by the AI system that replaces or constrains people's capability and lifestyle (Brezillon, 2003;Ford et al, 2015;Hoffman et al, 2004;Jaimes et al, 2006) Need-Design Response (NDR) Design and development of AI systems for physical, intellectual and emotional needs of people Identification of unethical AI systems design based on needs such as addiction (Faste, 1987;Gilmore et al, 1999;McKim, 1959McKim, , 1980 Interaction Design, Persuasive Technologies, and Human-centered Computing enable the examination of the interaction and behavior of people with the AI system. Examining and iteratively developing the human-AI system interactions allows identifying potential harm and enables creating safe and secure experiences and solutions for people.…”

Section: Hcd Research For Human-centered Artificial Intelligencementioning

confidence: 99%

Human-centered AI: The role of Human-centered Design Research in the development of AI

Auernhammer¹

2020

Proceedings of DRS

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Artificial Intelligence has the tremendous potential to produce progress and innovation in society. Designing AI for people has been expressed as essential for societal well-being and the common good. However, human-centered is often used generically without any commitment to a philosophy or overarching approach. This paper outlines different philosophical perspectives and several Human-centered Design approaches and discusses their contribution to the development of Artificial Intelligence. The paper argues that humanistic design research should play a vital role in the pan-disciplinary collaboration with technologists and policymakers to mitigate the impact of AI. Ultimately, Human-centered Artificial Intelligence incorporates involving people and designing Artificial Intelligence systems for people through a genuine human-centered philosophy and approach.

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“…Human-centered computing from a multimedia perspective is discussed James et al. 27 and Aragon et al., 28 where the main properties of human-centered multimedia, their interactions, research agendas, and potential applications are analyzed. Besides, different issues regarding human-centered computing using a machine-learning approach is examined by Fiebrink and Gillies 29 and Riedl.…”

Section: Main Textmentioning

confidence: 99%

Interdependent Networks: A Data Science Perspective

2020

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Summary Traditionally, networks have been studied in an independent fashion. With the emergence of novel smart city technologies, coupling among networks has been strengthened. To capture the ever-increasing coupling, we explain the notion of interdependent networks, i.e., multi-layered networks with shared decision-making entities, and shared sensing infrastructures with interdisciplinary applications. The main challenge is how to develop data analytics solutions that are capable of enabling interdependent decision making. One of the emerging solutions is agent-based distributed decision making among heterogeneous agents and entities when their decisions are affected by multiple networks. We first provide a big picture of real-world interdependent networks in the context of smart city infrastructures. We then provide an outline of potential challenges and solutions from a data science perspective. We discuss potential hindrances to ensure reliable communication among intelligent agents from different networks. We explore future research directions at the intersection of network science and data science.

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Human-centered computing

Cited by 96 publications

References 40 publications

On data-driven systems analyzing, supporting and enhancing users’ interaction and experience

On data-driven systems analyzing, supporting and enhancing users’ interaction and experience

Human-centered AI: The role of Human-centered Design Research in the development of AI

Interdependent Networks: A Data Science Perspective

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