Semantics-Empowered Big Data Processing with Applications

We discuss the nature of Big Data and address the role of semantics in analyzing and processing Big Data that arises in the context of Physical-Cyber-Social Systems. We organize our research around the Five Vs of Big Data, where four of the Vs are harnessed to produce the fifth V - value. To handle the challenge of Volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision-making. To handle the challenge of Variety, we resort to the use of semantic models and annotations of data so that much of the intelligent processing can be done at a level independent of heterogeneity of data formats and media. To handle the challenge of Velocity, we seek to use continuous semantics capability to dynamically create event or situation specific models and recognize relevant new concepts, entities and facts. To handle Veracity, we explore the formalization of trust models and approaches to glean trustworthiness. The above four Vs of Big Data are harnessed by the semantics-empowered analytics to derive value for supporting practical applications transcending physical-cyber-social continuum

Bias in data-driven artificial intelligence systems—An introductory survey

Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Legal Issues

Bias in data-driven artificial intelligence systems - An introductory survey

Artificial Intelligence (AI)‐based systems are widely employed nowadays to make decisions that have far‐reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well‐grounded in a legal frame. In this survey, we focus on data‐driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth

A review and comparison of ontology-based approaches to robot autonomy

Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.Peer ReviewedPostprint (author's final draft

The Ambience of Innovation: a Material Semiotic Analysis of Corporate and Community Innovation Sites

There are unprecedented opportunities in professional and technical writing (PTW) and rhetoric research thanks to a contemporary expansion of rhetorical studies beyond the linguistic/symbolic and into the material, accounting for the rhetorical contributions of “nonhumans” (Latour Reassembling the Social). Material rhetoric frameworks such as Thomas Rickert’s ambient rhetoric and Bruno Latour’s actor-network theory, provide fertile grounds for PTW/rhetoric research that explores the diffusion of “rhetoric into material space” (Rickert xii) which has especially exciting implications for the study of place and how it embodies values and rhetorically shapes acting, thinking, and the entire spectrum of “human flourishing” (Rickert xii). This renewed interest in the rhetoric of artifacts and how they unite to enact agency within material spaces correlates with an enduring PTW/rhetoric interest in the process that creates things: innovation. The rhetoric of innovation analyzes the complex communication process involved with generating, conveying, and transferring ideas into marketable technology products (Doheny-Farina; Akrich, Callon, and Latour). This work, then, contributes to contemporary PTW/rhetoric research by applying commitments of rhetorical material-semiotics to innovation to understanding the context of innovation and the role of place in ideation. My underlying rhetorical interest within these spaces is the generation, communication, and dispersal of agency during ideation. I explore this process from three perspectives: how the designers of innovation spaces and workshop leverage material context to convey values of innovation; how the artifacts within innovation spaces enact agency upon facilitators and participants to shape their approaches to the innovation process; and how agency is symmetrically distributed across a network of human and nonhuman actants during real time ideation. My project analyzes innovation workshops, brainstorming sessions, and strategic planning sessions, within eight material spaces designed to cultivate creativity through different material means. These spaces are diverse as are the sessions I observed, but, across all of them, I apply a mix of observation, interviews, and ambience descriptions in order to pursue the answers to my research questions and uncover insights about the dispersal of agency within innovation spaces. My analysis of these spaces has numerous implications for PTW/Rhetoric scholars in its expansion of material rhetorics into space analysis; it also has implications PTW/Rhetoric teaching related to materially distribution of agency in the classroom space. Finally, it can help innovation practitioners such as interior designers, engineers, and industrial designers to rhetorically communicate their values of innovation and establish a culture of innovation in their companies through material-linguistic means