Andrew Iliadis

Bio: Andrew Iliadis is an academic researcher from Temple University. The author has contributed to research in topics: Metadata & Ontology (information science). The author has an hindex of 7, co-authored 20 publications receiving 281 citations. Previous affiliations of Andrew Iliadis include Purdue University & University of Ontario Institute of Technology.

Critical data studies: An introduction:

Abstract: Critical Data Studies (CDS) explore the unique cultural, ethical, and critical challenges posed by Big Data. Rather than treat Big Data as only scientifically empirical and therefore largely neutral phenomena, CDS advocates the view that Big Data should be seen as always-already constituted within wider data assemblages. Assemblages is a concept that helps capture the multitude of ways that already-composed data structures inflect and interact with society, its organization and functioning, and the resulting impact on individuals’ daily lives. CDS questions the many assumptions about Big Data that permeate contemporary literature on information and society by locating instances where Big Data may be naively taken to denote objective and transparent informational entities. In this introduction to the Big Data & Society CDS special theme, we briefly describe CDS work, its orientations, and principles.

Algorithms, ontology, and social progress:

Abstract: Recently, media and communication researchers have shown an increasing interest in critical data studies and ways to utilize data for social progress. In this commentary, I highlight several useful...

Informational Ontology: The Meaning of Gilbert Simondon's Concept of Individuation

Abstract: The French philosopher Gilbert Simondon (1924-1989) was the first true philosopher of information, yet heremains relatively unknown outside of his native France. This situation is curious, given the warm receptionhis work has received from a small group of internationally renowned thinkers. Simondon’s lifelong projectwas to expound the appearance of what I call an “informational ontology,” a subject that deserves to beaddresses at length. This article limits itself by focusing on three aspects of Simondon’s philosophy ofinformation. First, it situates Simondon within the French intellectual scene in post-World War II Europe toget sense of his cultural milieu. Second, it positions Simondon’s work in the context of the Americancybernetic tradition from which it emerged. Finally, it offers an exegesis of Simondon’s informationalontology, a radically new materialism that stands to change contemporary debates surrounding issues relatedto information, communication, and technology.

A New Individuation: Deleuze’s Simondon Connection

Abstract: The French philosopher Gilles Deleuze (1925-1995) wrote monographs on the philosophers whose work greatly influenced his own, with one glaring exception: he did not write one on his contemporary Gilbert Simondon (1924-1989). Simondon is cited only a few times in the Deleuzean corpus yet his influence is everywhere, from ideas concerning the virtual to the concept of individuation. The following paper provides a much needed survey of Simondon’s influence on Deleuze in two steps. First, I show how Simondon’s ontology emerged from a rethinking of Aristotle’s theory of substance (hylomorphism). Second, I elaborate on the few passages where Deleuze explicitly appropriates this new ontology, particularly Simondon’s concept of individuation. In this way, I show how Simondon foresaw our new modes of existence and argued for a new philosophy for them well before any of his contemporaries (Deleuze included) in a way that carries great import for philosophies of information and communication.

The Tower of Babel problem: making data make sense with Basic Formal Ontology

Abstract: Applied computational ontologies (ACOs) are increasingly used in data science domains to produce semantic enhancement and interoperability among divergent data. The purpose of this paper is to propose and implement a methodology for researching the sociotechnical dimensions of data-driven ontology work, and to show how applied ontologies are communicatively constituted with ethical implications.,The underlying idea is to use a data assemblage approach for studying ACOs and the methods they use to add semantic complexity to digital data. The author uses a mixed methods approach, providing an analysis of the widely used Basic Formal Ontology (BFO) through digital methods and visualizations, and presents historical research alongside unstructured interview data with leading experts in BFO development.,The author found that ACOs are products of communal deliberation and decision making across institutions. While ACOs are beneficial for facilitating semantic data interoperability, ACOs may produce unintended effects when semantically enhancing data about social entities and relations. ACOs can have potentially negative consequences for data subjects. Further critical work is needed for understanding how ACOs are applied in contexts like the semantic web, digital platforms, and topic domains. ACOs do not merely reflect social reality through data but are active actors in the social shaping of data.,The paper presents a new approach for studying ACOs, the social impact of ACO work, and describes methods that may be used to produce further applied ontology studies.

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.

Abstract: In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models. This overview examines the existing literature in the field of XAI, including a prospect toward what is yet to be reached. We summarize previous efforts to define explainability in Machine Learning, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought. We then propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at Deep Learning methods for which a second taxonomy is built. This literature analysis serves as the background for a series of challenges faced by XAI, such as the crossroads between data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to XAI with a reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.

科研数据共享的挑战 (The Conundrum of Sharing Research Data)

Abstract: We must all accept that science is data and that data are science, and thus provide for, and justify the need for the support of, much-improved data curation. (Hanson, Sugden, & Alberts) Researchers are producing an unprecedented deluge of data by using new methods and instrumentation. Others may wish to mine these data for new discoveries and innovations. However, research data are not readily available as sharing is common in only a few fields such as astronomy and genomics. Data sharing practices in other fields vary widely. Moreover, research data take many forms, are handled in many ways, using many approaches, and often are difficult to interpret once removed from their initial context. Data sharing is thus a conundrum. Four rationales for sharing data are examined, drawing examples from the sciences, social sciences, and humanities: (1) to reproduce or to verify research, (2) to make results of publicly funded research available to the public, (3) to enable others to ask new questions of extant data, and (4) to advance the state of research and innovation. These rationales differ by the arguments for sharing, by beneficiaries, and by the motivations and incentives of the many stakeholders involved. The challenges are to understand which data might be shared, by whom, with whom, under what conditions, why, and to what effects. Answers will inform data policy and practice. © 2012 Wiley Periodicals, Inc.

The normal and the pathological

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