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  • The Information Manifold: Why Computers Can't Solve Algorithmic Bias and Fake News by Antonio Badia
  • Christiana Varda
The Information Manifold: Why Computers Can't Solve Algorithmic Bias and Fake News
by Antonio Badia
MIT PRESS, 2019. 352 PP.
ISBN 978-0-2620-4303-8

As we navigate the contemporary digital landscape, information feels ubiquitous. Push notifications from news organizations, social media, and private messaging apps serve as a constant reminder that the flow of information is incessant, if not overwhelming. As we traverse this "information age" characterized by immediate access to abundant information, Antonio Badia invites us to pause and consider what counts as information. His book, The Information Manifold: Why Computers Can't Solve Algorithmic Bias and Fake News, examines how we define information by considering three different perspectives (syntactic, semantic, and pragmatic) that color not only how we understand information but also how we approach and manage it online on a daily basis in the context of issues such as algorithmic bias and misinformation.

Badia spends the majority of the book framing his argument that many of the contemporary issues we're facing online can be addressed by understanding how information is viewed across three levels of analysis. At the syntactic level, any data is considered information; the content of the information is of less importance than the transmission of the information from sender to receiver. Building on the ideas of Claude Shannon and Andrey Kolmogorov, Badia underlines that this lens primarily views information as data of any pattern or structure.1 This view does not consider the message as information, but the semantic level does; from this level, the content and its denoted meaning constitute information. This semantic lens views data as referents and only considers data patterns that are meaningful—as opposed to any pattern at the syntactic level—as information. This semantic approach resembles a more intuitive way of defining information—that the message itself is the information—but it also complicates how information is defined, because meaning is both contextual and tentative. At this semantic level, Badia connects information to knowledge by examining Fred Dretske's view that information is the basis of knowledge and to data by examining Luciano Floridi's view that information is data that we notice and interpret.2 The semantic approach is somewhat extended by the third, pragmatic level of analysis, which focuses not only on the data patterns that are meaningful but also on the ones that serve a specific purpose. The pragmatic lens is more concerned with the goals of the information, but it still values meaningful patterns. The difference is that it views information as only the data structures or patterns that are relevant to a current situation or content. Badia makes painstaking connections to Shannon's approach to information in the semantic and pragmatic levels and draws a convincing argument on how each of these perspectives works progressively, but more narrowly, in scope. [End Page 227]

Beyond these three lenses, Badia discusses the connection between information and communication and, more specifically, the way information is distributed and spread among groups and how it is processed in networks. This network-based approach is an important addition, given that the book goes on to explore issues that are rooted in the social web, and it would have been a serious omission to ignore the communication perspective on information. However, the author argues that despite the value of understanding how information is exchanged and processed, to answer the basic question that this book puts forth, it is important to consider the information content and its relevance to achieving specific goals. One must examine information at the semantic and pragmatic levels before considering how the information is used.

These braided approaches are played out with issues that have recently come to the forefront of the information stage: machine learning, big data, algorithmic fairness, and misinformation. The information problems we encounter today begin with the amount of available data and what we can do with them in order to make better, more "informed" decisions. An attempt to answer these problems is through computing solutions that can help us tackle the abundance of available data...


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pp. 227-228
Launched on MUSE
Open Access
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