Misleadingness in the algorithm society: Misinformation and disinformation

Sille Obelitz Søe, University of Copenhagen, Denmark.

Mar 6, 2017 · 5 min read
Information by Elaine is licensed under CC BY 2.0

Data as Fact

The algorithm society has arrived. It has arrived with big data analytics, computational decision-making, as well as unintelligible and non-transparent information processes (cf. Pasquale, 2015). It is a society where data is at the core, articulated simply as ‘data’ or ‘big data’ - almost as uniform commodities despite their irregular and diverse appearances. Further, it is a society driven forward by the promises and hopes for a smarter data-driven future.

“[w]ith the right information, it is becoming increasingly possible to make exactly the right product or service, tailored to a particular individual’s current needs and circumstances, at exactly the right time. (…) The power of personal data is that it enables this richness, relevance and waste reduction to be applied to every individual, in every aspect of their lives, and across all industries.”

(Ctrl-Shift, 2015, p. 7).

This idea of the current and future potential of data analytics presupposes information and personal data as truthful, otherwise ‘richness, relevance and waste reduction’ would not be expected. Thus, it seems to be the general conception that if we just have enough data or information of the right kind then we can predict the future with high degrees of certainty.

Data as Meaning

“People’s data, whether provided, observed, derived or inferred from usage, is playing an increasingly pivotal role in the creation and evolution of innovative new services, transforming our economy into a data driven economy.” (Deadman, 2015, p. 3). Observation, derivation, and inference are all based on interpretation and meaning. What some data or information means is dependent on the context. Thus, shifts in context can lead to shifts in meaning for the same datum or piece of information. It is in the relation between data/information, context, and meaning that different kinds of misleadingness can occur. Misleadingness - e.g. misinformation and disinformation - always arises as a consequence of the relation between content and context.

Data as Intention

When an utterance or an act is algorithmically collected as a fact - i.e. as data in the objective and truthful interpretation - the context outside the digital platform and the intentions behind the utterance or act are not parts of the data collected. For instance, the fact that I buy a specific book online does not say anything about my intentions. Why I bought the book and who it is for is not reflected in the data. When my purchase is collected as a fact the underlying assumption is that I bought the book because I like it. However, if I bought the book for someone else the context changes and the data does not reflect my preferences. Thus, the data about the purchase becomes misleading.

Big data - small meaning - global discourses

In focus are the challenges of the information society…


Written by

A research collaboration between University of Copenhagen (http://iva.ku.dk/english/) and Lund University (http://www.kultur.lu.se/en/).

Big data - small meaning - global discourses

In focus are the challenges of the information society meeting the tensions between large amounts of data (Big Data) and their interpretation in a local context from a humanities perspective.

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