Heuristics of the algorithm: Big Data, user interpretation and institutional translation

By Göran Bolin, Jonas Andersson Schwarz. 2015

75 citations recorded by the source corpus.

Intelligence on mass media audiences was founded on representative statistical samples, analysed by statisticians at the market departments of media corporations. The techniques for aggregating user data in the age of pervasive and ubiquitous personal media (e.g. laptops, smartphones, credit cards/swipe cards and radio-frequency identification) build on large aggregates of information (Big Data) analysed by algorithms that transform data into commodities. While the former technologies were built on socio-economic variables such as age, gender, ethnicity, education, media preferences (i.e. categories recognisable to media users and industry representatives alike), Big Data technologies register consumer choice, geographical position, web movement, and behavioural information in technologically complex ways that for most lay people are too abstract to appreciate the full consequences of. The data mined for pattern recognition privileges relational rather than demographic qualities. We argue that the agency of interpretation at the bottom of market decisions within media companies nevertheless introduces a ‘heuristics of the algorithm’, where the data inevitably becomes translated into social categories. In the paper we argue that although the promise of algorithmically generated data is often implemented in automated systems where human agency gets increasingly distanced from the data collected (it is our technological gadgets that are being surveyed, rather than us as social beings), one can observe a felt need among media users and among industry actors to ‘translate back’ the algorithmically produced relational statistics into ‘traditional’ social parameters. The tenacious social structures within the advertising industries work against the techno-economically driven tendencies within the Big Data economy.

Subfields: Communication, Safety Research, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

1. David Beer
2. Donghee Shin
3. J. Dijck
4. Tarleton Gillespie
5. Daniel Neyland
6. Kate Crawford

In Safety Research

1. Donghee Shin
2. David Beer

In Sociology and Political Science

1. David Beer
2. Donghee Shin
3. J. Dijck
4. Tarleton Gillespie
5. Daniel Neyland
6. Kate Crawford

Citations in the corpus

• The social power of algorithms (2017, background): “As Bolin and Schwarz (2015) have discussed elsewhere, algorithmic outcomes are often ‘translated back’ into ‘traditional’ social parameters.”
• User Perceptions of Algorithmic Decisions in the Personalized AI System:Perceptual Evaluation of Fairness, Accountability, Transparency, and Explainability (2020, background): “How people see and use personalization algorithms, to what extent people approve the use of their data, and how people satisfy with the personalized results all influence the operation and the performance of personalization algorithms (Bolin & Schwarz, 2015).”