For the IST23, Rayya Roumanos & Olivier Le Deuff presented the reasoning behind Graph Algo-J, the knowledge management graph currently being developed within the framework of the Algo-J project.
Thanks to Sheila Webber for the live blogging of the presentation.
Abstract
Today, algorithms are under intense scrutiny. While developers focus on their efficiency and reliability, legal experts study their compliance with the law, social scientists explore their widespread influence on people’s lives, and philosophers examine their ethics. What about journalists ? To date, very few newsrooms have taken up the challenge of investigating algorithms (Diakopoulos, 2018) despite the growing importance of these technological actants (Latour, 2005) in everyday life. One of the obstacles preventing them from addressing this critical issue is the lack of comprehensive understanding regarding how algorithms operate and impact lives.
This talk aims to present a tool currently being developed within the framework of a regional research project entitled AlgoJ whose objective is to provide journalists with the necessary resources to address this intricate matter. The tool in question is a knowledge management graph structured in such a manner that it provides multiple levels of reading and navigation regarding algorithms. Its purpose is to enhance algorithm literacy among journalists, empowering them to set up effective investigations into the disruptive influence of these technical pieces of engineering. It is built around a lexicon comprising linked notes or cards, with the aim of defining and elaborating on various terms using hypertext techniques. As a first step, all definitions within the prototype are produced by researchers based on scientific references as well as other documents (e.g. news publications). The next step will be to enable journalists to make use of the tool and enrich it with their own experiential knowledge. One of the distinctive features of this tool called Graph AlgoJ is its capacity to morph into a “hyperdocument” (Le Deuff, 2021, Otlet, 1934) that encompasses both established information and knowledge that is currently being developed.
The focus of the talk will be to provide a comprehensive look at the reasoning behind the tool. It will first present the sociotechnical perspective used to establish its analytical framework then discuss its functions. The premise is that algorithms are not merely technical objects, but “heterogeneous and diffuse sociotechnical systems” (Seaver, 2017) that embody technological and normative dimensions. They are the product of a social context and are, as such, highly permeable to power dynamics stemming from cultural, political, and economic grounds. Furthermore, as omnipresent infrastructures they have become “invisible”, and have gained the ability to structure and ritualise people’s lives without their knowledge.
In order to investigate their “social power”(Beer, 2017), journalists first need to acquire an acute understanding of their nature and function within a specific social context. Graph AlgoJ intends to offer a convincing response to this challenge through a structure that merges two interconnected paths: the first one adopts a conventional knowledge management approach, which involves providing a set of definitions to bridge the gap in knowledge, both in terms of general understanding and specialized expertise. The second focuses on connecting multiple elements in the graph in order to highlight relationships between actors, actants, and actions.
We argue that this two layer approach can alleviate the pervasive imagery of the “black box” (Pasquale, 2016) as well as other highly effective “fictions” (Gillespie, 2014) presenting algorithms as objective, reliable, and unavoidable. Graph algoJ is designed to serve as a hyperdocument that combines academic information with empirical knowledge, with the goal of transcending mere informative content and becoming an “instrument” (Otlet, 1934) consisting of “a complex assemblage of fragments, with meaning derived from the various pathways established through active reading” (Balpe, 1990: 6). By providing this function, it will help journalists overcome technical, cognitive and cultural obstacles preventing them from applying critical and empirical attention to algorithms.
References
- Balpe, J.P. (1990), Hyperdocuments, hypertextes, hypermédias, Paris, Eyrolles.
- Beer, D.G. (2017) The Social Power of Algorithms. Information, Communication and Society. pp. 1-13.
- Diakopoulos, N. (2018) The Algorithms Beat. Data Journalism Handbook. Eds. Liliana Bornegru and Jonathan Gray.
- Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Cambridge, MA: Harvard University Press.
- Gillespie, T. (2014a). The relevance of algorithms. In T. Gillespie, P. J. Boczkowski, & K. A. Foot (Eds.), Media technologies: Essays on communication, materiality, and society (pp. 167–193). Cambridge: MIT Press
- Latour, B. (2005). Reassembling the Social: An Introduction to Actor-Network-Theory. Oxford: Oxford University Press
- Le Deuff, O. (2021), hyperdocumentation. London: Wiley-ISTE
- Otlet, P. (1934), Traité de documentation. Le livre sur le livre, Palais Mondial, Bruxelles
- Seaver, N. (2017) Algorithms as culture: Some tactics for the ethnography of algorithmic systems, Big Data & Society, July–December 2017: 1–12