Algorithms determine our situation. From bubble sort to Google’s Page Rank, credit scores, and predictive policing, the logic of algorithms intervenes at every step in our lives. Some operate opaquely, shielding their inner workings from curious eyes. Others strive to be transparent, are shared on repositories like GitHub, and follow an ethics of open-source accountability. In both cases, however, a more than trivial effort is required to understand the source codes in which algorithms are usually written. And with machine learning in the form of artificial neural networks, these efforts may well be in vain, as there is no code to inspect any longer.

The online workshop “Source Code Criticism: Hermeneutics, Philology, and Didactics of Algorithms” examined the various ways in which code – both sequential and connectionist – can be read, interpreted, and made accessible to current and future readers, and investigated its role both as often impenetrable societal force as well as a very particular type of text. It took place March 25/26, 2022. The sessions have been recorded and can be accessed and watched via the following links. 

March 25 

Markus Krajewski / Hannes Bajohr (Basel): Introduction

Dan Verständig (Magdeburg): Coding and Uncertain Certainty [KEIN VIDEO UND KEINE DISKUSSION]

Sarah Lang / Sebastian Stoff (Graz): Expectation Management: Testability and Replicability in the context of research software development

Christoph Engemann (Berlin): Sources of Centrality: The Social Genealogy of Modern Graph   Algorithms

Mark Marino (Los Angeles): First Findings: 15 Years of Critical Code Studies

March 26

Anne Kaun (Stockholm): On Robot Colleagues and Software stories: Cultural Techniques of Knowing and Unknowing the Algorithm

Leah Henrickson (Leeds): Grieving via GPT: Circling Around Cadaverous Chatbots

Tyler Shoemaker (Davis, CA): Preprocessing the Word

Matthew Kirschenbaum (College Park, MD): Reading Recurrent Neural Networks