Different people have different perceptions about artificial intelligence (AI). It is extremely important to bring together all the alternative frames of thinking—from the various communities of developers, researchers, business leaders, policymakers, and citizens—to properly start acknowledging AI. This article highlights the ‘fruitful collaboration’ that sociology and AI could develop in both social and technical terms. We discuss how biases and unfairness are among the major challenges to be addressed in such a sociotechnical perspective. First, as intelligent machines reveal their nature (...) of ‘magnifying glasses’ in the automation of existing inequalities, we show how the AI technical community is calling for transparency and explainability, accountability and contestability. Not to be considered as panaceas, they all contribute to ensuring human control in novel practices that include requirement, design and development methodologies for a fairer AI. Second, we elaborate on the mounting attention for technological narratives as technology is recognized as a social practice within a specific institutional context. Not only do narratives reflect organizing visions for society, but they also are a tangible sign of the traditional lines of social, economic, and political inequalities. We conclude with a call for a diverse approach within the AI community and a richer knowledge about narratives as they help in better addressing future technical developments, public debate, and policy. AI practice is interdisciplinary by nature and it will benefit from a socio-technical perspective. (shrink)

Platform companies use techniques of algorithmic management to control their users. Though digital marketplaces vary in their use of these techniques, few studies have asked why. This question is theoretically consequential. Economic sociology has traditionally focused on the embedded activities of market actors to explain competitive and valuation dynamics in markets. But restrictive platforms can leave little autonomy to market actors. Whether or not the analytical focus on their interactions makes sense thus depends on how restrictive the platform is, turning (...) the question into a first order analytical concern. The paper argues that we can explain why platforms adopt more and less restrictive architectures by focusing on the design logic that informs their construction. Platforms treat markets as search algorithms that blend software computation with human interactions. If the algorithm requires actors to follow narrow scripts of behavior, the platform should become more restrictive. This depends on the need for centralized computation, the degree to which required inputs can be standardized, and the misalignment of interests between users. The paper discusses how these criteria can be mobilized to explain the architectures of four illustrative cases. (shrink)

As states increasingly use algorithms to improve the legibility of society, particularly during the COVID-19 pandemic, it is common for concerns about the expanding power of the algorithm or the state to be raised in a deterministic manner. However, how are the algorithms for states’ legibility projects enacted, contested, and reconfigured? Drawing on interviews and media data, this study fills this gap by examining Health Code, the Chinese contact tracing and risk assessment algorithmic system that serves as the COVID-19 health (...) passport. I first explore the intensive and invisible work and infrastructures that enact and stabilize Health Code’s sociotechnical assemblage. I then show how this assemblage is frequently challenged and destabilized by errors, breakdowns, and exclusions. Facing unintended engagements from heterogeneous social actors, local interests, and power hierarchies, Health Code reassembles into multiple and contradictory assemblages at different periods and social localities. Finally, I examine how people game and bypass the algorithm’s surveillance with their agencies. Recognizing this messiness and heterogeneity contributes to a more nuanced and realistic understanding of states’ use of algorithms, including the risks. Doing so also urges us to rethink the politics of citizenship and inequality in the digital age beyond inclusion. (shrink)

International relations are made up of thick layers of meaning and big streams of data. How can we capture the nuances and scales of increasingly digitalised world politics, taking advantage of the possibilities that come with ‘big data’ and ‘digital methods’ in our discipline of International Relations? What is needed, we argue, is a methodological twin-move of making big data thick and thick data big. Taking diplomacy, one of IR's core practices as our case, we illustrate how anthropological and computational (...) approaches can be merged in IR research. We report from our experiences with the project DIPLOFACE: Diplomatic Face-Work between Confidential Negotiations and Public Display, investigating how digital communication technologies influence both the study and conduct of age-old and traditionally analogue practices of inter-state diplomacy. (shrink)