Uses of advanced artificial intelligence are changing how societies organize labor, govern, produce knowledge, and make meaning. In light of these developments, this essay argues that AI models, tools, and systems pose three interrelated imperatives for social science: they demand renewed attention to social theories of how technology, human experience, and social order are entangled; they require study as objects of inquiry in their own right; and they offer capabilities that may transform—or upend—the practice of social investigation itself. From Weber’s analysis of rationalization to Du Bois’s study of technology and inequality to contemporary scholarship on algorithmic governance, the essay examines what social science distinctively offers: the capacity to historicize the apparently unprecedented, to trace connections across scales, and to center those most affected by technological change. It identifies how algorithmic systems are remaking the distribution of opportunity and risk as a central task of social inquiry and asks what futures social science might help bring into being.
AI as Social Technology: Astor Lecture by Professor Henry Farrell
New Studies: How Commercial Forces Make Science Less Reliable
Computational social science has been distorted by commercial forces, and AI is making it worse.
B. Scot Rousse: "Language, Technology, & Care"
From the Platform Society to the AI Society: Towards Critical Studies of Generative AI
The era of AI has begun. Generative AI is rapidly reshaping knowledge production, culture, and political authority, giving rise to an emerging AI society. Yet this transformation did not emerge ex nihilo. This paper argues that the AI society can only be understood in relation to the platform society from which it arises. Tracing the transition from platforms to AI, we identify interlinked economic, epistemic, and political shifts. Economically, AI emerges within platform-based rentier capitalism but reconfigures the monopoly mechanisms on which its accumulation depends. Epistemically, LLMs mark a shift from predictive to generative epistemics, entangling theory formation and knowledge production with private research-as-a-service infrastructures. Politically, governance shifts from data politics to alignment politics: from shaping visibility to shaping what can be said, thought, and imagined. Together, these transformations signal a qualitative shift in mediation—from governing interaction to governing cognition itself—and call for a Critical AI Studies.
Charting AI’s Role in Scientific Discovery — Renaissance Philanthropy – A brighter future for all through science, technology, and innovation
Renaissance Philanthropy, with support from Google.org , is conducting a landscape study of AI integration in scientific research — and we want your perspective.
Large AI models are cultural and social technologies
Implications draw on the history of transformative information systems from the past , Debates about artificial intelligence (AI) tend to revolve around whether large models are intelligent, autonomous agents. Some AI researchers and commentators speculate that we are on the cusp of creating agents with artificial general intelligence (AGI), a prospect anticipated with both elation and anxiety. There have also been extensive conversations about cultural and social consequences of large models, orbiting around two foci: immediate effects of these systems as they are currently used, and hypothetical futures when these systems turn into AGI agents—perhaps even superintelligent AGI agents. But this discourse about large models as intelligent agents is fundamentally misconceived. Combining ideas from social and behavioral sciences with computer science can help us to understand AI systems more accurately. Large models should not be viewed primarily as intelligent agents but as a new kind of cultural and social technology, allowing humans to take advantage of information other humans have accumulated.
AI, Decomputing and the Interregnum
This paper treats AI as diagnostic for the deeper changes taking place in the existing order of things. It uses AI's alignment with both the political economy and with the dualisms that underpin it, including race, gender and anthropocentrism, to highlight the nihilistic character of the current restructuring. AI's scaling and accelerationism are taken as examples of the wider tactics being invoked by hegemonic power to maintain control under changing conditions. From this perspective, the massive build-out of data centres isn't simply a seizure of energy resources but a manifestation of an aggressive and misogynist technopolitics. The paper argues that a liberal push for digital sovereignty doesn't interrupt these dynamics but plays into the hands of emerging technofascism. It proposes instead the prefigurative tactic of 'decomputing', which draws on degrowth, deautomatisation and a convivial approach to technology. It explores decomputing as a means to mitigate both material and relational harms and as a decisive turn towards infrastructuring the common good. The paper concludes that AI is the contradiction that reveals many others, not least the gap between claims to legitimacy and the actuality of destructive violence, and proposes an alternative technopolitics of reciprocity that prioritises care and sustainability.
AI, Decomputing and the Interregnum
This paper treats AI as diagnostic for the deeper changes taking place in the existing order of things. It uses AI's alignment with both the political economy and with the dualisms that underpin it, including race, gender and anthropocentrism, to highlight the nihilistic character of the current restructuring. AI's scaling and accelerationism are taken as examples of the wider tactics being invoked by hegemonic power to maintain control under changing conditions. From this perspective, the massive build-out of data centres isn't simply a seizure of energy resources but a manifestation of an aggressive and misogynist technopolitics. The paper argues that a liberal push for digital sovereignty doesn't interrupt these dynamics but plays into the hands of emerging technofascism. It proposes instead the prefigurative tactic of 'decomputing', which draws on degrowth, deautomatisation and a convivial approach to technology. It explores decomputing as a means to mitigate both material and relational harms and as a decisive turn towards infrastructuring the common good. The paper concludes that AI is the contradiction that reveals many others, not least the gap between claims to legitimacy and the actuality of destructive violence, and proposes an alternative technopolitics of reciprocity that prioritises care and sustainability.
AI, Ethics, and Society — Home
Bridging the gap: inequalities that divide those who can and cannot create sustainable outcomes with AI
The widespread use of AI technologies impacts individuals, organisations, and societies in ways that warrant more comprehensive investigation. Inequalities in benefiting from AI advancements may le...
Building Political Superintelligence
Amidst fears of dystopia, a blueprint for how we use AI to reinvent the way we govern ourselves
AI as Social Technology
Our debates about ‘AI’ grow out of 1990s science fiction. Back then, Vinge (1993) wrote essays and novels urging us to face up to the oncoming “Singularity”: a moment of rapid change that would fundamentally transform the human condition. On that day, AI would rapidly evolve from merely human-level intelligence, what some now call ‘artificial general intelligence’ (AGI), into something super-intelligent with its own interests and goals. Humanity would then either be casually eliminated by out-of-control machines, or humans would become as gods, with super-human servitors at our command.
The Scaling Era: An Oral History of AI, 2019–2025
An inside view of the AI revolution, from the people an…
"AI" is Automated Inequality
Tech bros still dominate the discussions about so-called "AI" with false claims. Even most "AI"-critical researchers spend much of their time meticulously debunking (always only a subset of) claims, leaving vast areas of the economic consequences of "AI" unexplored. (Even the "AI"-evangelist Economi
Remarkable how over a decade ago @michaelnielsen.bsky.social pointed the way towards solving long standing issues plaguing science to this day (issues that are all the more relevant in the age of AI mediated science)
Ronen Tamari
This is why @atproto.science is so relevant rn This compilation of essays indicates that scientists are most frustrated by insufficient “community tools and resources... Essential infrastructure for sharing, maintaining and building on existing work and data is also badly underdeveloped." >