Drawing on films and documentaries about big data, machine learning and AI, including analysis of the sociological and critical theory of AI, ‘Screening Big Data: Films That Shape Our Algorithmic Literacy‘ by Gerald Sim discusses the role of popular media in the formation of algorithmic literacy.
In this blog post, Jenn Chubb explains how Sim’s book provides a rich and vital analysis of the socio-political dimensions of stories and visuals about AI which challenge audiences to think more carefully about the motivations and interests behind tech-driven media.
Having spent the past few years researching stories about AI and forgotten or overlooked aspects of AI literacy, I am delighted to read Gerald Sim’s book ‘Screening big data: films that shape our algorithmic literacy.’ I have seen all of the films he discusses and have only begun to scratch the surface of the messages they propagate. In this book, Sim goes further and demonstrates in a sophisticated way how films and documentaries about AI are directing the public response. He calls for the reader to identify the influence of these stories, guiding the reader to decode the motivations and interests behind tech-driven stories.
For those of us concerned about the imagery associated with AI, there is so much to learn from this book. In addition to framing the book in terms of ‘cinema’s reliance on visuality’, Sim writes that “research in science communication has long held that visual literacy is crucial.” In fact, the images we see on screen are an important part of how the public form opinions about technology because they are often ideologically framed and carefully curated. Understanding this requires a critical “visual literacy” and countervisuality that media scholars, drawing on film theory, use to challenge the seemingly transparent portrayals of technology in the media.
I will start by saying that Sim has a politically sharp lens, and skillfully makes connections between culture, technology, and politics, challenging the reader to question whose interests popular culture serves. The films in question are close readings of; ‘Minority Report’, ‘Moneyball’, ‘The Social Dilemma’ and ‘Coded Bias’, and they make for great case studies. If I am being picky, the latter documentaries carry even greater responsibility not to adopt problematic framings, which Sim acknowledges. Yet according to Sim there is a commonality; they are reflective of a network of media and technology institutions which are exerting political power in favour of their own interests.
Screening Big Data begins with a pointed example. It’s not an example from science fiction (in fact, Sim is clear that his focus is on narrow AI and algorithms, not the stuff of superintelligence or AGI). Instead, the book begins with the example of polling data, used to exemplify that algorithms have a political and human backbone. This is a useful device, because in the same way, films don’t just entertain; they propagate powerful ideologies. For Sim, stories aren’t neutral, they’re influential and drive public attitudes, spark policy discussions, and even sway governmental perspectives. Films, documentaries, and media coverage become, as Sim frames it, cultural drivers of ideology. It is particularly refreshing to me that Screening Big Data avoids Hollywood’s usual focus on superintelligent AI tropes, such as those seen in ‘Ex Machina’ or ‘Blade Runner.’ As he rightly points out there has been great work on this by scholars working on the Global AI Narratives project and more. Instead, Sim examines narrow forms of AI and machine learning – e.g. the systems currently impacting society, from facial recognition to predictive policing. These technologies, what Cathy O’Neill called “Weapons of Math Destruction”, shape real, everyday lives in ways that go largely unexamined.
Algorithmic and social imaginaries
From this position, Sim borrows from Science and Technology (STS) literature and Frankfurt School of critical theory to consider the effects of film on public perception. With respect to the former, Sim argues that films contribute to what sociologists call ‘algorithmic imaginaries’ or more simply put, the ways in which one might conceptualise and understand the potential and risks of algorithms. He draws on the works of scholars like Sheila Jasanoff and Taina Bucher to explore how these cultural narratives reinforce ideas about AI’s role in society. Sim’s account of the imaginary is rich, a lesson in algorithmic literacy itself.
However, Sim also notes the narrow focus of these portrayals, which often sidelines the broader societal impacts in favour of dramatic dystopian futures or reductive narratives. As Sim implies, the stories or scenes which reinforce polarisation are often the ones that get ‘stuck’ in cultural time and space. ‘Minority Report’ is a prime example of this, often praised for its predictive depictions of technology, that of spatial computing, biometric scanners, and gesture-based interfaces, much of which has endured and entered the real world today. Such depictions stick in the public consciousness, framing technology in polarising terms – either a dystopian threat or as an empowering tool.
Technomedia industrial complex
I mentioned in my introduction that Sim’s argument is framed in such a way that suggests these films are reflective of a network of media and technology institutions exerting political power in favour of their own interests. He explores the ‘technomedia industrial complex’, a web of media and tech institutions, in which companies like Google, Netflix, and Facebook wield significant power. This is really at the heart of his book and I am convinced by his articulation of the films as vehicles which ‘peddle industry ideology’, technological fantasy (however prescient) and documentaries that simplify complex issues especially concerning science communication.
One such ideology has a long history – Films like ‘Moneyball’, for example, depict data as a ‘moral and virtuous truth,’ and present data scientists as objective crusaders while downplaying the biases and ethical questions surrounding AI. This framing resonates with me – Sim has chosen examples which exemplify the assumption that data scientists are objective number crunchers, which, he states, provides ‘cover from scrutiny’. Apparently, it’s the qualitative aspect that needs fixing, (isn’t that always the case?) – the human is qualitative, the machine is quantitative – and the human fails. Social science is in question, hard science is not. As a qualitative type who relies on ‘anecdotes and adhoc thinking’ (..!..), I am convinced that these narratives reinforce the longstanding two cultures debate. So too, they cause us to reflect on how we imagine the role of the scientist… (Clue, are they really all data geeks who can’t possibly grasp ethics…?)
One might think that including two documentaries would make for an interesting counterpoint to the stuff of fiction. However while both raise technological literacy about pervasive technologies they also arise from the techlash. Sim explains that the documentary ‘The Social Dilemma’, dramatises the dangers of algorithms but risks framing technology as something beyond human control, an idea that allows tech companies to shirk responsibility. I could not be in more agreement concerning the framing of this documentary which opens with an apology for opening Pandora’s box by the very people who opened it and plays into the tropes that humans have no control whatsoever. Meanwhile, ‘Coded Bias’ which rightly tackles facial recognition biases can be criticised for simplifying complex geopolitical issues by focusing on surveillance concerns in countries like China without the same scrutiny on Western practices. The extent to which this is entirely supportive of our algorithmic literacy is then rightly questioned.
What difference does it make?
“The narrow truth about whether traditional film genres have been superseded may seem insignificant, but their continued relevance provides good reason to be wary of techno-determinist braggadocio and of how easy it is to be caught in the slipstream of techno-optimist celebration and techno-libertarian currents.”
I think it’s important to note that Sim is not critical of the films for their artistic merit. He is simply calling for reflection on how they impact us. If I had to criticise, Sim’s book could focus more on the social and subtle emotional cues in the films. For instance, the dominance of white men narrating, or the manipulative musical scores directing our emotions towards the binary positions he warns us about. I might also look for further contrast in the cases used – for instance to seek counterpoints in the framings of AI across commercial vs ‘art-house’ films where intention will be very different. Perhaps that is where we might find strong examples to guide a more considered, responsible and nuanced approach to storytelling.
But that really is another story. His provocation extends beyond simply understanding AI. While critical, it is optimistic and stands for civic and public mobilisation and a shift toward resistance such as that described by scholars Dan McQuillan and Kate Crawford toward disingenuous mantras of tech companies. In fact, I take comfort in the ways that Sim frames his view of the collective response to AI.
“If you would indulge in some optimism of a different sort, I might venture that what the integrity of genres reveals, is that we are more resilient to capitalist atomization than we realise.”
These films direct our individual and collective response to technology, subtly encouraging acceptance or resistance. To respond requires education of algorithmic technology and an avoidance of its reification. We do well to scrutinise the technopolitics of storytelling and to critically engage with the media we consume to reveal the political and economic interests going on behind the scenes. This book is a crucial read for anyone interested in the hype of AI, and should be indispensable to anyone researching or teaching the socio-political and cultural aspects of AI in Higher Education.
Dr Jenn Chubb is a Lecturer in the Department of Sociology at the University of York, UK. Jenn’s research explores the societal and ethical implications of science and technology with a particular focus on the public perception of AI across the domains of science policy, education, health and the creative industries.
Sim, G. (2024). Screening Big Data: Films that Shape Our Algorithmic Literacy. Taylor & Francis.
You can hear more about the book on the New Books Network podcast.