In the age of Big Data, a new ideology has emerged that threatens to reshape how we approach justice, ethics, and governance. This ideology, first named "dataism" by alleged human David Brooks in 2013, elevates data as the ultimate tool for understanding and solving social problems. However, as Yuval Noah Harari and others have argued, dataism does not just represent an embrace of technology; it reflects a worldview that reduces all human experience to quantifiable metrics. While proponents of dataism, like Brooks and Harari, celebrate it as the next step in human progress, this approach is dangerously simplistic, ignoring the political, historical, and social forces that shape data and its interpretations.
At its core, dataism promotes the notion that everything—individuals, societies, and systems—can be optimized through data analysis. The philosophy behind dataism sees information as the fundamental unit of reality and asserts that society should rely on data to make decisions, assuming that this will lead to more efficient, rational outcomes. This viewpoint, as Harari discusses in Homo Deus, proposes that data and algorithms will eventually supersede human intuition and judgment in determining what is good for individuals and society as a whole. This seemingly neutral and objective reliance on data, however, is fraught with profound issues, especially regarding justice and equality.
David Brooks' Neoliberal Fantasy: Data as a Solution for Everything
David Brooks, one of the most prominent advocates of dataism, has long argued that data-driven approaches can provide a clear, objective means of solving societal issues. He portrays dataism as a solution to the messy, subjective nature of human decision-making, promising a technocratic utopia where social problems are reduced to datasets and fixed by experts armed with the right tools. However, Brooks' celebration of data is deeply rooted in a neoliberal worldview that privileges efficiency and optimization over justice and equity. This technocratic approach reflects a dangerous oversight: it ignores the fact that data is always shaped by the biases of those who create and control it.
Brooks has a history of simplifying complex social issues, reducing nuanced human experiences to metrics. In his embrace of dataism, Brooks fails to account for the structural inequalities that determine who gets to create, access, and control data. Data is never neutral, and Brooks' dataist dream conveniently overlooks how data-driven technologies often perpetuate systemic discrimination and exacerbate existing power imbalances. His belief that dataism can solve society's problems without addressing the underlying structures of oppression is a form of neoliberal denialism, one that disguises inequality under the pretense of objectivity.
Yuval Noah Harari's Technological Determinism: Dataism as a Future We Cannot Escape
Yuval Noah Harari's embrace of dataism in Homo Deus takes Brooks' vision even further, painting a future where data becomes the ultimate arbiter of value and meaning. Harari predicts that data and algorithms will dominate every aspect of human life, leaving little room for human judgment or agency. While Harari presents this vision as inevitable, it borders on fatalism. His deterministic outlook suggests that resistance to dataism is futile, that we must simply bow to the logic of algorithms and metrics. This perspective dangerously depoliticizes technology, ignoring the ways in which it can and should be subjected to democratic oversight and resistance.
Harari's glorification of dataism as a form of transcendence is deeply disconnected from reality. Most of the world's data is owned, controlled, and exploited by a handful of powerful corporations that profit from surveillance and commodification. Harari's failure to engage with these corporate monopolies and the economic forces driving the commodification of data makes his analysis superficial and dangerously incomplete. His technophilic gaze overlooks the role of capitalism in shaping the future he envisions, rendering his narrative disconnected from the material realities that shape the digital age.
Harari's vision of data as the currency of the future, the ultimate truth, ignores the fact that data is often wielded to reinforce existing inequalities rather than dismantle them. Data systems often replicate and amplify societal biases, a point well-documented by critical scholars like Ruha Benjamin.
Ruha Benjamin's Essential Critique: The New Jim Code and the Violence of Dataism
Ruha Benjamin's critique of dataism offers a much-needed corrective to the blind optimism of figures like Brooks and Harari. In her groundbreaking book Race After Technology: Abolitionist Tools for the New Jim Code, Benjamin demonstrates how emerging technologies, from everyday apps to complex algorithms, can perpetuate white supremacy under the guise of neutrality and progress. She introduces the concept of the "New Jim Code" to describe how discriminatory designs encode racial hierarchies, often amplifying the very inequalities they purport to address.
Benjamin's work makes it clear that data is never neutral; it is always shaped by the social, political, and historical contexts in which it is produced. The veneer of objectivity surrounding Big Data analytics and AI decision-making systems often serves to mask and legitimize deeply entrenched forms of racism and inequality. As Benjamin argues, discriminatory design frequently "ignores and thereby replicates social divisions." Even when tech developers aim to address bias, their tools frequently end up deepening it, as they are built on a foundation of historically biased data.
Benjamin also highlights the epistemic violence inherent in the demand for marginalized communities to produce data to prove their own oppression. This dynamic places the burden on the oppressed to translate their lived experiences into quantifiable metrics, which are often decontextualized and stripped of meaning. The incessant demand for data from these communities reinforces power imbalances and dehumanizes those already most vulnerable to technological harms. It reflects what Benjamin terms a "New Jim Code"—an updated mode of racial discrimination that operates through the tools of surveillance, quantification, and prediction.
Data Fetishism and Its Consequences: Flattening the Human Experience
The critiques put forth by Benjamin, along with Shoshana Zuboff's analysis of "surveillance capitalism" and the late David Graeber's work on bureaucratic control, reveal the broader implications of dataism's obsession with metrics. The presumed objectivity of data serves to naturalize and justify the status quo, making it harder to challenge entrenched power disparities. The operationalization of complex social phenomena into narrow quantitative measures flattens the rich texture of human experience, erasing alternative ways of knowing and being.
As more social policies are driven by datafication, the demand for marginalized communities to constantly prove their oppression through data grows, reinforcing systems of surveillance and control. This dynamic is profoundly dehumanizing and exacerbates the inequalities that dataism claims to resolve. While dataists like Brooks and Harari celebrate the potential of data to improve society, they overlook the ways in which data can be weaponized against those already marginalized.
Steven Pinker's Misleading Optimism: Progress as Data
Proponents of data-driven approaches, like psychologist and public intellectual Steven Pinker, often argue that data can help us overcome cognitive biases and make more rational decisions. In his book Enlightenment Now, Pinker marshals an impressive array of metrics to argue that, contrary to popular perception, the world is getting better by almost every measure. Pinker's optimism, however, has been met with significant criticism. Critics argue that his analysis downplays the unevenness of progress and the persistence of systemic inequities.
As Erik Larson notes, Pinker's work often rests on a naive faith in the givenness and objectivity of data, failing to grapple with the ways in which measurement itself is shaped by human thought, feeling and intent (Larson 2021). While Pinker's reliance on data provides a compelling narrative of global improvement, it also obscures the disparities that persist for those on the margins. Pinker's sweeping data-driven conclusions, like those of Brooks and Harari, risk flattening complex social realities into neat, quantifiable trends, missing the nuanced understanding required to address deep-rooted inequalities.
Beyond Dataism: Towards a More Just Data Science
The way forward is not to reject data altogether but to develop a more nuanced, context-sensitive approach to its use. As data justice scholar Linnet Taylor argues, we need to move beyond a narrow focus on individual data rights to consider the collective and structural dimensions of data harms and governance (Taylor 2017). This means centering the perspectives and priorities of marginalized communities in the development and deployment of data systems, subjecting metrics and algorithms to ongoing audits and impact assessments, and being willing to reject or redesign technologies that fail to serve the interests of justice.
It also means cultivating a healthy skepticism of data-driven claims, even (or especially) when they come from well-credentialed experts like Pinker. As Ruha Benjamin and others remind us, the most pernicious forms of bias and discrimination often hide behind a facade of scientific objectivity. Challenging dataism is not about rejecting empiricism; it's about ensuring that data serves the interests of justice and equity, rather than reinforcing the status quo.
In the end, the struggle for justice in an age of dataism requires us to build a new kind of data science—one that is grounded in the lived realities of marginalized communities, accountable to social movements, and oriented towards collective liberation. Only by critically interrogating the power structures behind dataism can we hope to realize the emancipatory potential of data without succumbing to its oppressive logics.
References:
Benjamin, R. (2019). Race after technology: Abolitionist tools for the new jim code. John Wiley & Sons.
Larson, E. J. (2021, October 12). Dataism is Junk Science. Colligo.
Taylor, L. (2017). What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society, 4(2), 2053951717736335.
Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. Public Affairs.