In the age of Big Data, a new ideology threatens to reshape justice, ethics, and governance. First named “dataism” by David Brooks in 2013, it elevates data as the ultimate tool for understanding and solving social problems. Yuval Noah Harari and others argue that dataism does more than embrace technology; it reduces all human experience to quantifiable metrics. While proponents celebrate it as the next step in progress, the approach is dangerously simplistic and ignores the political, historical, and social forces that shape data and its interpretations.
Dataism claims that everything—individuals, societies, and systems—can be optimized through data analysis. It treats information as the fundamental unit of reality and argues that society should rely on data to make decisions, assuming this will yield more efficient, rational outcomes. Harari’s Homo Deus suggests that data and algorithms will eventually replace human intuition in determining what is good for individuals and society. This seemingly neutral reliance on data, however, raises profound concerns about justice and equality.
David Brooks' Neoliberal Fantasy: Data as a Solution for Everything
Brooks, one of the most prominent dataism advocates, argues that data-driven approaches provide a clear, objective means of solving societal issues. He frames dataism as a remedy for the messy subjectivity of human decision-making, promising a technocratic utopia where problems are reduced to datasets and fixed by experts with the right tools. Yet Brooks’ celebration is rooted in a neoliberal worldview that prioritizes efficiency over justice and equity. The technocratic approach overlooks the fact that data is always shaped by the biases of those who create and control it.
Brooks simplifies complex social issues, reducing nuanced human experiences to metrics. In his embrace of dataism he ignores the structural inequalities that determine who can create, access, and control data. Data is never neutral, and Brooks’ 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 underlying structures of oppression is a form of neoliberal denial, disguising inequality under the pretense of objectivity.
Yuval Noah Harari's Technological Determinism: Dataism as a Future We Cannot Escape
Harari’s vision in Homo Deus extends Brooks’ idea, painting a future where data becomes the ultimate arbiter of value and meaning. He predicts that data and algorithms will dominate every aspect of human life, leaving little room for judgment or agency. While Harari presents this as inevitable, it borders on fatalism. His deterministic outlook suggests that resistance to dataism is futile and that we must simply bow to the logic of algorithms. This perspective depoliticizes technology, ignoring how it can and should be subjected to democratic oversight.
Harari’s glorification of dataism is 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. By failing to engage with these corporate monopolies and the economic forces driving data’s commodification, Harari’s analysis remains superficial and incomplete. His technophilic gaze overlooks capitalism’s role in shaping the future he envisions, rendering his narrative disconnected from material realities.
Harari’s vision of data as the currency of the future ignores that data is often wielded to reinforce existing inequalities rather than dismantle them. Data systems frequently replicate and amplify societal biases, a point well documented by scholars like Ruha Benjamin.
Ruha Benjamin's Essential Critique: The New Jim Code and the Violence of Dataism
Benjamin’s critique corrects the blind optimism of figures like Brooks and Harari. In Race After Technology: Abolitionist Tools for the New Jim Code, she shows how emerging technologies—from everyday apps to complex algorithms—can perpetuate white supremacy under the guise of neutrality and progress. She introduces 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 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 masks deeply entrenched racism and inequality. As Benjamin argues, discriminatory design frequently “ignores and thereby replicates social divisions.” Even when developers aim to address bias, their tools often deepen it because they are built on historically biased data.
She also highlights the epistemic violence inherent in demanding that marginalized communities produce data to prove their oppression. This dynamic places the burden on the oppressed to translate lived experiences into quantifiable metrics, which are often decontextualized and stripped of meaning. The constant demand for data reinforces power imbalances and dehumanizes those most vulnerable to technological harms, embodying the “New Jim Code” of surveillance, quantification, and prediction.
Data Fetishism and Its Consequences: Flattening the Human Experience
Benjamin’s critiques, along with Shoshana Zuboff’s analysis of “surveillance capitalism” and David Graeber’s work on bureaucratic control, reveal dataism’s broader implications. The presumed objectivity of data naturalizes and justifies the status quo, making it harder to challenge entrenched power disparities. Operationalizing 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 data’s potential, they overlook how it can be weaponized against those already marginalized.
Steven Pinker's Misleading Optimism: Progress as Data
Pinker, a psychologist and public intellectual, argues that data can help us overcome cognitive biases and make more rational decisions. In Enlightenment Now, he marshals an impressive array of metrics to claim that, contrary to popular perception, the world is improving by almost every measure. Pinker’s optimism has faced 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 how measurement itself is shaped by human thought, feeling, and intent (Larson 2021). While Pinker’s reliance on data offers a compelling narrative of global improvement, it also obscures disparities that persist for those on the margins. His sweeping data-driven conclusions 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. As data justice scholar Linnet Taylor argues, we must move beyond a narrow focus on individual 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 justice.
It also requires cultivating a healthy skepticism of data‑driven claims, even from well‑credentialed experts like Pinker. Ruha Benjamin and others remind us that the most pernicious forms of bias often hide behind a façade of scientific objectivity. Challenging dataism is not about rejecting empiricism; it’s about ensuring that data serves justice and equity rather than reinforcing the status quo.
Ultimately, the struggle for justice in an age of dataism demands a new kind of data science—one grounded in the lived realities of marginalized communities, accountable to social movements, and oriented toward collective liberation. Only by critically interrogating the power structures behind dataism can we realize its emancipatory potential without succumbing to 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.
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Dataism is Junk Science
I’ve been thinking lately about the disappearance of classically debated ideas in the 21st century. Once upon a time fierce debates raged on in classrooms and conferences and around campfires about whether God existed or if scientific materialism were true. “Mechanism” challenged earlier notions championing the uniqueness of human minds, a view sometime…
2 years ago · 15 likes · 27 comments · Erik J Larson
- 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.