AI-Human Entanglement, Agency, and the Transformation of Governance

Introduction: Why the Framework Matters

As artificial intelligence (AI) becomes deeply woven into the operations of government, military, and civil society, the way decisions are made—and who or what makes them—is undergoing a profound shift. To understand the stakes, we need conceptual tools that clarify what is truly new and unsettling about this transformation. Two philosophical frameworks, repurposed for our era, provide this clarity: Wilfrid Sellars' distinction between the "space of reasons" and the "space of causes," and Jürgen Habermas's analysis of "system" and "lifeworld." By adapting these ideas, we can see how AI is not just a tool but a force reshaping the very structure of agency, accountability, and meaning in society.

Philosophical Foundations: Sellars and Habermas, Repurposed

1. Sellars: Reasons, Causes, and the Human/AI Divide

Sellars' Original Distinction:

The "manifest image" is the world as we experience it—where people act for reasons, deliberate, and explain themselves in terms of intentions and purposes.
The "scientific image" is the world as described by science—where all events, including human actions, are explained by physical, chemical, and biological causes.
Sellars argued that these are rival frameworks: when we see someone as a reason-giver, we inhabit the manifest image; when we see them as a collection of causal processes, we inhabit the scientific image. We can't do both at once.

A Contemporary Adaptation:

Human beings actually operate in both spaces at once. Our actions are shaped by reasons (deliberation, values, intentions) and causes (emotions, hormones, physical states) in a complex, interdependent way. For example, a decision to protest might be driven by both moral conviction and a surge of adrenaline.
AI, by contrast, operates only in the "space of causes." No matter how sophisticated, AI systems process data and generate outputs through algorithmic, causal processes—without understanding, intention, or reason-giving. Their "decisions" are not the result of deliberation but of mathematical optimization.
This distinction is crucial: while humans can justify and explain their actions, AI systems cannot. When we treat AI outputs as if they were reasoned decisions, we risk falling for the "user's illusion"—the mistaken belief that we remain in control, when in fact we are ceding agency to systems that do not and cannot reason as we do.

2. Habermas: System, Lifeworld, and the New AI Era

Habermas' Original Framework:

The "lifeworld" is the background of shared meanings, cultural knowledge, and communicative practices—where people interact, deliberate, and create social norms through language and mutual understanding.
The "system" is the realm of formal organizations, markets, and bureaucracies, governed by instrumental rationality—efficiency, control, and goal-oriented action.
Habermas warned that the expansion of system logic could "colonize" the lifeworld, crowding out spaces for genuine communication, reason-giving, and meaning.

An Updated Framework:

In the AI era, "system" now includes algorithmic infrastructures—AI platforms, data analytics, automated protocols—that are embedded in the very fabric of society.
These systems automate and reshape human reasoning itself, often bypassing or eroding the space for deliberation, evaluation, and shared meaning.
The colonization of the lifeworld is thus radically intensified: not only are bureaucratic procedures crowding out meaning, but so are non-sentient, algorithmic processes that lack any capacity for communicative understanding or normative judgment.

The User's Illusion: How Agency Transfer Is Masked

The "user's illusion" manifests across multiple levels, from personal interfaces to institutional processes. This psychological and structural phenomenon creates the false impression that humans remain in control while agency is systematically transferred to algorithmic systems. Understanding how this illusion operates is crucial to recognizing the scope of the transformation underway.

Contemporary Examples at the Personal Level

The newest "agentic AI" systems demonstrate this illusion most clearly. Users sign EULAs granting AI assistants access to credit cards and calendars, thinking they're enabling "smart recommendations." In reality, these systems make autonomous purchasing decisions—booking flights, ordering groceries, buying concert tickets to bands users have never heard of—while framing these actions as helpful suggestions.¹ Users experience convenience while ceding fundamental choice-making authority to algorithms operating purely through causal optimization.

How Institutions Sustain the Illusion

HR Departments: Managers believe they're "using AI tools to help with hiring decisions" while algorithms pre-filter 80-90% of candidates before any human review. The illusion of human judgment persists while the actual decision-making has been outsourced to systems that cannot explain their selection criteria in human terms.

Judicial Systems: Judges think they're "consulting risk assessment tools" while COMPAS or similar algorithms effectively determine sentencing recommendations that are rarely overridden. The appearance of judicial deliberation masks algorithmic determination of outcomes.

Medical Diagnosis: Doctors feel they're "leveraging AI assistance" while diagnostic algorithms increasingly drive treatment protocols and insurance approvals, often with physicians unable to explain or override algorithmic recommendations.

Interface Design That Conceals Agency Transfer

Modern AI systems use sophisticated interface design to maintain the illusion of human control:

"Recommendation" language: Framing algorithmic decisions as suggestions while providing no meaningful alternatives
Consent theater: Lengthy terms of service that bury actual agency transfers in legal language while creating the appearance of informed choice
Dashboard illusions: Control panels allowing minor preference adjustments while major algorithmic decisions remain hidden and unalterable

Case Study: Palantir, Trump-Era Policies, and the Outsourcing of Agency

The Rise of AI-Driven Infrastructure

Palantir's platforms (Foundry, Gotham) are now central to data integration and decision-making across US government agencies, including the Department of Defense, Homeland Security, IRS, and ICE.² Executive orders have mandated the elimination of "information silos," enabling the creation of centralized, cross-agency databases that aggregate sensitive personal data on millions of Americans.³

These platforms are not just tools—they are infrastructural substrates that mediate, automate, and even supplant processes of human deliberation across institutions. The integration represents a fundamental shift from human-centered decision-making to algorithm-mediated governance.

From Deliberate Abuse to Systemic Transformation

Deliberate abuses—such as the use of AI-driven platforms for surveillance, mass deportations, and targeting of protesters—are visible and alarming.⁴ But the more profound yet less visible transformation is the displacement of human judgment by algorithmic processes. Palantir's AI tools enable immigration enforcement practices that risk undermining due process, as evidenced by reported cases of arbitrary detentions and deportations without traditional legal protections.⁵

The Stealthier Transformation: The systematic replacement of human deliberation with algorithmic optimization occurs through several mechanisms:

Speed and scale requirements: Algorithmic governance enables rapid, large-scale interventions (mass deportations, predictive policing) that would be impossible under human-led systems
Opacity as standard practice: Automated decisions cannot be interrogated or justified in human terms, undermining traditional mechanisms of appeal and contestation
Efficiency imperatives: Institutional pressure to process more cases faster inherently favors algorithmic over deliberative approaches

The Illusion of Control in Government Operations

Many believe that humans remain in control of AI systems, as with traditional tools. In reality, agency is distributed and often ceded to opaque algorithmic processes. This "user's illusion" manifests in government through:

Policy language: Describing algorithmic outputs as "recommendations" while rarely overriding them
Procedural theater: Maintaining forms of human review that lack meaningful authority to alter algorithmic determinations
Technical mystification: Treating algorithmic complexity as inherently neutral rather than embodying particular values and decision criteria

Consequences: Accountability, Justice, and Social Fabric

Loss of Accountability and Reason-Giving

Opacity: Automated decisions cannot be interrogated or justified in human terms, undermining traditional mechanisms of appeal and contestation. When individuals ask "Why was this decision made?" the response increasingly becomes "The algorithm determined..." rather than a reasoned explanation.

Curtailment of Oversight: Policy changes limiting judicial review—particularly the Supreme Court's restriction of nationwide injunctions—further erode the capacity for human agents to check or reverse algorithmic outputs.⁶ The traditional separation of powers assumes human decision-makers who can be held accountable through reason-giving.

Erosion of Due Process: Individuals facing life-altering decisions—such as deportation—may have no opportunity for a human judge to hear their claims or weigh the moral stakes. The shift from reasons to causes eliminates the space for contextual judgment and moral consideration.

Societal Transformation

Speed and Scale: Algorithmic governance enables rapid, large-scale interventions that would be impossible under human-led systems. This transforms not just efficiency but the fundamental character of government action—from deliberative to mechanistic.

Dehumanization: The replacement of reason-giving with automated causality transforms individuals from moral agents deserving of consideration into data points to be processed. People become objects of system outputs rather than participants in meaning-making processes.

Reshaping Communities: As these practices scale, they alter the composition of the workforce, the structure of communities, and the broader constitution of society. The cumulative effect is a society increasingly organized around algorithmic optimization rather than human values and deliberation.

Tracking the Transformation: Empirical Measures of Agency Displacement

Institutional Metrics

Job Displacement Indicators:

Track positions specifically involving judgment, evaluation, or deliberation that have been automated (loan officers, diagnostic radiologists, parole officers)
Monitor human review rates: percentage of algorithmic outputs that receive meaningful human evaluation before implementation
Measure appeal/override frequency: how often algorithmic decisions are successfully challenged through human intervention

Process Transformation Indicators:

Document deliberation time reduction: average time allocated for human deliberation before vs. after AI implementation
Count stakeholder consultation elimination: number of voices/perspectives removed from decision-making processes
Analyze documentation changes: shift from narrative justifications to algorithmic output reports

Accountability Metrics

Reason-Giving Capacity:

Measure explainability gaps: percentage of decisions where institutions cannot provide human-intelligible justifications
Track black box prevalence: proportion of critical decisions made by systems whose decision-making process cannot be audited
Assess response to contestation: institutional ability to engage with challenges to decisions vs. "the algorithm decided"

Democratic Participation Indicators:

Monitor public comment opportunities and their influence on algorithmic policy implementation
Evaluate legislative/judicial review capacity over algorithmic governance systems
Track transparency access: public availability of algorithmic decision criteria and audit results

Behavioral and Social Metrics

Agency Atrophy Indicators:

Survey decision delegation rates: how often individuals defer to algorithmic recommendations vs. making independent choices
Assess critical evaluation skills through standardized testing of ability to evaluate information and detect bias
Measure preference articulation: people's ability to express and defend choices when algorithmic recommendations are removed

Meaning-Making Degradation:

Evaluate narrative coherence: individual ability to provide coherent accounts of their own decision-making processes
Track value reflection capacity: time and cognitive resources people dedicate to examining their goals and purposes
Monitor community deliberation: frequency and quality of collective decision-making in local communities and civic groups

Policy and Governance: Navigating the New Reality

Beyond Traditional Regulation

Regulatory models premised on external oversight are inadequate when agency is distributed and opaque. Traditional regulatory approaches assume human decision-makers who can be held accountable through reason-giving. Algorithmic systems fundamentally challenge this assumption by operating in the space of causes rather than reasons.

Policy must begin with a realistic description of our entanglement with AI, recognizing that humans are embedded within, not external to, these systems. We cannot regulate AI as if we stand outside it when our economic, social, and political systems are already dependent on algorithmic infrastructures for basic functioning.

Quantifying and Preserving Human Agency

Develop metrics to track the displacement of human judgment and the shift from "reasons" to "causes" across domains. This involves both quantitative measures (jobs eliminated, appeal rates, processing times) and qualitative assessments (capacity for reason-giving, democratic participation, individual agency).

Identify and protect domains where human purposive agency is essential—such as justice, care, and democratic deliberation. This requires not just preventing AI deployment in certain areas, but actively fostering practices and institutions that preserve the capacity for human evaluation, contestation, and reason-giving.

Navigate cybernetically within the system rather than attempting external control. Using negative feedback data about agency displacement, policy can guide deployment decisions to preserve human judgment where it matters most while accepting AI augmentation where it enhances rather than replaces human capabilities.

Conclusion: The Stakes of AI-Human Entanglement

The integration of AI infrastructures like Palantir's Foundry and Gotham into government and civil society marks a profound shift in the mode of governance and social coordination. The dangers are not limited to deliberate abuses, but extend to the stealthy, systemic outsourcing of human agency and the erosion of accountability, justice, and meaning.

By repurposing the frameworks of Sellars and Habermas, we gain a clear lens for understanding—and hopefully resisting—the colonization of the "space of reasons" by the "space of causes" in the age of AI. The challenge is not to reject AI entirely, but to navigate our entanglement with these systems in ways that preserve what is essentially human: the capacity for deliberation, reason-giving, and the creation of shared meaning through communicative action.

The metrics and approaches outlined here offer concrete ways to track and potentially reverse the displacement of human purposive agency. But time is limited—each day brings further entrenchment of algorithmic infrastructures and deeper erosion of the institutional and cultural practices that sustain democratic life. The question is not whether we will live with AI, but whether we can do so while remaining recognizably human in our capacity for moral reasoning, accountability, and meaning-making.

References

Footnotes

Beth Duckett, "Visa, Mastercard Offer Support for AI Agents," Retail, May 6, 2025, https://www.retaildive.com/news/visa-mastercard-ai-agents-payment-tools/716543/. ↩
Sheera Frenkel and Aaron Krolik, "Trump Taps Palantir to Compile Data on Americans," New York Times, May 30, 2025, https://www.nytimes.com/2025/05/30/technology/trump-palantir-data-americans.html. ↩
Executive Order, "Stopping Waste, Fraud, and Abuse by Eliminating Information Silos," The White House, March 20, 2025, https://www.whitehouse.gov/briefing-room/presidential-actions/2025/03/20/executive-order-on-stopping-waste-fraud-and-abuse-by-eliminating-information-silos/. ↩
"Trump Wants to Merge Government Data. Here Are 314 Things It Might Know About You," New York Times, April 9, 2025, https://www.nytimes.com/2025/04/09/technology/trump-government-data-collection.html. ↩
Mariana Olaizola Rosenblat, "Palantir Is Profiting from Trump's Ravenous Appetite for Deportations," NYU Stern School of Business, April 18, 2025, https://www.stern.nyu.edu/technology-democracy/palantir-2025-qt2. ↩
Abbie VanSickle, "Supreme Court Limits Judges' Ability to Issue Nationwide Injunctions, a Win for Trump," New York Times, June 27, 2025, https://www.nytimes.com/2025/06/27/us/supreme-court-nationwide-injunctions-trump.html.

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