The News Event

Reporting from The Intercept reveals that Palantir built a powerful surveillance application called ELITE for Immigration and Customs Enforcement, and that new ICE hires were granted access to restricted immigration data through that app before completing required background checks. Compounding the problem, the federal government never published the legally mandated Privacy Impact Assessment for the system. This is not a story about AI going rogue. It is a story about institutional process collapsing under the weight of deployment velocity.

Deployment as Coordination Problem

There is a pattern worth naming here. Organizations adopt algorithmically-mediated systems faster than they develop the governance infrastructure to manage them. The ELITE situation is an extreme case, but the underlying dynamic is not unique to federal agencies. When a platform is deployed before its operators are vetted, before its privacy implications are formally assessed, and before accountability structures are in place, the system is not being coordinated with organizational reality. It is being dropped into a vacuum and left to run.

This connects directly to what my dissertation research identifies as the core inversion problem in platform-mediated coordination. Classical coordination theory, as summarized in Kellogg, Valentine, and Christin (2020), assumes that workers arrive with ex-ante competence. They know the rules before they interact with the system. Platform coordination inverts this: competence must develop endogenously, through participation. ELITE accelerated that inversion into a governance crisis. Operators who had not passed background checks were already inside the system, interacting with restricted data, before any legitimate coordination structure existed around them.

Palantir's Playbook and the Schema Problem

There is a separate but related story in the business press this week: Silicon Valley firms are increasingly copying Palantir's deployment model by hiring field engineers who embed directly with client organizations, earning upward of $188,000 annually to get AI systems operational on-site. The pitch is efficiency. Get the technology running first, train the client second. This is a procedural-first, schema-second model of implementation.

Hatano and Inagaki (1986) distinguished between routine expertise, which is tied to specific procedures, and adaptive expertise, which is anchored in structural understanding. A field engineer who can configure Palantir's data pipelines inside a federal agency is producing routine expertise in the client organization. The client learns to operate the tool. What the client does not necessarily develop is a structural schema for what the tool is doing, what constraints govern it, and what failure modes it carries. The absence of that schema is precisely what allowed the ELITE situation to unfold: the agency had operational access but not structural understanding of what access actually meant.

The Awareness-Capability Gap Has a Governance Analog

Algorithmic literacy research has documented what I call the awareness-capability gap: individuals can know that an algorithm governs their outcomes without knowing how to respond to it effectively (Gagrain, Naab, and Grub, 2024). The governance analog is this: institutions can know that a system requires oversight without having the structural capacity to exercise it. ICE presumably knew that a Privacy Impact Assessment was legally required. The requirement existed. The awareness existed. The institutional capacity to complete that process before deployment did not.

Rahman (2021) describes how algorithmic systems create invisible constraints on worker behavior, constraints that are real but structurally opaque. In the ELITE case, the opacity ran in the opposite direction. The constraints were visible on paper, in the form of legal requirements, but invisible in practice because no one built the process infrastructure to operationalize them. The result is what I would call a governance topology problem: the shape of the required oversight structure was known, but no one navigated it.

What This Means for Organizations Adopting AI Systems

The ELITE case is a useful limit case for a broader argument. When organizations adopt AI-mediated systems through deployment-first models, they create conditions where procedural access precedes structural understanding. The organization can use the system before it can govern the system. Sundar (2020) argues that machine agency introduces new accountability challenges precisely because users interact with outputs without understanding the processes that generate them. For individual users, this produces folk theories. For institutions, it produces governance gaps that carry legal and ethical exposure.

The practical implication is not that organizations should slow AI adoption categorically. It is that the sequence matters. Structural schema development, understanding what the system does, what data it touches, and what constraints govern it, needs to precede operational deployment, not follow it. When it follows, you get what happened with ELITE: a powerful system running inside an institution that was not yet ready to be accountable for it.

References

Gagrain, A., Naab, T. K., and Grub, J. (2024). Algorithmic media use and algorithm literacy. New Media and Society.

Hatano, G., and Inagaki, K. (1986). Two courses of expertise. In H. Stevenson, H. Azuma, and K. Hakuta (Eds.), Child development and education in Japan (pp. 262-272). Freeman.

Kellogg, K. C., Valentine, M. A., and Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366-410.

Rahman, H. A. (2021). The invisible cage: Workers' reactivity to opaque algorithmic evaluations. Administrative Science Quarterly, 66(4), 945-988.

Sundar, S. S. (2020). Rise of machine agency: A framework for studying the psychology of human-AI interaction. Journal of Computer-Mediated Communication, 25(1), 74-88.