The Ontological Edge: Using Latent Logic to Reframe Curatorial Decision-Making

Curatorial decisions in digital aesthetics are never neutral. Every selection, every framing, every label carries an implicit ontology—a set of assumptions about what the work is, where it belongs, and why it matters. Yet most curators operate without a systematic way to surface these assumptions. The result is a practice that often defaults to inherited categories: provenance, medium, artist intent, or market value. These categories work well for physical objects but break down when applied to generative art, networked performances, or AI-authorship works. This guide offers a different path: latent logic, a method for making the hidden structures of curatorial judgment explicit and actionable.

We write for experienced practitioners—curators, archivists, and theorists who already know the basics of digital preservation and aesthetic evaluation. What we offer is a framework to reframe decision-making from the ground up, not by adding more criteria but by examining the ontological commitments you already hold. By the end, you will be able to identify latent assumptions in your own practice, map them to specific curatorial choices, and build workflows that are both rigorous and flexible.

Why Latent Logic Matters for Digital Curators

Digital artworks challenge traditional curatorial frameworks because they are ontologically unstable. A generative piece may exist as code, as a live output, as a documented performance, or as a series of stills—each version carrying different claims about what the work 'is.' When a curator chooses one version over another, they are not just making a practical decision; they are making an ontological claim. Latent logic helps surface that claim by asking: What is the essential nature of this work, and how does that nature inform how we should treat it?

Beyond Provenance and Authenticity

Traditional curatorial decision-making relies heavily on provenance (who owned it, where it has been) and authenticity (is it the original?). For digital works, these concepts are often meaningless. A generative artwork may have no single original; a networked performance may exist only as a distributed event. Latent logic shifts focus from these external markers to the internal structure of the work itself. It asks: What are the core processes, materials, and relationships that define this work? How do these elements interact with the context in which the work is presented? By answering these questions, curators can make decisions that are faithful to the work's ontology, not just its history.

Composite Scenario: The Generative Portrait

Consider a generative portrait that uses a custom GAN trained on the artist's own photographs. The work exists as a Python script, a trained model, a set of output images, and a video documentation of the training process. A curator must decide which of these to acquire for a collection. Traditional logic might prioritize the output images as the 'finished' work. Latent logic, however, would examine the ontological structure: the core process is the training, the material is the data and code, and the output images are instantiations. A latent-logic decision might acquire the entire system—code, model, and a representative set of outputs—and present them as an evolving installation rather than a fixed set of prints. This reframing respects the work's generative nature and opens up new curatorial possibilities.

Why This Matters for Your Practice

Without latent logic, curators risk applying outdated frameworks to new media. The result is collections that misrepresent the works they contain, or worse, that actively distort the artist's intent. By adopting latent logic, you gain a tool for making consistent, defensible decisions that are grounded in the work's actual ontology. This is not about adding more rules; it is about understanding the rules you already use and making them explicit.

Core Frameworks: How Latent Logic Works

Latent logic is not a single technique but a family of approaches that share a common goal: making the implicit explicit. In practice, it involves three steps: surfacing assumptions, mapping them to decision criteria, and testing those criteria against real cases. We outline the core frameworks below.

Framework 1: Ontological Mapping

Ontological mapping is the process of identifying the key components of a digital artwork and their relationships. Start by listing all the elements that could be considered part of the work: code, data, hardware, documentation, display conditions, audience participation, etc. Then ask: Which of these are essential? Which are contingent? For example, in a generative artwork, the code might be essential, while the specific display monitor might be contingent. This mapping creates a blueprint of the work's ontology, which can then guide curatorial decisions about acquisition, preservation, and presentation.

Framework 2: Decision Criteria Derivation

Once you have an ontological map, you can derive decision criteria that are specific to that work. For example, if the mapping shows that the work's essence lies in its procedural generation, then a key criterion might be 'Does this acquisition preserve the generative process?' If the work is inherently networked, then 'Can we maintain the network infrastructure?' becomes important. These criteria are not universal; they emerge from the specific ontology of each work. This is what makes latent logic powerful: it tailors decision-making to the work itself, rather than applying a one-size-fits-all checklist.

Framework 3: Iterative Refinement

Latent logic is not a one-time analysis. As a work evolves—through new versions, different contexts, or changing technology—its ontology may shift. Curators should revisit their ontological mapping periodically and adjust their criteria accordingly. This iterative process ensures that curatorial decisions remain aligned with the work's current state. For instance, a piece that was originally conceived as a static image might later be re-released as an interactive web application. The ontology changes, and the curatorial approach must change with it.

Execution: A Repeatable Workflow for Applying Latent Logic

Knowing the theory is one thing; applying it in practice is another. Below we present a step-by-step workflow that teams can use to integrate latent logic into their curatorial process. This workflow is designed to be flexible—adapt it to your context.

Step 1: Inventory the Work's Components

Begin with a comprehensive inventory of everything that could be considered part of the work. This includes not only the obvious elements (files, hardware) but also less tangible ones (documentation, artist statements, audience interactions). Use a table or spreadsheet to list each component, its format, its dependencies, and its role in the work. This inventory is the raw material for ontological mapping.

Step 2: Identify Essential vs. Contingent Elements

With your inventory in hand, classify each component as essential or contingent. Essential elements are those without which the work would cease to be itself; contingent elements are those that could be replaced or removed without changing the work's fundamental identity. This classification is often subjective, but it can be guided by the artist's intent, the work's history, and your own curatorial judgment. Document your reasoning for each classification.

Step 3: Derive Curatorial Criteria

From your classification, derive a set of criteria that will guide decisions about acquisition, preservation, and presentation. For example, if a component is essential, you must ensure it is preserved in its original form. If it is contingent, you may allow substitutions. Write these criteria as actionable statements: 'We will preserve the original code in a version-controlled repository' or 'We will document the hardware specifications but allow future upgrades.'

Step 4: Test Against Real Cases

Apply your criteria to a small set of real or hypothetical works. This testing phase reveals gaps, contradictions, or unintended consequences in your criteria. For example, you might find that your criteria for preserving code conflict with the need to run it on modern systems. Adjust your criteria accordingly, and iterate until they produce consistent, sensible decisions.

Step 5: Document and Share

Finally, document your ontological mapping, your criteria, and your reasoning. Share this documentation with your team and, where appropriate, with artists and audiences. Transparency builds trust and invites feedback that can improve your process over time.

Tools, Stack, and Economic Realities

Implementing latent logic does not require expensive software, but it does require some basic tools and a willingness to invest time. Below we compare three common approaches to curatorial framing and discuss the economic considerations.

Comparison of Three Approaches

Economic Realities

Latent logic can be resource-intensive, especially in the initial mapping phase. For small institutions or independent curators, the time cost may be a barrier. However, the investment pays off in reduced risk of misrepresentation and increased coherence in collection development. To manage costs, start with a single work or small group of works, and build your process incrementally. Use open-source tools like Git for version control and Markdown for documentation. Avoid over-engineering; the goal is clarity, not complexity.

Maintenance Realities

Digital works require ongoing maintenance. Latent logic helps by making the dependencies explicit, so you can plan for preservation. For example, if a work depends on a specific library that is no longer supported, you can decide whether to emulate the old environment or migrate to a new one. Documenting these dependencies as part of your ontological mapping makes maintenance decisions more systematic.

Growth Mechanics: Positioning and Persistence

Adopting latent logic can also serve as a strategic differentiator for your curatorial practice. In a field where many institutions still apply traditional frameworks to digital works, being able to articulate a coherent ontological approach can attract artists, funding, and audiences who value depth over convention.

Positioning Your Practice

When communicating with artists, funders, or the public, frame latent logic as a way to honor the complexity of digital art. Emphasize that your approach is not about imposing rigid categories but about understanding each work on its own terms. This positions your institution as thoughtful and rigorous, which can be a strong selling point for artists who are wary of traditional gatekeeping.

Persistence Over Time

One challenge is maintaining consistency as staff changes or as works age. To ensure persistence, embed latent logic into your institutional documentation and training materials. Create templates for ontological mapping and decision criteria that can be used by future curators. Regularly review and update these templates as the field evolves.

Composite Scenario: Institutional Adoption

A mid-sized museum specializing in digital art decided to adopt latent logic across its collection. They started with a pilot project involving ten generative works. The initial mapping took two months, but it surfaced several inconsistencies in their existing acquisition policies. For example, they had been acquiring only output images for generative works, ignoring the code and documentation. After adopting latent logic, they revised their acquisition criteria to include the full system. This led to richer exhibitions and better relationships with artists, who appreciated the museum's nuanced approach.

Risks, Pitfalls, and Mitigations

Latent logic is not a silver bullet. It has limitations and can be misapplied. Below we discuss common pitfalls and how to avoid them.

Over-Analysis Paralysis

The most common pitfall is spending too much time on ontological mapping and not enough on action. To mitigate, set a time limit for each mapping session (e.g., two hours per work) and focus on the most essential elements. Remember that the goal is not perfect mapping but better decisions.

Ignoring Artist Intent

Latent logic can sometimes override the artist's own understanding of their work. Always involve the artist in the mapping process, or at least consult their documentation. If the artist considers a component essential, respect that, even if your analysis suggests otherwise.

False Precision

Ontological mapping can create an illusion of objectivity. In reality, many classifications are subjective. Be honest about the uncertainty in your mappings, and avoid making decisions that depend on fine-grained distinctions that are not robust. Use ranges or multiple scenarios where appropriate.

When Not to Use Latent Logic

For works that are well-understood within existing frameworks (e.g., traditional video art), latent logic may add unnecessary complexity. Reserve it for works that genuinely challenge curatorial norms. Also, avoid using it as a substitute for ethical considerations; ontology does not replace equity, access, or community engagement.

Decision Checklist and Mini-FAQ

Decision Checklist

Use this checklist when evaluating a new digital work for acquisition or exhibition:

• Have you inventoried all components of the work?
• Have you classified each component as essential or contingent?
• Have you derived decision criteria from your classification?
• Have you tested your criteria against at least one real or hypothetical case?
• Have you documented your reasoning and shared it with stakeholders?
• Have you set a schedule for revisiting the mapping?

Mini-FAQ

Q: Do I need to do this for every work in my collection?
A: No. Focus on works that are ontologically complex—generative, networked, or AI-authorship works. For simpler works, traditional frameworks may suffice.

Q: What if the artist disagrees with my mapping?
A: Defer to the artist's intent when possible. If a disagreement persists, document both perspectives and make your decision transparent.

Q: How often should I revisit the mapping?
A: At least once a year, or whenever the work undergoes a significant change (e.g., a new version, a different display context).

Q: Can latent logic be applied to physical artworks?
A: Yes, but it is most useful for works that have digital components or that challenge traditional notions of medium and authenticity.

Synthesis and Next Actions

Latent logic offers a way to bring rigor and flexibility to curatorial decision-making in the digital age. By surfacing the ontological commitments embedded in your practice, you can make choices that are more faithful to the works you steward. The key is to start small, iterate, and stay humble about the limits of any framework.

Next Actions

Begin by selecting one work in your current collection that you find ontologically puzzling. Apply the inventory and classification steps from this guide. Document your findings and share them with a colleague. Use the feedback to refine your approach. Over time, build a library of mappings that can inform your entire practice.

We encourage you to see latent logic not as a fixed method but as a living conversation—with artists, with technology, and with the evolving nature of digital aesthetics. The edge you gain is not a competitive advantage but a deeper understanding of the works you care for.