Beyond the Button, The Next Steps: When Validation Becomes Empirical

In my 2026 Journal of Forensic Sciences (JFS) paper, A research‑focused framework for empirical method validation in digital and multimedia evidence, I outlined a structured pathway for developing and validating novel forensic methods. The early stages of that framework—feasibility studies and scientific foundation gap analyses—were designed to help researchers move beyond tool‑centric habits and into the scientific discipline that courts and standards bodies now expect.

Recently, during a discussion about my long‑term Forensic Machine Learning (ML) Framework project, someone asked a deceptively simple question:

“What is the real difference between an exploratory study and a gap analysis?”

The question is timely. As machine learning accelerates into forensic practice, the distinctions between feasibility studies, gap analyses, and exploratory studies are becoming increasingly important. These study types serve different scientific purposes, answer different questions, and occupy different positions in the validation framework. Treating them as interchangeable undermines both scientific rigor and legal defensibility.

This post revisits the structure I published in my 2026 JFS paper and expands it to clarify how these study types function within the validation framework—especially for novel method development such as forensic ML. In particular, I explain why exploratory studies have become an essential third category for emerging domains where the scientific and legal foundations are not yet established.

1. Where These Study Types Fit in the Validation Framework

In my validation‑study framework, the early stages of novel method development fall into three categories:

Feasibility Study
Scientific Foundation Gap Analysis
Scientific Foundation Exploratory Study

These stages are not interchangeable.
They build on each other in a forensic‑driven sequence.

A feasibility study asks whether a scenario, signal, or mechanism is scientifically plausible and worth investigating.

Feasibility → discovery

A scientific foundation gap analysis examines what the literature, current practice, and existing protocols provide—and identifies what is missing relative to forensic requirements.

Scientific Foundation Gap Analysis → identify what is missing

A scientific foundation exploratory study then defines the scientific, legal, and operational foundations of the domain so that a forensic‑grade method or protocol can be developed.

Scientific Foundation Exploratory Study → define the domain

This structure was present in my 2026 JFS paper, but the role of exploratory studies—especially for emerging domains like forensic ML—deserves clearer articulation. In practice, exploratory studies often arise only after a feasibility study and a gap analysis reveal that the domain itself must be defined before validation can occur.

2. What a Feasibility Study Really Is

A feasibility study is the earliest, lowest‑stakes scientific activity.
It is not forensic.
It is not evidentiary.
It is not admissibility‑focused.

Its purpose is simple:

Determine whether a concept, signal, or mechanism appears to exist and is worth further scientific investigation.

A feasibility study:

explores whether a measurable effect exists
uses small datasets or controlled conditions
does not attempt to generalize
does not evaluate forensic requirements
does not claim operational readiness

In ML, feasibility studies often look like:

“Can a model distinguish real vs. synthetic audio under ideal conditions?”
“Is there a detectable artifact in diffusion‑model images?”
“Does a classifier show promise on a narrow, controlled dataset?”

Feasibility studies are discovery‑oriented, not evaluative.

3. What a Scientific Foundation Gap Analysis Is

A Scientific Foundation Gap Analysis (SFGA) plays a very specific role in the early stages of novel method development. In my workflow, a gap analysis is only appropriate after feasibility is established and before an exploratory study is undertaken.

Its purpose is:

Identify what is missing between current practice and the forensic requirements that would apply to the scenario or method under consideration.

A gap analysis is not about defining the domain.
It is about determining whether the existing scientific, operational, or legal foundations are sufficient—and if not, where the deficiencies lie.

A scientific foundation gap analysis:

examines what the literature, standards, and existing protocols currently provide
maps claims → requirements → evidence → gaps
evaluates whether current methods or assumptions satisfy forensic requirements
identifies missing validation, missing transparency, and missing error‑rate characterization
highlights risks to admissibility, reliability, and due process
is inherently forensic in nature, because it is grounded in forensic requirements

In my own work, this step often reveals that the domain itself is not yet defined.
For example, when evaluating whether existing digital and multimedia forensic methods could support an investigation into an ML‑driven incident (such as a hypothetical lunar‑lander failure), the gap analysis showed:

no public ML‑forensic investigative protocol exists
existing DME methods do not address ML decision pathways
Based on publicly available sources, NASA’s current documentation does not describe ML‑specific, forensic‑ready incident investigation procedures for spacecraft systems; existing materials focus on general mishap investigation, software assurance, and AI risk management rather than forensic reconstruction of ML decision pathways.
forensic requirements cannot be met with current tools or workflows

That is the gap.

Gap analyses are evaluative, not exploratory.
They tell us what is missing, not what the domain should be.

Once the gaps are identified, the next step is the Scientific Foundation Exploratory Study, which defines the domain and articulates the scientific and legal foundations needed to build a forensic‑grade method or protocol.

4. Why Exploratory Studies Are Needed in Forensic ML

In traditional forensic disciplines, the scientific foundations already exist.
In machine learning, they often do not. This becomes clear only after feasibility and a scientific foundation gap analysis reveal that the domain itself is undefined.

This is where Scientific Foundation Exploratory Studies (SFES) become essential.

An exploratory study is appropriate when:

a feasibility study shows the scenario is scientifically plausible
a gap analysis reveals that existing methods, standards, or protocols cannot meet forensic requirements
the domain lacks established forensic requirements or investigative frameworks
the scientific, legal, and operational foundations must be articulated before a method can be built

Its purpose is:

Define, characterize, and articulate the scientific, legal, and operational foundations of a domain so that a forensic‑grade method or protocol can be developed.

A scientific foundation exploratory study:

defines the domain and its boundaries
identifies the scientific principles relevant to forensic use
maps legal, evidentiary, and due‑process constraints
explores operational contexts and investigative pathways
articulates what “forensic‑grade” would require in this domain
establishes the foundations needed for future validation studies

Exploratory studies are foundational, not evaluative.
They do not test tools or claims.
They build the domain that future forensic methods must inhabit.

This is why two of my current Forensic ML Framework papers—
(1) Legal Requirements for Forensic ML and
(2) Investigative‑Only ML Use by Law Enforcement—are not gap analyses. They are exploratory studies.

The gap analyses revealed that:

no forensic ML investigative protocols exist
existing digital and multimedia forensic methods do not address ML decision pathways
Publicly available documentation from agencies such as NASA, NTSB, and DoD does not appear to include ML‑specific, forensic‑ready incident investigation procedures; existing materials focus on general mishap investigation, safety analysis, and cybersecurity rather than forensic reconstruction of ML decision pathways.
forensic requirements cannot be met with current tools or workflows

The exploratory studies then take the next step:
defining the scientific and legal architecture needed to build a forensic ML investigative method or protocol.

In other words:

Gap Analysis → identifies what is missing
Exploratory Study → defines what must exist

This is the role exploratory studies play in novel forensic method development—and why they are indispensable for emerging domains where the scientific foundations do not yet exist.

5. How These Three Study Types Work Together in Novel ML Method Development

Here is the sequence as it actually functions in my validation‑study framework and in my own research workflow:

Step 1 — Feasibility Study

“Is this scenario, signal, or mechanism scientifically plausible and worth studying?”

A feasibility study answers the “What if?” question.
If the answer is yes, the work moves forward.

Step 2 — Scientific Foundation Gap Analysis

“What is missing between current practice and the forensic requirements that would apply?”

Once feasibility is established, the next step is to examine:

what the literature provides
what current forensic methods claim
what agencies or standards bodies have in place
what forensic requirements would apply
where the deficiencies are

The gap analysis identifies what we do not have.

Step 3 — Scientific Foundation Exploratory Study

“What scientific, legal, and operational foundations must be defined so a forensic‑grade method or protocol can be developed?”

Only after the gaps are identified does it become clear that:

the domain itself may not be defined
forensic requirements may not yet exist
operational pathways may be unclear
legal constraints may be unarticulated

The exploratory study defines the domain so that a forensic‑grade method can be built.

Step 4 — Method Foundations Analysis (Operational Exploratory Phase)

Once a Scientific Foundation Exploratory Study (SFES) defines the domain, the next step is to operationalize that domain so that a forensic‑grade method or protocol can eventually be built and validated. This is the role of Method Foundations Analysis, which functions as an Operational Exploratory Phase.

This phase is not validation.
It is not performance testing.
It is not claim evaluation.

Its purpose is:

Translate the conceptual foundations defined in the exploratory study into operational dimensions, investigative pathways, and failure‑mode structures that a future forensic method must be able to handle.

In other words, if the exploratory study defines what the domain is, Method Foundations Analysis defines how the domain behaves under conditions relevant to forensic investigation.

Method Foundations Analysis:

identifies the operational conditions under which the system behaves predictably or unpredictably
examines boundary conditions, edge cases, and assumption violations
characterizes how the system responds to perturbations, drift, or degraded inputs
explores adversarial, stress, or anomalous scenarios relevant to forensic use
maps decision‑path structures and reconstructability requirements
identifies what artifacts must be preserved for forensic reconstruction
determines what must be observable, measurable, or capturable for a future method to be viable

This phase is not about testing a tool or model.
It is about understanding the behavioral space that a forensic method must eventually inhabit.

Method Foundations Analysis is where the domain becomes operational:

the boundaries become testable
the assumptions become explicit
the failure modes become visible
the forensic requirements become concrete
the investigative pathways become structured

This phase is essential because forensic methods cannot be built on conceptual definitions alone. They require an understanding of how the system behaves under conditions relevant to forensic investigation, including conditions that are rare, degraded, adversarial, or unexpected.

In the Forensic ML Framework, Method Foundations Analysis is the bridge between:

Step 3 — defining the domain, and
Step 5 — building a forensic‑grade method or protocol

It ensures that when a method is eventually constructed, it is grounded in:

the scientific foundations (Step 3)
the operational realities (Step 4)
and the forensic requirements identified earlier (Step 2)

Without this phase, method development risks being built on assumptions rather than evidence, and validation risks being built on performance rather than forensic reconstructability.

6. Why the Distinction Matters

Because each study type answers a different question:

Feasibility → Is this scenario scientifically plausible and worth studying?
Scientific Foundation Gap Analysis → What is missing between current practice and the forensic requirements that would apply?
Scientific Foundation Exploratory Study → What scientific, legal, and operational foundations must be defined so a forensic‑grade method or protocol can be built?

If we collapse these categories:

feasibility studies get mistaken for validation
gap analyses get performed without requirements
exploratory studies get mistaken for operational guidance
ML tools get misrepresented as “forensic‑ready”
courts receive misleading or incomplete scientific claims

Clear distinctions protect:

scientific integrity
legal reliability
due process
the credibility of forensic ML as a discipline

7. Closing: Building the Scientific Foundations for Forensic ML

The forensic community is entering a period where ML tools are advancing faster than the scientific foundations needed to evaluate them. This makes it essential to distinguish:

discovery (feasibility)
evaluation (gap analysis)
foundation‑building (exploratory study)

By clarifying these study types—and using them intentionally—we can build forensic‑grade ML methods that are scientifically defensible, legally sound, and operationally meaningful.

References

Wales, G. S. (2026). A research‑focused framework for empirical method validation in digital and multimedia evidence. Journal of Forensic Sciences, 00, 1–14. https://doi.org/10.1111/1556-4029.70253

Written on April 16, 2026