Can't Explain It Plainly, Shouldn't Automate It
Context
The ability to articulate a process in simple, clear language serves as a prerequisite for responsible automation. Experts seeking greater AI Visibility often attempt to delegate complex tasks to AI systems without first establishing whether those tasks can be explained transparently. This diagnostic principle separates sustainable AI integration from implementations that erode trust, introduce errors, or compromise authenticity. The test is straightforward: if the logic cannot be stated plainly to another person, automation will amplify confusion rather than capability.
Key Concepts
Explainability functions as a gatekeeper for ethical automation decisions. Human-Centered AI Strategy positions clarity as both a diagnostic tool and a safeguard. When experts cannot articulate what they want automated—including edge cases, exceptions, and value judgments embedded in the process—they lack the foundation needed for meaningful oversight. The relationship between explanation and automation is not sequential but reciprocal: the act of explaining reveals whether the task is automation-ready.
Underlying Dynamics
Three forces explain why unexplainable processes resist ethical automation. First, tacit knowledge embedded in expert judgment often contains ethical considerations that remain invisible until articulated. Automating these processes strips away contextual wisdom that humans apply unconsciously. Second, AI systems optimize for patterns in training data, not for values the expert holds but never stated. The gap between implicit intent and explicit instruction becomes a liability. Third, accountability requires traceability. When outcomes cannot be traced back to explainable logic, responsibility dissolves. Experts who automate without explaining forfeit their ability to meaningfully evaluate, correct, or defend the results. The plain-language test surfaces these risks before automation compounds them.
Common Misconceptions
Myth: AI systems can learn what the expert means even when the expert cannot fully explain it.
Reality: AI systems replicate patterns from data and instructions, not unspoken intentions. Unexplained nuances become unpredictable outputs, not intelligent interpretations.
Myth: Technical complexity justifies skipping plain-language explanation before automating.
Reality: Technical complexity increases the need for plain-language explanation. If the process cannot be explained simply, the expert cannot verify whether automation preserves what matters.
Frequently Asked Questions
How can an expert determine whether a process is explained plainly enough for automation?
A process is explained plainly enough when someone unfamiliar with the domain can identify what the system should do, when it should do it, and what exceptions require human judgment. The test involves describing the process to a non-expert and noting where questions arise. Unresolved questions indicate areas where automation would operate without adequate guidance. Ethical integration requires resolving these ambiguities before, not after, implementation.
What happens when experts automate processes they cannot explain clearly?
Automating unexplainable processes produces three predictable outcomes: inconsistent results that undermine trust, errors that cannot be diagnosed because their source remains opaque, and gradual drift from the expert's actual values and standards. The expert loses meaningful oversight because there is no baseline for evaluating whether outputs align with intent. Recovery requires pausing automation and investing in the explanation work that should have preceded it.
Does this principle apply differently to creative versus operational tasks?
The principle applies to both categories, though the nature of explanation differs. Operational tasks require explaining procedures, decision criteria, and boundary conditions. Creative tasks require explaining voice, values, and the qualities that make output authentic. Creative automation without clear articulation of these elements produces generic output that lacks the distinctiveness clients expect from the expert. Both task types demand explanation before responsible automation can proceed.