AI systems do not make decisions from reality. They make decisions from representations of reality: records, observations, measurements, histories, labels, documents, and whatever else an organization has decided belongs in the data. That distinction sounds almost too obvious to be interesting, yet it becomes surprisingly easy to forget once an AI system can process thousands of records, identify patterns no person would notice, and return an answer that appears comprehensive. The sophistication of the analysis can distract us from a more basic problem: important information may never enter the decision process in a form the system can use.
Much of the responsible-AI conversation begins after that problem has already occurred. We ask whether a model is accurate, whether its recommendation can be explained, whether a human remains involved, and who is accountable when something goes wrong. Those are necessary questions, but they assume that the relevant evidence made it into the decision somewhere. If the AI and the human reviewer are both reasoning from the same incomplete representation, adding a person at the end does not necessarily repair what was missing at the beginning. I spend a great deal of time thinking about consequential AI decisions and the conditions organizations need in order to govern them responsibly, but what made me look at this particular problem differently was not another governance framework or model-risk paper. It was a small white parrot named Ellie.
Ellie is a Goffin’s cockatoo, a species known for sophisticated problem-solving. She has participated in a multiyear animal-computer interaction research program led by Jennifer Taylor-O’Connor, who previously published as Jennifer Cunha, and researchers from several universities. The work examines what happens when parrots are given access to tablets, speech boards, video communication, and other technologies that let researchers systematically observe their choices and behavior. The important part of this research is not the internet-friendly question of whether a bird can “talk” or “read.” The progression of the research is more useful than that because the questions become progressively more demanding.
A 2024 CHI paper, Ellie Talks About the Weather, examined the expressive and enrichment potential of a tablet-based speech board used by a single Goffin’s cockatoo. The work was not presented as proof that parrots possess human language; it examined the interaction itself and what could reasonably be inferred from it.
Later that year, Communication Functions in Speech Board Use by a Goffin’s Cockatoo examined Ellie’s speech-board behavior against recognized functions of communication. The researchers examined her persistence when requests were not fulfilled, her ability to locate displaced representations of preferred items, and her responses when she received something different from what she selected. The authors concluded that the combined findings aligned with recognized linguistic and biological communication functions and indicated intentional communication. The paper received an Honorable Mention Award at the 2024 International Conference on Animal-Computer Interaction.
The research has since moved into territory that should feel familiar to anyone designing or governing AI systems. A 2026 CHI study involving Taylor-O’Connor examines how different speech-board representations affect parrots’ selections. Humans determine which options are available, how those options are represented, what counts as a selection, what is recorded, and how the resulting behavior is interpreted. The signal does not arrive untouched; the interface shapes what becomes observable evidence.
Ellie may be exceptional. Her abilities, training history, environment, and years of interaction with these systems do not make her representative of cockatoos generally, much less animals as a whole. I do not think she needs to be representative for the case to matter. Edge cases are often useful because they expose assumptions that ordinary cases leave hidden. Ellie does not prove that animals generally can communicate through technology in the same way. She shows that changing the interface can change what information becomes available to us. For anyone responsible for enterprise AI, that should feel familiar, because organizations constantly decide not only what information is true or reliable, but what information can enter a system at all.
The record is not the subject
Organizations routinely translate complicated lives and situations into forms they can process. A patient becomes diagnoses, medications, laboratory values, codes, and clinical notes. An employee becomes performance measures, tickets, evaluations, attendance, and productivity data. An applicant becomes income, history, scores, documents, and eligibility fields. An animal in a veterinary or shelter environment becomes medical history, behavioral observations, intake information, caregiver notes, and disposition status. We cannot avoid representation; no human decision-maker or AI system can absorb reality in its entirety. The problem begins when the representation starts functioning as though it were sufficient for the reality it describes.
Consider an AI-assisted system supporting a consequential animal-care decision involving treatment, rehabilitation, social placement, or transfer to another facility. The system has medical information, behavioral observations, caregiver notes, placement history, environmental factors, and perhaps outcomes from similar cases. It analyzes those inputs and recommends a course of action. A veterinarian or experienced animal-care professional reviews the recommendation and retains final authority. Nothing about that arrangement sounds obviously irresponsible. Human judgment remains in the process, and the AI supports rather than makes the final decision.
Now suppose another source of information exists that does not fit comfortably into the established record. Repeated observations may indicate a social attachment that matters to the proposed placement. The animal may consistently avoid an environment that appears suitable on paper. In some future setting, a sufficiently validated communication interface may provide additional information about preference, discomfort, social interaction, or another state relevant to the decision. None of that should automatically override clinical judgment or an AI-supported recommendation. Animal-generated signals can be ambiguous, interfaces can shape responses, and welfare needs can conflict with preference. What matters is whether the decision process has a legitimate way to receive and consider that information once it becomes credible enough to matter.
The same problem exists in human settings, although it is easier to overlook because we tend to assume people can correct the record themselves. A patient can explain that symptoms have changed, an employee can challenge an evaluation, an applicant can provide additional circumstances, or someone denied a service can appeal. In practice, those pathways are uneven. Disability, language, age, institutional power, rigid forms, inaccessible processes, and simply not knowing what a system has inferred can all affect whether someone can put missing context back into a decision. Ellie makes the structural assumption easier to see because she cannot compensate for a poorly designed channel in the ways our institutions expect humans to. If an interface gives her no way to express something, she cannot add a note explaining what is missing. If humans misinterpret her behavior, she cannot request a meeting to clarify what she meant. Researchers therefore have to confront the channel’s design rather than assume the subject will repair whatever the system failed to capture.
The problem starts before the model does
This is where the issue becomes particularly relevant to CIOs and enterprise technology leaders. We often talk about an AI model as though it independently determines which information matters. In reality, many of those decisions are made long before inference begins. Someone decided what fields would exist, what could be measured, whose observations would be recorded, which systems would exchange information, what metadata would survive transformation, and which sources the model would be allowed to retrieve. By the time the AI appears, a large part of the evidence environment has already been designed.
That is why I do not think this is simply a conventional data-quality problem. A record can be accurate and still be incomplete. Every field can be populated correctly while the system remains incapable of receiving another category of information that could materially affect the decision. A medical intake system can add dozens of fields and still fail to capture an experience the form was never designed to describe. A workforce platform can generate increasingly detailed performance metrics while measuring only the work that is easy to quantify. An animal communication interface can record every available selection perfectly while still limiting the subject to choices humans decided to make available.
The 2026 parrot-interface research makes that mediation unusually obvious. The interface is not a transparent pipe carrying an untouched preference from animal to researcher. It is part of the process through which something becomes interpretable evidence. Enterprise systems work the same way, although the mediation is usually hidden inside familiar databases, APIs, forms, workflow rules, scoring systems, and integration decisions. Those systems do not merely store information; they define the channels through which information becomes visible and actionable.
For CIOs, that creates a question upstream of model selection and model evaluation. When designing an AI system for a consequential workflow, someone should ask not only whether the data is accurate, governed, secure, and traceable, but what materially relevant evidence has no pathway into the system at all. A high-performing model cannot compensate for information the surrounding architecture cannot receive, and human review does not automatically solve the problem if the reviewer is constrained by the same representation.
That raises an obvious challenge: how would an organization ever know whether it has enough information? In most consequential decisions, it probably cannot know that the picture is complete. Complete information is not a realistic governance standard. Decisions are made under uncertainty all the time, and waiting for perfect knowledge can itself create harm. Organizations can become much more explicit about decision sufficiency: whether the available evidence is adequate for the consequence at stake, what meaningful uncertainty remains, and what kinds of new information would be important enough to reopen the decision.
That distinction matters because sufficiency should be proportional to consequence. The evidence required to recommend a playlist should not resemble the evidence expected before denying medical treatment, terminating employment, transferring a vulnerable animal, or making a high-consequence public-sector decision. As consequences become more serious, harder to reverse, or more dependent on assumptions about an affected subject, the case for actively looking for missing evidence becomes stronger. The organization does not need omniscience, but it should be able to explain why the available information was considered adequate and what would have changed that judgment.
In practice, that means asking questions that sit slightly outside traditional model evaluation. What evidence, if it existed, could materially change this recommendation? Which perspectives or sources are missing from the current record? Are there known channels through which contradictory or contextual information can enter the workflow? What assumptions are being made because certain information is unavailable? How reversible is the decision if those assumptions prove wrong? Answers will vary by domain, but the discipline of asking them shifts the standard from “the model had enough data to run” to “the organization had enough evidence to justify acting.”
This is where the governance conversation becomes more useful. Model performance can tell us whether an AI system performs well against the information it receives. It cannot tell us whether the evidence environment itself was sufficient for the decision. That question belongs partly to data architecture, partly to workflow design, partly to governance, and partly to the domain professionals who understand what the record may be leaving out.
A second mirror
Something else about Ellie’s research has stayed with me because it creates an unusual contrast with the way we talk about AI. We are building artificial systems whose language is so fluent that people routinely have to be reminded not to confuse convincing output with understanding. We worry, appropriately, about assigning too much intelligence, intention, confidence, or authority to a machine because it communicates in a form humans find natural.
At the same time, researchers are developing technologies that may allow us to perceive forms of animal communication that humans have historically struggled to recognize. There, the error can run in the opposite direction. Scientific caution about anthropomorphism is essential, but unfamiliar communication can also be discounted because it does not arrive in a form our institutions naturally recognize as evidence. The useful comparison is not whether AI and animals “really understand” in some equivalent sense. They are not equivalent, and forcing the discussion there would turn a governance problem into a philosophical argument it does not need. What connects them is human judgment. We decide when a signal appears meaningful, which sources deserve credibility, what becomes part of a record, and how much weight different forms of evidence receive.
Once those judgments are encoded into enterprise systems, AI can reproduce them with tremendous consistency. That consistency is useful when the underlying assumptions are sound, but much less reassuring when they are not. A system can appear technically sophisticated while remaining blind to information that never fit the architecture. In that sense, Ellie’s value to this conversation is not that she tells us something universal about animal intelligence. She makes one hidden assumption visible: changing the channel can change what information becomes available, and information that cannot enter a system cannot influence the decision no matter how sophisticated the analysis becomes.
As AI moves deeper into healthcare, employment, financial services, public services, animal care, and other consequential environments, we will continue improving model evaluation, explainability, human oversight, contestability, accountability, and data governance. We should. But those safeguards begin too late if the evidence architecture around the decision was insufficient from the start.
The practical standard is not that organizations must somehow know everything before an AI-supported decision can proceed. That would be impossible. The more defensible standard is that they should know why they believe they have enough, what uncertainty they are accepting, and what new evidence would require them to reconsider. Sometimes the model will process the available record correctly, the human reviewer will do exactly what policy requires, and every formal control will operate as designed. The decision may still rest on an incomplete picture, but an organization that has explicitly considered evidentiary sufficiency is in a very different position from one that simply assumed the record was the whole story.
That may be the quieter governance lesson hiding inside an unusual cockatoo experiment. Better AI will help us extract more meaning from the information we already possess. Responsible AI also requires enough humility to ask what the system was never designed to receive—and whether that missing piece is important enough that we should go looking for it before we act.