We can gain valuable insight into the fundamental challenges of integrating artificial intelligence within organizational contexts by examining the dynamics portrayed in the television series Star Trek: The Next Generation. The dramatic interactions between Captain Jean-Luc Picard; the truth-driven, factually precise, yet emotionally detached android Data; and the rest of the crew aboard the USS Enterprise (NCC-1701-D), vividly illustrate the complexities involved in balancing rational AI capabilities with human judgment, empathy, and intuition.

Let take a dialogue from the episode “Peak Performance” (Season 2, Episode 21), where the USS Enterprise engages in strategic war-games designed to test their readiness. Captain Picard and Data clash over how to approach a critical tactical decision.

The Enterprise is participating in war-games simulating a battle scenario. Data, commanding a separate ship (the USS Hathaway), faces a vastly superior Enterprise commanded by Picard. Data, after initially failing a simulated exercise against a master strategist, experiences self-doubt, believing that his computational decision-making may be flawed. Picard addresses Data’s reliance on pure rationality versus the human ability to adapt creatively under pressure.

Data: “Captain, I have analyzed every conceivable alternative. Our resources and firepower are insufficient to produce victory. Logic dictates withdrawal from this engagement.”

Picard (firmly): “Data, battles are not always won by computation alone. When facing an opponent of superior strength, victory often hinges on creativity, intuition, and the willingness to take risks. Logic may help us understand the battlefield—but imagination can help us redefine it.”

Data (puzzled): “I do not understand, sir. Imagination is not a quantifiable variable in tactical scenarios.”

Picard (leaning forward with conviction): “And yet it often decides their outcomes. Your analytical skills are unparalleled, Data, but your greatest strength lies in adapting those calculations to the unpredictability of real life. This scenario isn’t simply a problem to be solved; it’s an opportunity to innovate.”

Data (hesitating briefly): “Captain, are you suggesting that I disregard rational analysis?”

Picard: “Not disregard, Data—transcend. True problem-solving isn’t confined to known variables. You must incorporate intuition, possibility, and the very human capacity to gamble on uncertainty. Without these, your analyses remain incomplete.”

Data (reflective pause): “I see. You propose that embracing uncertainty itself can be a strategy.”

Picard (smiling warmly): “Precisely. The unexpected is our ally in this engagement. Trust your instincts—or, at least, your best imitation of them.”

There are structural asymmetries between human beings and AI systems. Humans possess a sense of self and reflective awareness—qualities that AI language models currently lack. AI decisions rest predominantly on factual probabilities derived from data, algorithms, and computation; human decisions, in contrast, are rooted in tacit knowledge, emotions, contingency, intuition, and multiple layers of rationality.

There are also situational epistemic asymmetries. Effective dialogue depends significantly upon the participants’ sensitivity to their knowledge states, including explicit knowledge, recognized ignorance, unknown unknowns, and tacit or unconscious knowledge (unknown knowns). A problematic scenario emerges when either party—human or AI—is insensitive to these epistemic states, potentially leading to misunderstandings, misinformation, and poor decision-making.

This document sketches a method for identifying and managing risks arising from situational epistemic asymmetries. It delineates all possible combinations of these epistemic states, suggest general associated risks, and sketches broad risk mitigation strategies.

Four epistemic modalities characterize interactions for both human and AI participants:
1. Explicit Knowledge (K): Clear awareness and understanding. ‘I know that I know’ and/or my knowledge is certified.
2. Known Ignorance (KI): Recognition of what remains unknown. ‘I know what I do not know’ then, it is possible for the ingorant to make productive decisions from their ignorance. They can delegate, learn, ask questions, or ask supervision.
3. Unknown Unknowns (UU): Lack of awareness of gaps or blind spots. The obliviousness of ignorance does not allow even be sensitive to what is lacking and its potential consequences. It is a high risk situation in which one of the agents is ‘hallucinating’ and can not see it by itself.
4. Unknown Knowns (UK): Tacit knowledge; implicitly effective yet consciously unacknowledged. Unacknowledged tacit knowledge, limit the capacity to deconstruct and teach sophisticated skills. Often trivialize what it takes to do something and create excessive demands to others.

These four modalities lead to sixteen possible interaction combinations between human and AI participants. Each of them demands a specific type of diplomacy and context negotiation. Here is the combination of sixteen modalities:

#	Human	AI Agent	Description
1	K	K	Both explicitly knowledgeable.
2	K	KI	Human explicitly knowledgeable; AI aware of ignorance.
3	K	UU	Human explicitly knowledgeable; AI unaware of blind spots (hallucinates).
4	K	UK	Human explicitly knowledgeable; AI implicitly effective but unclear.
5	KI	K	Human aware of ignorance; AI explicitly knowledgeable.
6	KI	KI	Mutual awareness of ignorance.
7	KI	UU	Human aware of ignorance; AI unaware of blind spots.
8	KI	UK	Human aware of ignorance; AI implicitly effective but unclear.
9	UU	K	Human unaware of blind spots; AI explicitly knowledgeable.
10	UU	KI	Human unaware of blind spots; AI aware of ignorance.
11	UU	UU	Mutual unawareness of blind spots.
12	UU	UK	Human unaware; AI implicitly knowledgeable but unclear.
13	UK	K	Human implicitly knowledgeable; AI explicitly knowledgeable.
14	UK	KI	Human implicitly knowledgeable; AI aware of ignorance.
15	UK	UU	Human implicitly knowledgeable; AI unaware of blind spots.
16	UK	UK	Mutual implicit knowledge without explicit clarity.

Interaction combinations can be categorized by risk levels. This is a very general risk characterization that will be insuficient to capture demands coming from highly regulated environments or environments in which an error may have major consequences.
Low Risk (1, 2, 5, 13): Effective communication with minimal issues.
Moderate Risk (4, 6, 8, 9, 14, 16): Moderate inefficiencies, unclear assumptions, manageable confusion.
High Risk (3, 7, 10, 12, 15): Significant risks involving misinformation, false confidence, ineffective dialogue.
Very High Risk (11): Severe communication breakdown and high misinformation likelihood due to mutual blindness.

To effectively address these risks, five general levels of mitigation are recommended:

Low-Risk Scenarios:
Encourage explicit confirmations and documentation of knowledge.
Minimal yet consistent oversight to sustain productive conversations.

Moderate-Risk Scenarios:
Implement regular meta-level reflections to surface assumptions and implicit knowledge.
Establish standards for historical traceability of fact production; auditing theoretical models’ assumtions, axioms and beliefs.
Establish structured after-action reviews (AARs) to clarify implicit understandings.
Utilize external moderation or additional knowledgeable human/AI agents.

High-Risk Scenarios:
Introduce rigorous fact-checking and verification frameworks.
Practice structured skepticism to challenge assumptions actively.
Clearly differentiate between hypotheses and confirmed facts to mitigate misinformation.
Audit the end-to-end data production process.

Very High-Risk Scenario:
Immediately involve external knowledgeable participants or interventions.
Activate explicit warning systems to halt interactions promptly.
Provide explicit training in epistemic humility and AI introspection tools to identify and manage unknown unknowns effectively.

General Principles for All Scenarios:
Use explicit epistemic markers (high, moderate, low confidence).
Conduct regular epistemic checkpoints to reflect on uncertainties and implicit assumptions.
Educate participants to practice epistemic humility openly and consistently.

These structured approaches significantly reduce misinformation risk, enhance productive collaboration, and leverage the full epistemic potential of human-AI conversational interactions.