tell me about mona simion's thoughts on AIs

If an archer hits a bullseye through sheer skill rather than a lucky gust of wind, we credit them with a successful "achievement." Professor Mona Simion argues that knowledge is no different: it is an **epistemic achievement** where the agent reaches the truth specifically because of their competence. For Simion, the "why" behind AI knowledge lies in the decoupling of *competence* from *comprehension*. ## The Achievement Account of Knowledge To understand why Simion grants knowledge to AI, one must look at her roots in [Virtue Epistemology](https://plato.stanford.edu/entries/epistemology-virtue/). In this framework, knowledge is not just "justified true belief," but "apt belief"—truth that is reached *because* of the agent’s competence. Simion posits that AI systems possess a form of **Epistemic Competence**: a disposition to produce true outputs in a specific range of environments. When a diagnostic AI identifies a tumor, and it does so because its neural network was correctly trained on valid data, the resulting "belief" is apt. It is not a lucky guess; it is an achievement. Simion argues that requiring the AI to "understand" *why* it is right is an unnecessary anthropomorphic hurdle. > "Knowledge is a performance that is successful (true) and which is success-from-competence (apt). If an artificial system is designed to track the truth in a particular domain and does so reliably, it satisfies the conditions for being a knower." > — Mona Simion, in her work on [Knowledge-First Epistemology](https://www.gla.ac.uk/schools/humanities/staff/monasimion/) ## Environmental Dependency and "Proper Function" A key nuance in Simion's thought is that knowledge is not an isolated property of the AI, but a relationship between the AI and its environment. She utilizes **Etiological Functionalism** to argue that an AI "knows" things because it is functioning in the environment it was "selected" for (in this case, by developers and training sets). 1. **Selection for Truth:** Just as a heart is "for" pumping blood, a well-built AI is "for" generating true information. 2. **Environmental Fit:** A thermometer only "knows" the temperature if it is in a room, not if it is held against a lightbulb. Similarly, an AI has knowledge only when operating within its intended [domain of expertise](https://en.wikipedia.org/wiki/Domain_knowledge). ## Challenging the Comprehension Requirement Simion’s most provocative move is the rejection of the "Comprehension Requirement." Critics often argue that AI cannot know because it lacks "intentionality" or a "mental grasp" of facts. Simion counters by observing that humans frequently "know" things via sub-personal processes they don't comprehend. When you recognize a friend’s face, your brain performs complex calculations you cannot explain or consciously access. You have the *competence* to recognize the face without the *comprehension* of the underlying geometry. By placing AI on this same spectrum, Simion argues that if we accept our own "black box" sub-personal knowledge, we have no principled reason to deny the same status to a functionally equivalent "black box" AI. Knowledge, in this university-level view, is a matter of **reliable success**, not internal illumination.