Hadeel Naeem

I argue that we should ameliorate the concept of pain. The currently predominant concept, focused primarily or solely on treatment, is too narrow to accommodate the complexity of pain. This concept, when applied in clinical practice and medical research, causes epistemic harms that fall disproportionately on already marginalised groups. I argue that the primary purpose for which we need the concept is recognition, not treatment. To recognise someone in pain is to treat them as the primary epistemic authority regarding their pain experience and to emphatically see them in their pain experience and its impact. Ameliorating the concept of pain in this way allows us to make space for complex presentations of pain and pains that we do not want or expect to be treated.

Education aims to improve our innate abilities, teach new skills and habits, and nurture intellectual virtues. Poorly designed or misused generative AI disrupts these educational goals. I propose strategies to design generative AI that aligns with education's aims. The paper proposes a design for a generative AI tutor that teaches students to question well. I argue that such an AI can also help students learn to lead noble inquiries, achieve deeper understanding, and experience a sense of curiosity and fascination. Students who learn to question effectively through such an AI tutor may also develop crucial intellectual virtues.

Phenomenal transparency was once widely believed to be necessary for cognitive extension. Recently, this claim has come under attack, with a new consensus coalescing around the idea that transparency is neither necessary for internal nor extended cognitive processes. We take these recent critiques as an opportunity to refine the concept of transparency relevant for cognitive extension. In particular, we highlight that transparency concerns an agent’s employment of a resource – and that such employment is compatible with an agent consciously apprehending (or attending to) a resource. This means it is possible for an object to be transparent and opaque to an agent, even at a single moment in time. Once we understand transparency in this way, the detractors’ claims lose their bite, and existing arguments for transparency’s necessity for cognitive extension return to apply with full force.

We might worry that our seamless reliance on AI systems makes us prone to adopting the strange errors that these systems commit. One proposed solution is to design AI systems so that they are not phenomenally transparent to their users. This stops cognitive extension and the automatic uptake of errors. Although we acknowledge that some aspects of AI extension are concerning, we can address these concerns without discouraging transparent employment altogether. First, we believe that the potential danger should be put into perspective – many unreliable technologies are unlikely to be used transparently precisely because they are unreliable. Second, an agent who transparently employs a resource may also reflect (opaquely) on its reliability. Finally, agents can rely on a process transparently and be yanked out of their transparent use when it turns unreliable. When an agent is responsive to the reliability of their process in this way, they have epistemically integrated it, and the beliefs they form with it are formed responsibly. This prevents the agent from automatically incorporating problematic beliefs. Responsible (and transparent) use of AI resources – and consequently responsible AI extension – is hence possible. We end the paper with several design and policy recommendations that encourage epistemic integration of AI-involving belief-forming processes.