Option B: Fact-Checked Outputs

Epistemic Responsibility

Back at her desk, Emma weighed her options, feeling the weight of a decision that could shape how users perceived and relied upon her AI. Option B, which focused on restricting the AI to verified, fact-checked responses, aligned with the principle of epistemic responsibility - a commitment to ensuring information is accurate, well-supported, and responsibly presented. By prioritising this approach, Emma knew she could protect users from misinformation and build their confidence in the AI’s reliability. After all, if her AI assistant avoided speculative answers and stuck to verified data, it would reduce the risk of accidental errors, misunderstandings, or even potentially harmful outcomes in high-stakes areas.

Pragmatism

Yet, as she considered this cautious, fact-focused approach, Emma found herself questioning its potential limitations, especially when she recalled the ideas of William James, a leading figure in the Pragmatist movement. For James, truth wasn’t a fixed, objective ideal but rather a practical tool. In James’s view, a statement or belief was “true” if it helped people effectively navigate the world, solve problems, or achieve their goals. Instead of focusing on accuracy alone, James valued truth for its practical utility and adaptability in context, arguing that what we deem “true” often depends on the situation and purpose.

James’s Pragmatism presented a compelling critique of Emma’s approach. If truth was meant to serve people’s immediate needs and contexts, did it make sense to restrict the AI solely to verified responses? Would such a cautious approach, with its focus on factual consistency, miss opportunities to support users in ways that were more responsive and versatile? These reflections led Emma to see three main challenges to the limitations of a strictly fact-focused approach:

1. Restricted Practical Utility: James’s Pragmatism suggested that the value of a response lies in its usefulness, meeting people’s goals. Emma could see how, by rigidly adhering to verified information, Option B might limit the AI’s broader appeal. Users looking for creative ideas or exploratory insights might find the AI lacking if it only provided narrow, fact-checked answers. A response could be “true” in the sense of being factual, but not necessarily useful if it didn’t meet the practical needs of the user. Emma worried that in cases like brainstorming or artistic inspiration, the AI’s rigid adherence to factuality might restrict the dynamic engagement users were seeking.
2. Loss of Flexibility: James emphasised that truth’s relevance often depends on context; what serves as “true” for one purpose might not hold in another. Emma realised that a fact-focused AI could become rigid in scenarios where users need speculative or flexible responses. For instance, users seeking a mix of creative and factual input might find the AI’s strict fact-checking to be overly cautious, perhaps even uninspired. The AI’s responses could become predictable, losing the flexibility that users may find beneficial in more imaginative or open-ended contexts. Emma saw that to ignore this could be to overlook how people engage with AI tools for more than just accurate information—they might want curiosity-sparking possibilities that are more adaptable than strictly factual.
3. Overlooking Contextual Needs: Pragmatism, as James described, encourages truth to serve real-world, contextual needs. Emma realized that users would come to the AI with a variety of intentions, some of which wouldn’t require absolute factuality. For instance, a writer exploring potential storylines or a researcher brainstorming novel concepts might find less value in hard facts and more in thought-provoking suggestions. If the AI stuck to a rigid truth standard, it could miss opportunities to support users whose needs require responses that are open-ended, provocative, or interpretative rather than purely accurate. Emma worried that the AI, with its narrow focus, might inadvertently alienate users who valued flexibility and imagination over strict accuracy.

Caution and Flexibility

This dilemma left Emma in a difficult position: should she prioritise epistemic responsibility by focusing on the caution and reliability of strictly fact-checked responses, or should she embrace some form of pragmatism by designing the AI to allow more flexibility, adapting to users’ needs by prioritising practical usefulness over rigid accuracy? She found herself reflecting on the balance between these two philosophical perspectives.

Pragmatism posed a thought-provoking counterpoint to epistemic responsibility, inviting Emma to consider whether her AI should aim not just to be factual but also to be practically beneficial in various contexts. If she prioritises flexibility, she can offer users an AI that met them where they were, providing ideas, exploring options, and assisting with tasks in a fluid, adaptable way. But she also knows that this adaptability carries the risk of generating speculative, potentially misleading responses.

As Emma sat back, weighing these competing priorities, the question lingered: should the AI serve users with a clear adherence to verified truth, or should it follow the principle of practical usefulness, embracing flexibility even if it sometimes sacrificed factual accuracy? This choice could shape how users engaged with the AI and, perhaps more importantly, how they came to think about the very nature of truth in their interactions with it.