Ethics by Design
Truth Filters for LLMs?
Emma's curiosity deepened as she wondered how these philosophical perspectives on truth could be practically applied to help both users and developers interact more responsibly with LLMs. If truth could be seen through different lenses, then each lens might offer a way to improve the way people work with LLMs. She began to think of these theories as 'truth filters' - different ways of evaluating, interpreting and even designing model responses with specific goals in mind. These filters, she thought, could form the basis of an ethics by design framework, a system that could make interactions with LLMs more transparent, grounded, and ultimately safer for all.
She sketched out ideas for how users and developers could apply each truth filter to guide their approach:
- Correspondence Filter
- Coherence Filter
- Pragmatic Filter
- Epistemic Filter
2. Coherence Filter
- For users: Emma knew that sometimes the truth lies in consistency rather than in raw facts. She imagined a coherence filter that would help users assess whether an answer fits logically with a previous conversation or with their own knowledge. If a model began to contradict itself in a conversation, users would be prompted to take a closer look, encouraging critical engagement rather than passive acceptance.
- For developers: Developers could implement this filter by designing models with an internal consistency check, allowing the LLM to 'remember' its previous responses during an ongoing session. By training models to avoid inconsistencies, especially in longer dialogues, they could improve consistency. This would make LLM output more reliable and engaging, and reduce the model's tendency to contradict itself over time.