The Value of Gen-AI Conversations: A bottom-up Framework for AI Value Alignment
This addresses ethical challenges for conversational agent providers by offering a context-sensitive method, though it is incremental as it builds on existing standards.
The paper tackled the problem of ensuring ethical interactions in conversational agents by proposing a bottom-up approach using the ISO Value-Based Engineering standard, analyzing 593 sensitive outputs from 16,908 logs to identify nine core values and 32 misalignments that negatively impacted users.
Conversational agents (CAs) based on generative artificial intelligence frequently face challenges ensuring ethical interactions that align with human values. Current value alignment efforts largely rely on top-down approaches, such as technical guidelines or legal value principles. However, these methods tend to be disconnected from the specific contexts in which CAs operate, potentially leading to misalignment with users interests. To address this challenge, we propose a novel, bottom-up approach to value alignment, utilizing the value ontology of the ISO Value-Based Engineering standard for ethical IT design. We analyse 593 ethically sensitive system outputs identified from 16,908 conversational logs of a major European employment service CA to identify core values and instances of value misalignment within real-world interactions. The results revealed nine core values and 32 different value misalignments that negatively impacted users. Our findings provide actionable insights for CA providers seeking to address ethical challenges and achieve more context-sensitive value alignment.