EALM: Introducing Multidimensional Ethical Alignment in Conversational
  Information Retrieval
Yiyao Yu Junjie Wang Yuxiang Zhang Lin Zhang Yujiu Yang Tetsuya Sakai
Artificial intelligence (AI) technologies should adhere to human norms to
better serve our society and avoid disseminating harmful or misleading
information, particularly in Conversational Information Retrieval (CIR).
Previous work, including approaches and datasets, has not always been
successful or sufficiently robust in taking human norms into consideration. To
this end, we introduce a workflow that integrates ethical alignment, with an
initial ethical judgment stage for efficient data screening. To address the
need for ethical judgment in CIR, we present the QA-ETHICS dataset, adapted
from the ETHICS benchmark, which serves as an evaluation tool by unifying
scenarios and label meanings. However, each scenario only considers one ethical
concept. Therefore, we introduce the MP-ETHICS dataset to evaluate a scenario
under multiple ethical concepts, such as justice and Deontology. In addition,
we suggest a new approach that achieves top performance in both binary and
multi-label ethical judgment tasks. Our research provides a practical method
for introducing ethical alignment into the CIR workflow. The data and code are
available at https://github.com/wanng-ide/ealm .