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A Workshop on the Ethical Design of AIs was convened in September and October 2022. Workshop participants hailed from a wide range of disciplines and application domains, and expressed interest in establishing partnerships across academia, industry, and government agencies, to address the challenges that were identified during the event. One of the outcomes of the workshop was a recommendation for a 2023 Convergence Accelerator Track on the Ethical Design of AIs (EDAIs). Suggested recommendations of themes and goals for the EDAIs Track include the following: (1) Human Centered Design methodologies around Values and Measures and Incentives. (2) Proto Ethical AIs: Algorithms or Systems or Pipelines across multiple domains. (3) Best Practices for the design of ethical AIs. (4) Workforce development and education and training. This report documents the activities of the EDAIs Workshop 

Legal texts routinely use concepts that are difficult to understand. Lawyers elaborate on the meaning of such concepts by, among other things, carefully investigating how they have been used in the past. Finding text snippets that mention a particular concept in a useful way is tedious, time-consuming, and hence expensive. We assembled a data set of 26,959 sentences, coming from legal case decisions, and labeled them in terms of their usefulness for explaining selected legal concepts. Using the dataset we study the effectiveness of transformer models pre-trained on large language corpora to detect which of the sentences are useful. In light of models{'} predictions, we analyze various linguistic properties of the explanatory sentences as well as their relationship to the legal concept that needs to be explained. We show that the transformer-based models are capable of learning surprisingly sophisticated features and outperform the prior approaches to the task. 

In this paper, we assess the use of several deep learning classification algorithms as a step toward automatically preparing succinct summaries of legal decisions. Short case summaries that tease out the decision’s argument structure by making explicit its issues, conclusions, and reasons (i.e., argument triples) could make it easier for the lay public and legal professionals to gain an insight into what the case is about. We have obtained a sizeable dataset of expert-crafted case summaries paired with full texts of the decisions issued by various Canadian courts. As the manual annotation of the full texts is prohibitively expensive, we explore various ways of leveraging the existing longer summaries which are much less time-consuming to annotate. We compare the performance of the systems trained on the annotations that are manually ported to the full texts from the summaries to the performance of the same systems trained on annotations that are projected from the summaries automatically. The results show the possibility of pursuing the automatic annotation in the future.