The transition of Artificial Intelligence (AI) from a lab-based science to
live human contexts brings into sharp focus many historic, socio-cultural
biases, inequalities, and moral dilemmas. Many questions that have been raised
regarding the broader ethics of AI are also relevant for AI in Education
(AIED). AIED raises further specific challenges related to the impact of its
technologies on users, how such technologies might be used to reinforce or
alter the way that we learn and teach, and what we, as a society and
individuals, value as outcomes of education. This chapter discusses key ethical
dimensions of AI and contextualises them within AIED design and engineering
practices to draw connections between the AIED systems we build, the questions
about human learning and development we ask, the ethics of the pedagogies we
use, and the considerations of values that we promote in and through AIED
within a wider socio-technical system.

人工智能从实验室科学转变为实际人类环境，引发了许多历史、社会文化的偏见、不平等和道德困境。人工智能教育（AIED）提出了更多特定的挑战，涉及技术对用户的影响，这些技术如何用于加强或改变我们学习和教学的方式，以及作为社会和个体，我们对教育结果的价值观。该文章讨论了人工智能的关键伦理维度，并将其置于 AIED 设计和工程实践中，以建立我们所构建的 AIED 系统、我们提出的有关人类学习和发展的问题、我们使用的教学伦理以及我们通过 AIED 在更广泛的社会技术系统中提倡的价值观之间的联系。

教育领域中人工智能的伦理

The Ethics of AI in Education

The Competition on Legal Information Extraction/Entailment (COLIEE) is held
annually to encourage advancements in the automatic processing of legal texts.
Processing legal documents is challenging due to the intricate structure and
meaning of legal language. In this paper, we outline our strategies for
tackling Task 2, Task 3, and Task 4 in the COLIEE 2023 competition. Our
approach involved utilizing appropriate state-of-the-art deep learning methods,
designing methods based on domain characteristics observation, and applying
meticulous engineering practices and methodologies to the competition. As a
result, our performance in these tasks has been outstanding, with first places
in Task 2 and Task 3, and promising results in Task 4. Our source code is
available at this https URL

该论文介绍了在 COLIEE 2023 竞赛中处理 Task 2、Task 3 和 Task 4 的策略，使用了先进的深度学习方法、基于领域特征观察的设计方法，以及严谨的工程实践和方法论，在这些任务中取得了优秀的成绩，并在 Task 2 和 Task 3 中获得了第一名，Task 4 中也有良好结果。源代码可在指定的 URL 获得。

COLIEE 2023 的 CAPTAIN：法律信息检索和蕴含任务的高效方法

CAPTAIN at COLIEE 2023: Efficient Methods for Legal Information  Retrieval and Entailment Tasks

Many engineering organizations are reimplementing and extending deep neural
networks from the research community. We describe this process as deep learning
model reengineering. Deep learning model reengineering - reusing, reproducing,
adapting, and enhancing state-of-the-art deep learning approaches - is
challenging for reasons including under-documented reference models, changing
requirements, and the cost of implementation and testing. In addition,
individual engineers may lack expertise in software engineering, yet teams must
apply knowledge of software engineering and deep learning to succeed. Prior
work has examined on DL systems from a "product" view, examining defects from
projects regardless of the engineers' purpose. Our study is focused on
reengineering activities from a "process" view, and focuses on engineers
specifically engaged in the reengineering process.
Our goal is to understand the characteristics and challenges of deep learning
model reengineering. We conducted a case study of this phenomenon, focusing on
the context of computer vision. Our results draw from two data sources: defects
reported in open-source reeengineering projects, and interviews conducted with
open-source project contributors and the leaders of a reengineering team. Our
results describe how deep learning-based computer vision techniques are
reengineered, analyze the distribution of defects in this process, and discuss
challenges and practices. Integrating our quantitative and qualitative data, we
proposed a novel reengineering workflow. Our findings inform several future
directions, including: measuring additional unknown aspects of model
reengineering; standardizing engineering practices to facilitate reengineering;
and developing tools to support model reengineering and model reuse.

本文从 “过程” 视角描述了深度学习模型重新工程活动中的特征和挑战，分析了深度学习模型在计算机视觉领域的重构和缺陷分布，并提出了一种新的重新工程流程。这些结果可以指导今后更多的度量、标准化工程实践以促进重新工程，以及开发工具以支持重用深度学习模型的发展方向。