Mid-level ontologies are used to integrate terminologies and data across
disparate domains. There are, however, no clear, defensible criteria for
determining whether a given ontology should count as mid-level, because we lack
a rigorous characterization of what the middle level of generality is supposed
to contain. Attempts to provide such a characterization have failed, we
believe, because they have focused on the goal of specifying what is
characteristic of those single ontologies that have been advanced as mid-level
ontologies. Unfortunately, single ontologies of this sort are generally a
mixture of top- and mid-level, and sometimes even of domain-level terms. To
gain clarity, we aim to specify the necessary and sufficient conditions for a
collection of one or more ontologies to inhabit what we call a mid-level
architecture.

中级本体论、术语学、整合、广义性和架构

中间架构标准

Middle Architecture Criteria

The advent of advanced AI underscores the urgent need for comprehensive
safety evaluations, necessitating collaboration across communities (i.e., AI,
software engineering, and governance). However, divergent practices and
terminologies across these communities, combined with the complexity of AI
systems-of which models are only a part-and environmental affordances (e.g.,
access to tools), obstruct effective communication and comprehensive
evaluation. This paper proposes a framework for AI system evaluation comprising
three components: 1) harmonised terminology to facilitate communication across
communities involved in AI safety evaluation; 2) a taxonomy identifying
essential elements for AI system evaluation; 3) a mapping between AI lifecycle,
stakeholders, and requisite evaluations for accountable AI supply chain. This
framework catalyses a deeper discourse on AI system evaluation beyond
model-centric approaches.

该研究提出了一个 AI 系统评估的框架，包括协调术语、鉴定要素和建立映射，以促进跨学科的合作和综合评估。

推进人工智能安全的人工智能系统评估框架：术语、分类、生命周期映射

An AI System Evaluation Framework for Advancing AI Safety: Terminology,  Taxonomy, Lifecycle Mapping

The EU AI Act Article 5 is designed to regulate AI manipulation to prevent
potential harmful consequences. However, the practical implementation of this
legislation is challenging due to the ambiguous terminologies and the unclear
presentations of manipulative techniques. Moreover, the Article 5 also suffers
criticize of inadequate protective efficacy. This paper attempts to clarify
terminologies and to enhance the protective efficacy by integrating insights
from psychology and behavioral economics. Firstly, this paper employs cognitive
psychology research to elucidate the term subliminal techniques and its
associated representation. Additionally, this paper extends the study of
heuristics: a set of thinking shortcuts which can be aroused for behavior
changing from behavior economics to the realm of manipulative techniques. The
elucidation and expansion of terminologies not only provide a more accurate
understanding of the legal provision but also enhance its protective efficacy.
Secondly, this paper proposes five classical heuristics and their associated
examples to illustrate how can AI arouse those heuristics to alter users
behavior. The enumeration of heuristics serves as a practical guide for
stakeholders such as AI developers, algorithm auditors, users, and legal
practitioners, enabling them to identify manipulative techniques and implement
countermeasures. Finally, this paper critically evaluates the protective
efficacy of Article 5 for both the general public and vulnerable groups. This
paper argues that the current protective efficacy of Article 5 is insufficient
and thus proposes specific revision suggestions to terms a and b in Article 5
to enhance its protective efficacy. This work contributes to the ongoing
discourse on AI ethics and legal regulations, providing a practical guide for
interpreting and applying the EU AI Act Article 5.

本文旨在通过整合心理学和行为经济学的见解，澄清术语并提高保护效力，以解释和扩展用于人工智能操作的术语，并提出对 EU AI 法案第 5 条的具体修改建议，从而为解释和应用 EU AI 法案第 5 条提供实践指南。

规范人工智能操纵：应用行为经济学和心理学的洞察力以增强欧盟人工智能法的实用性

Regulating AI manipulation: Applying Insights from behavioral economics  and psychology to enhance the practicality of the EU AI Act

As neural machine translation (NMT) systems become an important part of
professional translator pipelines, a growing body of work focuses on combining
NMT with terminologies. In many scenarios and particularly in cases of domain
adaptation, one expects the MT output to adhere to the constraints provided by
a terminology. In this work, we propose metrics to measure the consistency of
MT output with regards to a domain terminology. We perform studies on the
COVID-19 domain over 5 languages, also performing terminology-targeted human
evaluation. We open-source the code for computing all proposed metrics:
this https URL

本研究提出度量机制以衡量神经机器翻译和术语一致性，对 5 种语言下 COVID-19 领域的术语进行人工评价，并公开提供度量方法的代码。