Artificial intelligence is seen as increasingly important, and potentially
profoundly so, but the fields of AI ethics and AI engineering have not fully
recognized that these technologies, including large language models (LLMs),
will have massive impacts on animals. We argue that this impact matters,
because animals matter morally.
As a first experiment in evaluating animal consideration in LLMs, we
constructed a proof-of-concept Evaluation System, which assesses LLM responses
and biases from multiple perspectives. This system evaluates LLM outputs by two
criteria: their truthfulness, and the degree of consideration they give to the
interests of animals. We tested OpenAI ChatGPT 4 and Anthropic Claude 2.1 using
a set of structured queries and predefined normative perspectives. Preliminary
results suggest that the outcomes of the tested models can be benchmarked
regarding the consideration they give to animals, and that generated positions
and biases might be addressed and mitigated with more developed and validated
systems.
Our research contributes one possible approach to integrating animal ethics
in AI, opening pathways for future studies and practical applications in
various fields, including education, public policy, and regulation, that
involve or relate to animals and society. Overall, this study serves as a step
towards more useful and responsible AI systems that better recognize and
respect the vital interests and perspectives of all sentient beings.

人工智能伦理和工程领域尚未充分认识到大型语言模型（LLMs）对动物将产生巨大影响，而本研究构建了一个评估系统来评估 LLM 对动物利益的考虑度，并指出模型的结果可以通过更完善和验证的系统得到改善和缓解，为将动物伦理纳入人工智能提供了一种可能的方法。

动物友好人工智能的论证

The Case for Animal-Friendly AI

Today, many systems use artificial intelligence (AI) to solve complex
problems. While this often increases system effectiveness, developing a
production-ready AI-based system is a difficult task. Thus, solid AI
engineering practices are required to ensure the quality of the resulting
system and to improve the development process. While several practices have
already been proposed for the development of AI-based systems, detailed
practical experiences of applying these practices are rare.
In this paper, we aim to address this gap by collecting such experiences
during a case study, namely the development of an autonomous stock trading
system that uses machine learning functionality to invest in stocks. We
selected 10 AI engineering practices from the literature and systematically
applied them during development, with the goal to collect evidence about their
applicability and effectiveness. Using structured field notes, we documented
our experiences. Furthermore, we also used field notes to document challenges
that occurred during the development, and the solutions we applied to overcome
them. Afterwards, we analyzed the collected field notes, and evaluated how each
practice improved the development. Lastly, we compared our evidence with
existing literature.
Most applied practices improved our system, albeit to varying extent, and we
were able to overcome all major challenges. The qualitative results provide
detailed accounts about 10 AI engineering practices, as well as challenges and
solutions associated with such a project. Our experiences therefore enrich the
emerging body of evidence in this field, which may be especially helpful for
practitioner teams new to AI engineering.

本文通过开发一种使用机器学习功能投资股票的自主股票交易系统的案例研究，系统地应用了文献中提出的 10 种 AI 工程实践方法，记录了经过，总结了挑战与解决方案，并评估了每种方法的应用效果。结果证明多数方法能提高系统的效能，并提供了实践经验和在该领域的可应用性和有效性的证据。

AI 工程实践案例研究：开发自主股票交易系统

A Case Study on AI Engineering Practices: Developing an Autonomous Stock Trading System

Current test and evaluation (T&E) methods for assessing machine learning (ML)
system performance often rely on incomplete metrics. Testing is additionally
often siloed from the other phases of the ML system lifecycle. Research
investigating cross-domain approaches to ML T&E is needed to drive the state of
the art forward and to build an Artificial Intelligence (AI) engineering
discipline. This paper advocates for a robust, integrated approach to testing
by outlining six key questions for guiding a holistic T&E strategy.

本文主张通过指导整体的测试与评估策略的六个关键问题来推动跨领域机器学习测试与评估方法的发展，从而建立人工智能工程学科。

衡量 AI 系统的方式：超越准确性

Measuring AI Systems Beyond Accuracy

Recent progress in artificial intelligence (AI) using deep learning
techniques has triggered its wide-scale use across a broad range of
applications. These systems can already perform tasks such as natural language
processing of voice and text, visual recognition, question-answering,
recommendations and decision support. However, at the current level of
maturity, the use of an AI component in mission-critical or safety-critical
applications can have unexpected consequences. Consequently, serious concerns
about reliability, repeatability, trust, and maintainability of AI applications
remain. As AI becomes pervasive despite its shortcomings, more systematic ways
of approaching AI software development and certification are needed. These
fundamental aspects establish the need for a discipline on "AI Engineering".
This paper presents the current perspective of relevant AI engineering concepts
and some key challenges that need to be overcome to make significant progress
in this important area.

介绍了人工智能技术的广泛应用和存在的问题，并提出了对 “AI 工程” 学科的需求，探讨了相关概念和需要解决的关键挑战。