Our research endeavors to advance the concept of responsible artificial
intelligence (AI), a topic of increasing importance within EU policy
discussions. The EU has recently issued several publications emphasizing the
necessity of trust in AI, underscoring the dual nature of AI as both a
beneficial tool and a potential weapon. This dichotomy highlights the urgent
need for international regulation. Concurrently, there is a need for frameworks
that guide companies in AI development, ensuring compliance with such
regulations. Our research aims to assist lawmakers and machine learning
practitioners in navigating the evolving landscape of AI regulation,
identifying focal areas for future attention. This paper introduces a
comprehensive and, to our knowledge, the first unified definition of
responsible AI. Through a structured literature review, we elucidate the
current understanding of responsible AI. Drawing from this analysis, we propose
an approach for developing a future framework centered around this concept. Our
findings advocate for a human-centric approach to Responsible AI. This approach
encompasses the implementation of AI methods with a strong emphasis on ethics,
model explainability, and the pillars of privacy, security, and trust.

我们的研究旨在推进负责任的人工智能（AI）的概念，这是欧盟政策讨论中日益重要的主题。我们提出了一种全面且目前为止我们所知的第一个负责任 AI 的统一定义。通过结构化文献综述，我们阐明了关于负责任 AI 的当前理解，并基于该分析提出了未来以此概念为中心的框架发展方法。我们的发现支持以人为中心的负责任 AI 方法，其中包括以伦理、模型可解释性以及隐私、安全和信任为支柱的 AI 方法的实施。

负责任的人工智能：一项结构化文献综述

Responsible Artificial Intelligence: A Structured Literature Review

All types of research, development, and policy work can have unintended,
adverse consequences - work in responsible artificial intelligence (RAI),
ethical AI, or ethics in AI is no exception.

研究，发展，政策工作，负责任的人工智能，道德人工智能

负责任的人工智能研究也需要影响声明

Responsible AI Research Needs Impact Statements Too

Responsible Artificial Intelligence (AI) - the practice of developing,
evaluating, and maintaining accurate AI systems that also exhibit essential
properties such as robustness and explainability - represents a multifaceted
challenge that often stretches standard machine learning tooling, frameworks,
and testing methods beyond their limits. In this paper, we present two new
software libraries - hydra-zen and the rAI-toolbox - that address critical
needs for responsible AI engineering. hydra-zen dramatically simplifies the
process of making complex AI applications configurable, and their behaviors
reproducible. The rAI-toolbox is designed to enable methods for evaluating and
enhancing the robustness of AI-models in a way that is scalable and that
composes naturally with other popular ML frameworks. We describe the design
principles and methodologies that make these tools effective, including the use
of property-based testing to bolster the reliability of the tools themselves.
Finally, we demonstrate the composability and flexibility of the tools by
showing how various use cases from adversarial robustness and explainable AI
can be concisely implemented with familiar APIs.

本文提出了两个新的软件库，即 hydra-zen 和 rAI-toolbox，这两个库旨在解决对负责任的人工智能工程的关键需求，包括使复杂的 AI 应用程序可配置，并使它们的行为可复现，以及以可扩展的方式评估和增强 AI 模型的鲁棒性。