Responsible AI: Best Practices for Creating Trustworthy AI Systems

AI systems are solving real-world challenges and transforming industries, but there are serious concerns about how responsibly they operate on behalf of the humans that rely on them. Many ethical principles and guidelines have been proposed for AI systems, but they're often too 'high-level' to be translated into practice. Conversely, AI/ML researchers often focus on algorithmic solutions that are too 'low-level' to adequately address ethics and responsibility. In this timely, practical guide, pioneering AI practitioners bridge these gaps. The authors illuminate issues of AI responsibility across the entire system lifecycle and all system components, offer concrete and actionable guidance for addressing them, and demonstrate these approaches in three detailed case studies.

Writing for technologists, decision-makers, students, users, and other stake-holders, the topics cover:

• Governance mechanisms at industry, organisation, and team levels
• Development process perspectives, including software engineering best practices for AI
• System perspectives, including quality attributes, architecture styles, and patterns
• Techniques for connecting code with data and models, including key tradeoffs
• Principle-specific techniques for fairness, privacy, and explainability
• A preview of the future of responsible AI

AI系統正在解決現實世界的挑戰，並改變著各個行業，但對於它們如何負責地為依賴它們的人們運作，存在著嚴重的擔憂。已經提出了許多關於AI系統的道德原則和指南，但它們往往過於「高層次」，無法轉化為實踐。相反，AI/ML研究人員通常專注於過於「低層次」的算法解決方案，無法充分解決倫理和責任問題。在這本及時而實用的指南中，開創性的AI從業者彌補了這些差距。作者們闡明了AI責任在整個系統生命周期和所有系統組件中的問題，提供了具體可行的指導方針來解決這些問題，並通過三個詳細的案例研究展示了這些方法。

本書針對技術人員、決策者、學生、用戶和其他利益相關者，涵蓋的主題包括：

- 行業、組織和團隊層面的治理機制
- 開發過程的觀點，包括AI的軟體工程最佳實踐
- 系統的觀點，包括質量屬性、架構風格和模式
- 將代碼與數據和模型相連的技術，包括關鍵的權衡
- 针对公平性、隱私和可解釋性的特定原則技術
- 負責任AI未來的預覽

Dr. Qinghua Lu is a principal research scientist and leads the Responsible AI science team at CSIRO's Data61. She received her PhD from University of New South Wales in 2013. Her current research interests include responsible AI, software engineering for AI/GAI, and software architecture. She has published 150+ papers in premier international journals and conferences. Her recent paper titled "Towards a Roadmap on Software Engineering for Responsible AI" received the ACM Distinguished Paper Award. Dr. Lu is part of the OECD.AI's trustworthy AI metrics project team. She also serves a member of Australia's National AI Centre Responsible AI at Scale think tank. She is the winner of the 2023 APAC Women in AI Trailblazer Award.

Dr./Prof. Liming Zhu is a Research Director at CSIRO's Data61 and a conjoint full professor at the University of New South Wales (UNSW). He is the chairperson of Standards Australia's blockchain committee and contributes to the AI trustworthiness committee. He is a member of the OECD.AI expert group on AI Risks and Accountability, as well as a member of the Responsible AI at Scale think tank at Australia's National AI Centre. His research program innovates in the areas of AI/ML systems, responsible/ethical AI, software engineering, blockchain, regulation technology, quantum software, privacy, and cybersecurity. He has published more than 300 papers on software architecture, blockchain, governance and responsible AI. He delivered the keynote "Software Engineering as the Linchpin of Responsible AI" at the International Conference on Software Engineering (ICSE) 2023.

Prof. Jon Whittle is Director at CSIRO's Data61, Australia's national centre for R&D in data science and digital technologies. With around 850 staff and affiliates, Data61 is one of the largest collections of R&D expertise in Artificial Intelligence and Data Science in the world. Data61 partners with more than 200 industry and government organisations, more than 30 universities, and works across vertical sectors in manufacturing, health, agriculture, and the environment. Prior to joining Data61, Jon was Dean of the Faculty of Information Technology at Monash University.

Dr. Xiwei Xu is a principal research scientist and the group leader of the software systems research group at Data61, CSIRO. With a specialization in software architecture and system design, she is at the forefront of research in these fields. Xiwei is identified by the Bibliometric Assessment of Software Engineering Scholars and Institutions as a top scholar and ranked 4th in the world (2013-2020) as the most impactful SE researchers by JSS (Journal of Systems and Software), a well-recognized academic journal in software engineering research.

Dr. Qinghua Lu 是澳洲CSIRO Data61的首席研究科學家，並領導著負責任的AI科學團隊。她於2013年獲得新南威爾士大學的博士學位。她目前的研究興趣包括負責任的AI、AI/GAI的軟體工程和軟體架構。她在國際頂級期刊和會議上發表了150多篇論文。她最近的論文《軟體工程負責任AI路線圖》獲得了ACM杰出論文獎。Lu博士是OECD.AI可信AI指標計劃小組的一員。她還是澳洲國家AI中心負責任AI規模智庫的成員。她是2023年APAC AI女性先鋒獎的獲獎者。

Dr./Prof. Liming Zhu 是CSIRO Data61的研究總監，也是新南威爾士大學（UNSW）的聯合教授。他是澳洲標準協會區塊鏈委員會的主席，並參與AI可信度委員會的工作。他是OECD.AI關於AI風險和問責制的專家小組成員，也是澳洲國家AI中心負責任AI規模智庫的成員。他的研究項目創新地涉及AI/ML系統、負責任/道德AI、軟體工程、區塊鏈、監管技術、量子軟體、隱私和網絡安全等領域。他在軟體架構、區塊鏈、治理和負責任AI方面發表了300多篇論文。他在2023年國際軟體工程大會（ICSE）上發表了主題演講《軟體工程作為負責任AI的關鍵》。

Prof. Jon Whittle 是澳洲CSIRO Data61的主任，該機構是澳洲國家數據科學和數字技術研發中心。Data61擁有約850名員工和合作夥伴，是全球最大的人工智能和數據科學研發專業機構之一。Data61與200多家工業和政府組織以及30多所大學合作，涉及製造業、醫療保健、農業和環境等垂直領域。在加入Data61之前，Jon曾擔任莫納什大學信息技術學院的院長。

Dr. Xiwei Xu 是CSIRO Data61的首席研究科學家，也是軟體系統研究小組的組長。她在軟體架構和系統設計方面具有專業知識，是這些領域的研究前沿人物。Xiwei被軟體工程學者和機構的文獻計量評估認定為頂尖學者，並在軟體工程研究領域的知名學術期刊《Journal of Systems and Software》中被評為世界上最有影響力的軟體工程研究者之一（2013-2020年）。