实践中的数据正义：开发人员指南
Data Justice in Practice: A Guide for Developers
摘要：
推进数据正义研究和实践项目旨在扩大对社会，历史，文化，政治和经济力量的理解，这些力量导致当代数据汇集，治理和使用生态中的歧视和不平等。这是一份面向开发人员和组织的指南的咨询草案，这些开发人员和组织正在生产，采购或使用数据密集型技术。在第一部分中，我们介绍了数据正义领域，从早期的讨论到最近的建议，以重新理解数据正义的含义。本节描述了本指南所围绕的数据公正的六大支柱。接下来，为了支持开发人员设计，开发和部署负责任和公平的数据密集型和AI/ML系统，我们通过社会技术视角概述了AI/ML项目生命周期。为了在整个人工智能/机器学习生命周期和数据创新生态系统中支持数据公正的运营，我们提出了负责、公平和值得信赖的数据研究和创新实践的五个首要原则，即安全、问责、公平、可解释性和数据质量、完整性、保护和隐私。最后一节提出了指导性问题，这些问题将帮助开发人员在整个AI/ML生命周期中解决数据公正问题，并参与反思性创新实践，以确保设计、开发和部署负责任和公平的数据密集型和AI/ML系统。
英文摘要：
The Advancing Data Justice Research and Practice project aims to broaden understanding of the social, historical, cultural, political, and economic forces that contribute to discrimination and inequity in contemporary ecologies of data collection, governance, and use. This is the consultation draft of a guide for developers and organisations, which are producing, procuring, or using data-intensive technologies.In the first section, we introduce the field of data justice, from its early discussions to more recent proposals to relocate understandings of what data justice means. This section includes a description of the six pillars of data justice around which this guidance revolves. Next, to support developers in designing, developing, and deploying responsible and equitable data-intensive and AI/ML systems, we outline the AI/ML project lifecycle through a sociotechnical lens. To support the operationalisation data justice throughout the entirety of the AI/ML lifecycle and within data innovation ecosystems, we then present five overarching principles of responsible, equitable, and trustworthy data research and innovation practices, the SAFE-D principles-Safety, Accountability, Fairness, Explainability, and Data Quality, Integrity, Protection, and Privacy. The final section presents guiding questions that will help developers both address data justice issues throughout the AI/ML lifecycle and engage in reflective innovation practices that ensure the design, development, and deployment of responsible and equitable data-intensive and AI/ML systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
