This paper presents a set of intersectional feminist principles for
conducting equitable, ethical, and sustainable AI research. In Data Feminism
(2020), we offered seven principles for examining and challenging unequal power
in data science. Here, we present a rationale for why feminism remains deeply
relevant for AI research, rearticulate the original principles of data feminism
with respect to AI, and introduce two potential new principles related to
environmental impact and consent. Together, these principles help to 1) account
for the unequal, undemocratic, extractive, and exclusionary forces at work in
AI research, development, and deployment; 2) identify and mitigate predictable
harms in advance of unsafe, discriminatory, or otherwise oppressive systems
being released into the world; and 3) inspire creative, joyful, and collective
ways to work towards a more equitable, sustainable world in which all of us can
thrive.

通过引入新的原则并与环境影响和同意相关，本文在探讨为进行公平、道德和可持续的 AI 研究而提出的交叉女权主义原则的基础上，阐述了女权主义对 AI 研究的重要性，并旨在解决 AI 研究中不平等、非民主、剥削和排斥力量，预防不安全、歧视或压迫性系统释放到世界上产生可预测的危害，并激发创造性、快乐和集体的方式，致力于建立一个更具公平、可持续的世界，让我们所有人都能蓬勃发展。

数据女权主义与人工智能

Data Feminism for AI

The burgeoning integration of Artificial Intelligence (AI) into
Environmental, Social, and Governance (ESG) initiatives within the financial
sector represents a paradigm shift towards more sus-tainable and equitable
financial practices. This paper surveys the industrial landscape to delineate
the necessity and impact of AI in bolstering ESG frameworks. With the advent of
stringent regulatory requirements and heightened stakeholder awareness,
financial institutions (FIs) are increasingly compelled to adopt ESG criteria.
AI emerges as a pivotal tool in navigating the complex in-terplay of financial
activities and sustainability goals. Our survey categorizes AI applications
across three main pillars of ESG, illustrating how AI enhances analytical
capabilities, risk assessment, customer engagement, reporting accuracy and
more. Further, we delve into the critical con-siderations surrounding the use
of data and the development of models, underscoring the importance of data
quality, privacy, and model robustness. The paper also addresses the imperative
of responsible and sustainable AI, emphasizing the ethical dimensions of AI
deployment in ESG-related banking processes. Conclusively, our findings suggest
that while AI offers transformative potential for ESG in banking, it also poses
significant challenges that necessitate careful consideration. The final part
of the paper synthesizes the survey's insights, proposing a forward-looking
stance on the adoption of AI in ESG practices. We conclude with recommendations
with a reference architecture for future research and development, advocating
for a balanced approach that leverages AI's strengths while mitigating its
risks within the ESG domain.

人工智能在环境、社会和治理（ESG）倡议中的蓬勃融合代表了对更可持续和公平的金融实践的范式转变。本文调查了工业景观，描述了人工智能在支持 ESG 框架方面的必要性和影响。我们的调查将人工智能应用程序划分为 ESG 的三个主要支柱，并且说明了人工智能如何增强分析能力、风险评估、客户参与、报告准确性等方面的能力。此外，我们还深入探讨了数据使用和模型发展方面的关键问题，强调了数据质量、隐私和模型的鲁棒性的重要性。本文还强调了负责任和可持续的人工智能的必要性，强调人工智能在 ESG 相关银行业务中的伦理维度。总而言之，我们的发现表明，虽然人工智能为银行业的 ESG 提供了变革性的潜力，但也带来了重大挑战，需要仔细考虑。本文最后综合了调查的见解，提出了在 ESG 实践中采用人工智能的前瞻性立场。我们最后通过提出一个未来研究和开发的参考架构来总结，主张在 ESG 领域中充分利用人工智能的优势并减轻其风险的平衡方法。