没有表示就没有计算：通过多样性避免数据和算法偏差
No computation without representation: Avoiding data and algorithm biases through diversity
摘要：
人工智能中伦理问题研究的出现和发展，特别是算法公平性，源于一个基本的观察，即社会中的结构性不平等反映在用于训练预测模型的数据和目标函数的设计中。虽然旨在缓解这些问题的研究本质上是跨学科的，但设计无偏见的算法和公平的社会技术系统是关键的预期成果，这取决于数据科学和计算领域的从业者。然而，这些计算领域也普遍存在与我们分析的数据集中相同的代表性不足问题。这种脱节既影响了预期结果的设计，也影响了我们衡量成功的指标。如果道德人工智能研究界接受这一点，我们就默认了现状，并违背了算法公平性、问责制和透明度所寻求解决的非歧视和公平的目标。因此，在这项工作中，我们倡导将计算多样化作为该领域的核心优先事项，并努力实现合乎道德的人工智能实践。我们在学术和专业计算领域缺乏多样性与数据集，机器学习模型，问题公式和结果解释中遇到的偏见的类型和广度之间建立了联系。通过研究人工智能文献中当前的公平性/道德，我们强调了这种缺乏多样性观点的情况，这是处理代表性不足和受保护群体数据的不公平的基础。我们还关注其他专业领域，如法律和卫生领域，这些领域的受训人员教育多样性和专业实践之间的差距已经缩小。我们利用这些经验教训来制定建议，为计算社区提供增加多样性的具体步骤。
英文摘要：
The emergence and growth of research on issues of ethics in AI, and in particular algorithmic fairness, has roots in an essential observation that structural inequalities in society are reflected in the data used to train predictive models and in the design of objective functions. While research aiming to mitigate these issues is inherently interdisciplinary, the design of unbiased algorithms and fair socio-technical systems are key desired outcomes which depend on practitioners from the fields of data science and computing. However, these computing fields broadly also suffer from the same under-representation issues that are found in the datasets we analyze. This disconnect affects the design of both the desired outcomes and metrics by which we measure success. If the ethical AI research community accepts this, we tacitly endorse the status quo and contradict the goals of non-discrimination and equity which work on algorithmic fairness, accountability, and transparency seeks to address. Therefore, we advocate in this work for diversifying computing as a core priority of the field and our efforts to achieve ethical AI practices. We draw connections between the lack of diversity within academic and professional computing fields and the type and breadth of the biases encountered in datasets, machine learning models, problem formulations, and interpretation of results. Examining the current fairness/ethics in AI literature, we highlight cases where this lack of diverse perspectives has been foundational to the inequity in treatment of underrepresented and protected group data. We also look to other professional communities, such as in law and health, where disparities have been reduced both in the educational diversity of trainees and among professional practices. We use these lessons to develop recommendations that provide concrete steps for the computing community to increase diversity.
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。
