规范AI公平性评价的七层模型
A Seven-Layer Model for Standardising AI Fairness Assessment
摘要：
问题陈述：人工智能公平性规则和基准的标准化是具有挑战性的，因为人工智能公平性和其他道德要求取决于多种因素，如上下文，用例，人工智能系统的类型等。在本文中，我们阐述了人工智能系统在其生命周期的每个阶段都容易产生偏见，从开始到使用，所有阶段都需要适当注意减轻人工智能偏见。我们需要一种标准化的方法来处理每个阶段的AI公平性。差距分析：虽然人工智能公平性是一个热门的研究课题，但人工智能公平性的整体策略通常缺失。大多数研究人员只关注人工智能模型构建的几个方面。同行评审显示过度关注数据集、公平性指标和算法偏见中的偏差。在这个过程中，影响AI公平性的其他方面被忽略了。建议的解决办法是：我们提出了一个全面的方法，在一个新的七层模型的形式，灵感来自开放系统互连（OSI）模型，以简化AI公平处理。尽管在各个方面存在差异，但大多数人工智能系统都有类似的建模阶段。该模型将AI系统生命周期分为七个抽象层，每个抽象层对应于一个定义良好的AI模型构建或使用阶段。我们还为每一层提供了检查表，并对每一层的潜在偏差来源及其缓解方法进行了审议。这项工作将促进人工智能公平规则和基准参数的分层标准化。
英文摘要：
Problem statement: Standardisation of AI fairness rules and benchmarks is challenging because AI fairness and other ethical requirements depend on multiple factors such as context, use case, type of the AI system, and so on. In this paper, we elaborate that the AI system is prone to biases at every stage of its lifecycle, from inception to its usage, and that all stages require due attention for mitigating AI bias. We need a standardised approach to handle AI fairness at every stage. Gap analysis: While AI fairness is a hot research topic, a holistic strategy for AI fairness is generally missing. Most researchers focus only on a few facets of AI model-building. Peer review shows excessive focus on biases in the datasets, fairness metrics, and algorithmic bias. In the process, other aspects affecting AI fairness get ignored. The solution proposed: We propose a comprehensive approach in the form of a novel seven-layer model, inspired by the Open System Interconnection (OSI) model, to standardise AI fairness handling. Despite the differences in the various aspects, most AI systems have similar model-building stages. The proposed model splits the AI system lifecycle into seven abstraction layers, each corresponding to a well-defined AI model-building or usage stage. We also provide checklists for each layer and deliberate on potential sources of bias in each layer and their mitigation methodologies. This work will facilitate layer-wise standardisation of AI fairness rules and benchmarking parameters.
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。
