The topic of fairness in AI, as debated in the FATE (Fairness,
Accountability, Transparency, and Ethics in AI) communities, has sparked
meaningful discussions in the past years. However, from a legal perspective,
particularly from European Union law, many open questions remain. Whereas
algorithmic fairness aims to mitigate structural inequalities at the design
level, European non-discrimination law is tailored to individual cases of
discrimination after an AI model has been deployed. The AI Act might present a
tremendous step towards bridging these two concepts by shifting
non-discrimination responsibilities into the design stage of AI models. Based
on an integrative reading of the AI Act, we comment on legal as well as
technical enforcement problems and propose practical implications on bias
detection and bias correction in order to specify and comply with specific
technical requirements.

AI Act 试图在 AI 模型的设计阶段，将非歧视责任与算法公平两个概念联系起来，以缓解结构性的不平等问题，并提出了对于偏见检测和偏见修正的实际应用和技术要求方面的建议。

AI 法案对非歧视法和算法公平性的影响

Implications of the AI Act for Non-Discrimination Law and Algorithmic  Fairness

Fairness in AI is a growing concern for high-stakes decision making. Engaging
stakeholders, especially lay users, in fair AI development is promising yet
overlooked. Recent efforts explore enabling lay users to provide AI
fairness-related feedback, but there is still a lack of understanding of how to
integrate users' feedback into an AI model and the impacts of doing so. To
bridge this gap, we collected feedback from 58 lay users on the fairness of a
XGBoost model trained on the Home Credit dataset, and conducted offline
experiments to investigate the effects of retraining models on accuracy, and
individual and group fairness. Our work contributes baseline results of
integrating user fairness feedback in XGBoost, and a dataset and code framework
to bootstrap research in engaging stakeholders in AI fairness. Our discussion
highlights the challenges of employing user feedback in AI fairness and points
the way to a future application area of interactive machine learning.

人工智能的公平性在高风险决策中越来越引起关注，让利益相关者，尤其是普通用户，参与公平的人工智能开发具有潜力但往往被忽视。最近的努力探索了让普通用户提供与公平性相关的反馈的方法，但对如何将用户的反馈整合到人工智能模型中以及这样做的影响尚不了解。为了填补这一差距，我们从 58 个普通用户收集了关于一个在 Home Credit 数据集上训练的 XGBoost 模型公平性的反馈，并进行了离线实验，以研究重新训练模型对准确性、个体和团体公平性的影响。我们的工作为在 XGBoost 中整合用户公平反馈提供了基准结果，并提供了一个数据集和代码框架，以推动参与利益相关者研究人工智能公平性的起步工作。我们的讨论突出了在人工智能公平性中使用用户反馈面临的挑战，并指出了交互式机器学习的未来应用领域的方向。

探索非专业用户反馈对提升 AI 公平性的影响

Exploring the Impact of Lay User Feedback for Improving AI Fairness

Fairness in AI has garnered quite some attention in research, and
increasingly also in society. The so-called "Impossibility Theorem" has been
one of the more striking research results with both theoretical and practical
consequences, as it states that satisfying a certain combination of fairness
measures is impossible. To date, this negative result has not yet been
complemented with a positive one: a characterization of which combinations of
fairness notions are possible. This work aims to fill this gap by identifying
maximal sets of commonly used fairness measures that can be simultaneously
satisfied. The fairness measures used are demographic parity, equal
opportunity, false positive parity, predictive parity, predictive equality,
overall accuracy equality and treatment equality. We conclude that in total 12
maximal sets of these fairness measures are possible, among which seven
combinations of two measures, and five combinations of three measures. Our work
raises interest questions regarding the practical relevance of each of these 12
maximal fairness notions in various scenarios.

本文研究了 AI 中的公平性问题，探讨了 Fairness measures 可能存在的最大集合以及一些实际应用方面的问题。

最大公平性

Maximal Fairness

Despite being responsible for state-of-the-art results in several computer
vision and natural language processing tasks, neural networks have faced harsh
criticism due to some of their current shortcomings. One of them is that neural
networks are correlation machines prone to model biases within the data instead
of focusing on actual useful causal relationships. This problem is particularly
serious in application domains affected by aspects such as race, gender, and
age. To prevent models from incurring on unfair decision-making, the AI
community has concentrated efforts in correcting algorithmic biases, giving
rise to the research area now widely known as fairness in AI. In this survey
paper, we provide an in-depth overview of the main debiasing methods for
fairness-aware neural networks in the context of vision and language research.
We propose a novel taxonomy to better organize the literature on debiasing
methods for fairness, and we discuss the current challenges, trends, and
important future work directions for the interested researcher and
practitioner.

在电脑视觉和自然语言处理领域中，神经网络虽然能够取得最新成果，但是其存在数据内的建模偏见，导致人工智能领域出现了公平性的研究方向，其目的为了纠正算法偏见，提出了几种基于公平性的神经网络去偏置的方法。