The vast majority of techniques to train fair models require access to the
protected attribute (e.g., race, gender), either at train time or in
production. However, in many important applications this protected attribute is
largely unavailable. In this paper, we develop methods for measuring and
reducing fairness violations in a setting with limited access to protected
attribute labels. Specifically, we assume access to protected attribute labels
on a small subset of the dataset of interest, but only probabilistic estimates
of protected attribute labels (e.g., via Bayesian Improved Surname Geocoding)
for the rest of the dataset. With this setting in mind, we propose a method to
estimate bounds on common fairness metrics for an existing model, as well as a
method for training a model to limit fairness violations by solving a
constrained non-convex optimization problem. Unlike similar existing
approaches, our methods take advantage of contextual information --
specifically, the relationships between a model's predictions and the
probabilistic prediction of protected attributes, given the true protected
attribute, and vice versa -- to provide tighter bounds on the true disparity.
We provide an empirical illustration of our methods using voting data. First,
we show our measurement method can bound the true disparity up to 5.5x tighter
than previous methods in these applications. Then, we demonstrate that our
training technique effectively reduces disparity while incurring lesser
fairness-accuracy trade-offs than other fair optimization methods with limited
access to protected attributes.

在有限的受保护属性标签访问情况下，本文开发了测量和减少公平违规的方法，提出了估计现有模型的常见公平度量和解决约束非凸优化问题以限制公平违规的模型训练技术，并且相比其他受限受保护属性的公平优化方法，在减小公平度的同时减少了公平性和准确性的权衡。

利用概率受保护特征估计和实现常规公平度量

Estimating and Implementing Conventional Fairness Metrics With  Probabilistic Protected Features

Limited access to computing resources and training data poses significant
challenges for individuals and groups aiming to train and utilize predictive
machine learning models. Although numerous publicly available machine learning
models exist, they are often unhosted, necessitating end-users to establish
their computational infrastructure. Alternatively, these models may only be
accessible through paid cloud-based mechanisms, which can prove costly for
general public utilization. Moreover, model and data providers require a more
streamlined approach to track resource usage and capitalize on subsequent usage
by others, both financially and otherwise. An effective mechanism is also
lacking to contribute high-quality data for improving model performance. We
propose a blockchain-based marketplace called "PredictChain" for predictive
machine-learning models to address these issues. This marketplace enables users
to upload datasets for training predictive machine learning models, request
model training on previously uploaded datasets, or submit queries to trained
models. Nodes within the blockchain network, equipped with available computing
resources, will operate these models, offering a range of archetype machine
learning models with varying characteristics, such as cost, speed, simplicity,
power, and cost-effectiveness. This decentralized approach empowers users to
develop improved models accessible to the public, promotes data sharing, and
reduces reliance on centralized cloud providers.

基于区块链的市场 ——“PredictChain”，用于解决有限访问、预测机器学习模型和数据分享方面的挑战。通过该市场，用户可以上传数据集来训练机器学习模型，请求对先前上传的数据集进行模型训练或查询训练好的模型，并由区块链网络中的节点提供计算资源以便操作这些模型，以满足不同特点、如成本、速度、简单性、性能和成本效益等的原型机器学习模型需求。这种去中心化的方法赋予用户开发改进模型的能力，促进数据共享，并减少对集中式云提供商的依赖。

PredictChain: 在分散式基於區塊鏈的市場中賦予協作和數據可訪問性的人工智能

PredictChain: Empowering Collaboration and Data Accessibility for AI in  a Decentralized Blockchain-based Marketplace

Multi-player multi-armed bandit is an increasingly relevant decision-making
problem, motivated by applications to cognitive radio systems. Most research
for this problem focuses exclusively on the settings that players have
\textit{full access} to all arms and receive no reward when pulling the same
arm. Hence all players solve the same bandit problem with the goal of
maximizing their cumulative reward. However, these settings neglect several
important factors in many real-world applications, where players have
\textit{limited access} to \textit{a dynamic local subset of arms} (i.e., an
arm could sometimes be ``walking'' and not accessible to the player). To this
end, this paper proposes a \textit{multi-player multi-armed walking bandits}
model, aiming to address aforementioned modeling issues. The goal now is to
maximize the reward, however, players can only pull arms from the local subset
and only collect a full reward if no other players pull the same arm. We adopt
Upper Confidence Bound (UCB) to deal with the exploration-exploitation tradeoff
and employ distributed optimization techniques to properly handle collisions.
By carefully integrating these two techniques, we propose a decentralized
algorithm with near-optimal guarantee on the regret, and can be easily
implemented to obtain competitive empirical performance.

提出了一种解决多人多臂赌博机问题的分布式算法，利用上置信区间和分布式优化技术，解决了现实世界应用中玩家仅能访问动态局部子集的问题，并获得了接近最优的后悔率。