Recent advancements in machine learning and deep learning have brought
algorithmic fairness into sharp focus, illuminating concerns over
discriminatory decision making that negatively impacts certain individuals or
groups. These concerns have manifested in legal, ethical, and societal
challenges, including the erosion of trust in intelligent systems. In response,
this survey delves into the existing literature on algorithmic fairness,
specifically highlighting its multifaceted social consequences. We introduce a
novel taxonomy based on 'tolerance', a term we define as the degree to which
variations in fairness outcomes are acceptable, providing a structured approach
to understanding the subtleties of fairness within algorithmic decisions. Our
systematic review covers diverse industries, revealing critical insights into
the balance between algorithmic decision making and social equity. By
synthesizing these insights, we outline a series of emerging challenges and
propose strategic directions for future research and policy making, with the
goal of advancing the field towards more equitable algorithmic systems.

机器学习和深度学习的最新进展使得算法公平成为一个关注焦点，引发对影响某些个体或群体的歧视性决策的担忧。本调研综述基于 “容忍度” 提出了一个新的分类系统，揭示了算法决策中公平的微妙性。通过分析多个行业，我们的系统性综述揭示了算法决策与社会公平之间的关键见解。通过综合这些见解，我们勾勒出一系列新兴挑战，并提出未来研究和政策制定的战略方向，以推动该领域朝着更加公平的算法系统发展。

算法公平性：一个容忍视角

Algorithmic Fairness: A Tolerance Perspective

The Generative Pre-trained Transformer models represent a notable
breakthrough in the domain of natural language processing, which is propelling
us toward the development of machines that can understand and communicate using
language in a manner that closely resembles that of humans. Generative
Pre-trained Transformer models are based on the transformer architecture, a
deep neural network designed for natural language processing tasks. Due to
their impressive performance on natural language processing tasks and ability
to effectively converse, Generative Pre-trained Transformer models have gained
significant popularity among researchers and industrial communities, making
them one of the most widely used and effective models in natural language
processing and related fields, which motivated to conduct this review. This
review provides a detailed overview of the Generative Pre-trained Transformer,
including its architecture, working process, training procedures, enabling
technologies, and its impact on various applications. In this review, we also
explored the potential challenges and limitations of a Generative Pre-trained
Transformer. Furthermore, we discuss potential solutions and future directions.
Overall, this paper aims to provide a comprehensive understanding of Generative
Pre-trained Transformers, enabling technologies, their impact on various
applications, emerging challenges, and potential solutions.

本文综述了生成式预训练转换模型，包括它的架构、工作流程、训练程序、相关技术以及对各种应用的影响，并探讨了潜在的挑战和解决方案。

生成预训练变压器：启用技术、潜在应用、新兴挑战和未来方向的全面评估

Generative Pre-trained Transformer: A Comprehensive Review on Enabling  Technologies, Potential Applications, Emerging Challenges, and Future  Directions