Algorithmic fairness in decision-making has been studied extensively in
static settings where one-shot decisions are made on tasks such as
classification. However, in practice most decision-making processes are of a
sequential nature, where decisions made in the past may have an impact on
future data. This is particularly the case when decisions affect the
individuals or users generating the data used for future decisions. In this
survey, we review existing literature on the fairness of data-driven sequential
decision-making. We will focus on two types of sequential decisions: (1) past
decisions have no impact on the underlying user population and thus no impact
on future data; (2) past decisions have an impact on the underlying user
population and therefore the future data, which can then impact future
decisions. In each case the impact of various fairness interventions on the
underlying population is examined.

本文综述了关于数据驱动的序贯决策的公平性的现有文献，重点讨论了两种类型的序贯决策及各种公平性干预措施对基础人口的影响。

基于学习的序列决策算法中的公平性：综述

Fairness in Learning-Based Sequential Decision Algorithms: A Survey

Designing agents capable of explaining complex sequential decisions remain a
significant open problem in automated decision-making. Recently, there has been
a lot of interest in developing approaches for generating such explanations for
various decision-making paradigms. One such approach has been the idea of {\em
explanation as model-reconciliation}. The framework hypothesizes that one of
the common reasons for the user's confusion could be the mismatch between the
user's model of the task and the one used by the system to generate the
decisions. While this is a general framework, most works that have been
explicitly built on this explanatory philosophy have focused on settings where
the model of user's knowledge is available in a declarative form. Our goal in
this paper is to adapt the model reconciliation approach to the cases where
such user models are no longer explicitly provided. We present a simple and
easy to learn labeling model that can help an explainer decide what information
could help achieve model reconciliation between the user and the agent.

本文提出了一种简单易学的标注模型，以帮助解释者决定哪些信息有助于实现用户与代理之间的模型对接，从而将 “解释” 作为 “模型对接” 来完成。

无模型调和模型

Model-Free Model Reconciliation

Bounded rationality, that is, decision-making and planning under resource
limitations, is widely regarded as an important open problem in artificial
intelligence, reinforcement learning, computational neuroscience and economics.
This paper offers a consolidated presentation of a theory of bounded
rationality based on information-theoretic ideas. We provide a conceptual
justification for using the free energy functional as the objective function
for characterizing bounded-rational decisions. This functional possesses three
crucial properties: it controls the size of the solution space; it has Monte
Carlo planners that are exact, yet bypass the need for exhaustive search; and
it captures model uncertainty arising from lack of evidence or from interacting
with other agents having unknown intentions. We discuss the single-step
decision-making case, and show how to extend it to sequential decisions using
equivalence transformations. This extension yields a very general class of
decision problems that encompass classical decision rules (e.g. EXPECTIMAX and
MINIMAX) as limit cases, as well as trust- and risk-sensitive planning.

本文提出了基于信息理论思想的有限理性理论，并提供了将自由能量函数作为表征有限理性决策的目标函数的概念上的理据。该文讨论了单步决策以及如何使用等价变换将其扩展到序贯决策，扩展后得到的类别决策问题非常广泛，包括古典决策规则等极限情况以及可信和风险敏感规划。