In the recent years, the raise in the usage and efficiency of Artificial
Intelligence and, more in general, of Automated Decision-Making systems has
brought with it an increasing and welcome awareness of the risks associated
with such systems. One of such risks is that of perpetuating or even amplifying
bias and unjust disparities present in the data from which many of these
systems learn to adjust and optimise their decisions. This awareness has on one
side encouraged several scientific communities to come up with more and more
appropriate ways and methods to assess, quantify, and possibly mitigate such
biases and disparities. On the other hand, it has prompted more and more layers
of society, including policy makers, to call for ``fair'' algorithms. We
believe that while a lot of excellent and multidisciplinary research is
currently being conducted, what is still fundamentally missing is the awareness
that having ``fair'' algorithms is per s\'e a nearly meaningless requirement,
that needs to be complemented with a lot of additional societal choices to
become actionable. Namely, there is a hiatus between what the society is
demanding from Automated Decision-Making systems, and what this demand actually
means in real-world scenarios. In this work, we outline the key features of
such a hiatus, and pinpoint a list of fundamental ambiguities and attention
points that we as a society must address in order to give a concrete meaning to
the increasing demand of fairness in Automated Decision-Making systems.

提出了针对自动决策系统的公平需求的知觉差异，指出了社会必须解决的基本模糊性和关注点列表，并为自动决策系统中对公平的增加需求赋予了具体含义。

足够公平？对 “公平” 算法要求的当前限制图谱

Fair Enough? A map of the current limitations of the requirements to  have "fair'' algorithms

Motivated by a plethora of practical examples where bias is induced by
automated-decision making algorithms, there has been strong recent interest in
the design of fair algorithms. However, there is often a dichotomy between
fairness and efficacy: fair algorithms may proffer low social welfare solutions
whereas welfare optimizing algorithms may be very unfair. This issue is
exemplified in the machine scheduling problem where, for $n$ jobs, the social
welfare of any fair solution may be a factor $\Omega(n)$ worse than the optimal
welfare. In this paper, we prove that this dichotomy between fairness and
efficacy can be overcome if we allow for a negligible amount of bias: there
exist algorithms that are both "almost perfectly fair" and have a constant
factor efficacy ratio, that is, are guaranteed to output solutions that have
social welfare within a constant factor of optimal welfare. Specifically, for
any $\epsilon>0$, there exist mechanisms with efficacy ratio
$\Theta(\frac{1}{\epsilon})$ and where no agent is more than an $\epsilon$
fraction worse off than they are in the fairest possible solution (given by an
algorithm that does not use personal or type data). Moreover, these bicriteria
guarantees are tight and apply to both the single machine case and the multiple
machine case. The key to our results are the use of Pareto scheduling
mechanisms. These mechanisms, by the judicious use of personal or type data,
are able to exploit Pareto improvements that benefit every individual; such
Pareto improvements would typically be forbidden by fair scheduling algorithms
designed to satisfy standard statistical measures of group fairness. We
anticipate this paradigm, the judicious use of personal data by a fair
algorithm to greatly improve performance at the cost of negligible bias, has
wider application.

本论文探讨了自动决策算法引起的偏见问题，提出了一种通过合理使用个人数据来平衡公平和有效性之间矛盾的 Pareto 调度机制，以及带来的社会福利和个人利益之间的差异。

公平算法设计：公平高效的机器调度

Fair Algorithm Design: Fair and Efficacious Machine Scheduling

In this work, we argue for the importance of causal reasoning in creating
fair algorithms for decision making. We give a review of existing approaches to
fairness, describe work in causality necessary for the understanding of causal
approaches, argue why causality is necessary for any approach that wishes to be
fair, and give a detailed analysis of the many recent approaches to
causality-based fairness.

本文讨论在决策制定中创造公平算法的因果推理的重要性，回顾了现有的公正性方法，描述了因果方法理解所必需的工作，论证了在希望公平的任何方法中都需要因果性，并详细分析了许多近期因果公平的方式。