Artificial intelligence (AI) systems can cause harm to people. This research
examines how individuals react to such harm through the lens of blame. Building
upon research suggesting that people blame AI systems, we investigated how
several factors influence people's reactive attitudes towards machines,
designers, and users. The results of three studies (N = 1,153) indicate
differences in how blame is attributed to these actors. Whether AI systems were
explainable did not impact blame directed at them, their developers, and their
users. Considerations about fairness and harmfulness increased blame towards
designers and users but had little to no effect on judgments of AI systems.
Instead, what determined people's reactive attitudes towards machines was
whether people thought blaming them would be a suitable response to algorithmic
harm. We discuss implications, such as how future decisions about including AI
systems in the social and moral spheres will shape laypeople's reactions to
AI-caused harm.

本研究探讨人们如何通过责备的方式来反应人工智能（AI）系统可能对人们带来的伤害，并研究了多种因素对人们对机器、设计者和用户的反应态度的影响。三项研究结果表明，人们归咎于不同的行动者，而不是 AI 系统的可解释性决定了人们的反应态度。

谁之过：是什么影响人们对算法伤害的反应

Blaming Humans and Machines: What Shapes People's Reactions to  Algorithmic Harm

Ensembling independent deep neural networks (DNNs) is a simple and effective
way to improve top-line metrics and to outperform larger single models. In this
work, we go beyond top-line metrics and instead explore the impact of
ensembling on subgroup performances. Surprisingly, even with a simple
homogenous ensemble -- all the individual models share the same training set,
architecture, and design choices -- we find compelling and powerful gains in
worst-k and minority group performance, i.e. fairness naturally emerges from
ensembling. We show that the gains in performance from ensembling for the
minority group continue for far longer than for the majority group as more
models are added. Our work establishes that simple DNN ensembles can be a
powerful tool for alleviating disparate impact from DNN classifiers, thus
curbing algorithmic harm. We also explore why this is the case. We find that
even in homogeneous ensembles, varying the sources of stochasticity through
parameter initialization, mini-batch sampling, and the data-augmentation
realizations, results in different fairness outcomes.

通过分析异质性模型，我们探究了不同培训集合的神经网络集成对子群体表现的影响。发现使用同质集成，即使所有单独模型使用相同的训练集，架构和设计选择，仍然可以获得令人信服和有力的最坏 k 值和少数群体的表现提升。我们的研究证明，简单的神经网络集成可以是减轻不同 DNN 分类器的不同影响的有效工具，从而遏制算法伤害。

FAIR-Ensemble: 深度集成自然产生公平性

FAIR-Ensemble: When Fairness Naturally Emerges From Deep Ensembling

Deployed AI systems often do not work. They can be constructed haphazardly,
deployed indiscriminately, and promoted deceptively. However, despite this
reality, scholars, the press, and policymakers pay too little attention to
functionality. This leads to technical and policy solutions focused on
"ethical" or value-aligned deployments, often skipping over the prior question
of whether a given system functions, or provides any benefits at all. To
describe the harms of various types of functionality failures, we analyze a set
of case studies to create a taxonomy of known AI functionality issues. We then
point to policy and organizational responses that are often overlooked and
become more readily available once functionality is drawn into focus. We argue
that functionality is a meaningful AI policy challenge, operating as a
necessary first step towards protecting affected communities from algorithmic
harm.

AI 系统的功能性是一个重要的挑战，在保护受影响的社区免受算法伤害方面发挥着必要的第一步作用。本文通过对一组案例进行分析，提出一套已知 AI 功能性问题的分类法，并指出通常被忽视的政策和组织反应，一旦功能性被集中关注将更易获取。