Two goals - improving replicability and accountability of Machine Learning
research respectively, have accrued much attention from the AI ethics and the
Machine Learning community. Despite sharing the measures of improving
transparency, the two goals are discussed in different registers -
replicability registers with scientific reasoning whereas accountability
registers with ethical reasoning. Given the existing challenge of the
Responsibility Gap - holding Machine Learning scientists accountable for
Machine Learning harms due to them being far from sites of application, this
paper posits that reconceptualizing replicability can help bridge the gap.
Through a shift from model performance replicability to claim replicability,
Machine Learning scientists can be held accountable for producing
non-replicable claims that are prone to eliciting harm due to misuse and
misinterpretation. In this paper, I make the following contributions. First, I
define and distinguish two forms of replicability for ML research that can aid
constructive conversations around replicability. Second, I formulate an
argument for claim-replicability's advantage over model performance
replicability in justifying assigning accountability to Machine Learning
scientists for producing non-replicable claims and show how it enacts a sense
of responsibility that is actionable. In addition, I characterize the
implementation of claim replicability as more of a social project than a
technical one by discussing its competing epistemological principles, practical
implications on Circulating Reference, Interpretative Labor, and research
communication.

通过将模型性能可复制性转变为主张可复制性，可以使机器学习科学家对产生不可复制的主张负责并对其造成的滥用和误解负责，从而缩小责任差距。

从模型性能到声明：机器学习可复现性的焦点转变如何帮助弥合责任鸿沟

From Model Performance to Claim: How a Change of Focus in Machine  Learning Replicability Can Help Bridge the Responsibility Gap

AI-Based Safety-Critical Systems (AI-SCS) are being increasingly deployed in
the real world. These can pose a risk of harm to people and the environment.
Reducing that risk is an overarching priority during development and operation.
As more AI-SCS become autonomous, a layer of risk management via human
intervention has been removed. Following an accident it will be important to
identify causal contributions and the different responsible actors behind those
to learn from mistakes and prevent similar future events. Many authors have
commented on the "responsibility gap" where it is difficult for developers and
manufacturers to be held responsible for harmful behaviour of an AI-SCS. This
is due to the complex development cycle for AI, uncertainty in AI performance,
and dynamic operating environment. A human operator can become a "liability
sink" absorbing blame for the consequences of AI-SCS outputs they weren't
responsible for creating, and may not have understanding of.
This cross-disciplinary paper considers different senses of responsibility
(role, moral, legal and causal), and how they apply in the context of AI-SCS
safety. We use a core concept (Actor(A) is responsible for Occurrence(O)) to
create role responsibility models, producing a practical method to capture
responsibility relationships and provide clarity on the previously identified
responsibility issues. Our paper demonstrates the approach with two examples: a
retrospective analysis of the Tempe Arizona fatal collision involving an
autonomous vehicle, and a safety focused predictive role-responsibility
analysis for an AI-based diabetes co-morbidity predictor. In both examples our
primary focus is on safety, aiming to reduce unfair or disproportionate blame
being placed on operators or developers. We present a discussion and avenues
for future research.

AI - 基于安全关键系统（AI-SCS）在现实世界中的部署越来越多。降低风险是开发和运营中的首要任务。本文考虑了责任的不同方面（角色、道德、法律和因果关系），以及它们在 AI-SCS 安全背景下的应用。我们使用一个核心概念（演员（A）对事件（O）负责）来创建角色责任模型，从而产生了一种捕捉责任关系并澄清以前已知的责任问题的实用方法。通过对事故案例和基于 AI 的糖尿病并发症预测器的安全性分析，我们的主要目标是在安全性方面减少对操作员或开发人员的不公平或不成比例的责备。我们提出了进一步研究的讨论和方向。