Standard datasets are frequently used to train and evaluate Machine Learning
models. However, the assumed standardness of these datasets leads to a lack of
in-depth discussion on how their labels match the derived categories for the
respective use case. In other words, the standardness of the datasets seems to
fog coherency and applicability, thus impeding the trust in Machine Learning
models. We propose to adopt Grounded Theory and Hypotheses Testing through
Visualization as methods to evaluate the match between use case, derived
categories, and labels of standard datasets. To showcase the approach, we apply
it to the 20 Newsgroups dataset and the MNIST dataset. For the 20 Newsgroups
dataset, we demonstrate that the labels are imprecise. Therefore, we argue that
neither a Machine Learning model can learn a meaningful abstraction of derived
categories nor one can draw conclusions from achieving high accuracy. For the
MNIST dataset, we demonstrate how the labels can be confirmed to be defined
well. We conclude that a concept of standardness of a dataset implies that
there is a match between use case, derived categories, and class labels, as in
the case of the MNIST dataset. We argue that this is necessary to learn a
meaningful abstraction and, thus, improve trust in the Machine Learning model.

标准数据集的标签与所得类别的匹配度是影响机器学习模型信任度的关键因素，通过采用基于实践的理论和可视化等评估方法，我们证明了对于 20 个新闻组数据集和 MNIST 数据集，标准数据集的标签可能是不准确的，而 MNIST 数据集则表明了其标签的准确性，因此数据集的标准化与用例、派生类别和标签的匹配度密切相关，对于学习有意义的抽象和提高机器学习模型的信任度是必要的。

标准迷蒙含义：关于使用标准数据集的明智立场

Standardness Fogs Meaning: A Position Regarding the Informed Usage of  Standard Datasets

Powered by recent advances in code-generating models, AI assistants like
Github Copilot promise to change the face of programming forever. But what is
this new face of programming? We present the first grounded theory analysis of
how programmers interact with Copilot, based on observing 20 participants--with
a range of prior experience using the assistant--as they solve diverse
programming tasks across four languages. Our main finding is that interactions
with programming assistants are bimodal: in acceleration mode, the programmer
knows what to do next and uses Copilot to get there faster; in exploration
mode, the programmer is unsure how to proceed and uses Copilot to explore their
options. Based on our theory, we provide recommendations for improving the
usability of future AI programming assistants.

通过观察 20 名参与者与 Copilot 互动解决不同编程任务的方式，通过首次建立的理论分析表明，编程助手的交互为双峰分布：在加速模式下，程序员知道下一步该做什么并使用 Copilot 完成；而在探索模式下，程序员不确定如何进行并使用 Copilot 探索选项。基于我们的理论，我们提出了改善未来 AI 编程助手可用性的建议。

基于情境的副驾驶：程序员如何与代码生成模型交互

Grounded Copilot: How Programmers Interact with Code-Generating Models

To generate trust with their users, Explainable Artificial Intelligence (XAI)
systems need to include an explanation model that can communicate the internal
decisions, behaviours and actions to the interacting humans. Successful
explanation involves both cognitive and social processes. In this paper we
focus on the challenge of meaningful interaction between an explainer and an
explainee and investigate the structural aspects of an explanation in order to
propose a human explanation dialog model. We follow a bottom-up approach to
derive the model by analysing transcripts of 398 different explanation dialog
types. We use grounded theory to code and identify key components of which an
explanation dialog consists. We carry out further analysis to identify the
relationships between components and sequences and cycles that occur in a
dialog. We present a generalized state model obtained by the analysis and
compare it with an existing conceptual dialog model of explanation.

本文旨在探讨为了与用户建立信任，可解释人工智能系统需要包括一个解释模型，其可以向交互的人类沟通其内部的决策、行为和操作。该文通过对 398 种不同解释对话类型的记录进行分析，提出了一种人类解释对话模型，并使用基础理论进行编码和确定其关键成分，还进一步分析了对话中发生的序列和周期的关系，提出了一种泛化状态模型。