Counterfactual explanations (CFE) are methods that explain a machine learning
model by giving an alternate class prediction of a data point with some minimal
changes in its features. It helps the users to identify their data attributes
that caused an undesirable prediction like a loan or credit card rejection. We
describe an efficient and an actionable counterfactual (CF) generation method
based on particle swarm optimization (PSO). We propose a simple objective
function for the optimization of the instance-centric CF generation problem.
The PSO brings in a lot of flexibility in terms of carrying out multi-objective
optimization in large dimensions, capability for multiple CF generation, and
setting box constraints or immutability of data attributes. An algorithm is
proposed that incorporates these features and it enables greater control over
the proximity and sparsity properties over the generated CFs. The proposed
algorithm is evaluated with a set of action-ability metrics in real-world
datasets, and the results were superior compared to that of the
state-of-the-arts.

基于粒子群优化的高效可行反事实生成方法被提出，该算法在实际数据集上通过一组行动性指标评估，结果优于现有技术。

基于 PSO 的高维数据生成可操作的反事实

A PSO Based Method to Generate Actionable Counterfactuals for High  Dimensional Data

We identify the task of measuring data to quantitatively characterize the
composition of machine learning data and datasets. Similar to an object's
height, width, and volume, data measurements quantify different attributes of
data along common dimensions that support comparison. Several lines of research
have proposed what we refer to as measurements, with differing terminology; we
bring some of this work together, particularly in fields of computer vision and
language, and build from it to motivate measuring data as a critical component
of responsible AI development. Measuring data aids in systematically building
and analyzing machine learning (ML) data towards specific goals and gaining
better control of what modern ML systems will learn. We conclude with a
discussion of the many avenues of future work, the limitations of data
measurements, and how to leverage these measurement approaches in research and
practice.

本文旨在通过测量数据来量化机器学习数据和数据集的组成部分，以促进机器学习的系统构建和分析，使现代机器学习系统更加准确和可控，同时讨论了未来工作的许多途径、数据测量的局限性，以及如何在研究和实践中利用这些测量方法。

数据测量

Measuring Data

Business process enactment is generally supported by information systems that
record data about process executions, which can be extracted as event logs.
Predictive process monitoring is concerned with exploiting such event logs to
predict how running (uncompleted) cases will unfold up to their completion. In
this paper, we propose a predictive process monitoring framework for estimating
the probability that a given predicate will be fulfilled upon completion of a
running case. The predicate can be, for example, a temporal logic constraint or
a time constraint, or any predicate that can be evaluated over a completed
trace. The framework takes into account both the sequence of events observed in
the current trace, as well as data attributes associated to these events. The
prediction problem is approached in two phases. First, prefixes of previous
traces are clustered according to control flow information. Secondly, a
classifier is built for each cluster using event data to discriminate between
fulfillments and violations. At runtime, a prediction is made on a running case
by mapping it to a cluster and applying the corresponding classifier. The
framework has been implemented in the ProM toolset and validated on a log
pertaining to the treatment of cancer patients in a large hospital.

提出了一个基于事件日志的预测性流程监控框架，在运行中的案例中，通过将其映射到群集并应用相应的分类器，估计一个给定谓词在完成时将被满足的概率，该框架已在 ProM 工具集上实现并在一家大型医院的癌症治疗日志上验证。