Explainability techniques for data-driven predictive models based on
artificial intelligence and machine learning algorithms allow us to better
understand the operation of such systems and help to hold them accountable. New
transparency approaches are developed at breakneck speed, enabling us to peek
inside these black boxes and interpret their decisions. Many of these
techniques are introduced as monolithic tools, giving the impression of
one-size-fits-all and end-to-end algorithms with limited customisability.
Nevertheless, such approaches are often composed of multiple interchangeable
modules that need to be tuned to the problem at hand to produce meaningful
explanations. This paper introduces a collection of hands-on training materials
-- slides, video recordings and Jupyter Notebooks -- that provide guidance
through the process of building and evaluating bespoke modular surrogate
explainers for tabular data. These resources cover the three core building
blocks of this technique: interpretable representation composition, data
sampling and explanation generation.

介绍了通过解释性表现组合、数据采样和解释生成构建和评估定制模块化替代解释器的过程的手册，以帮助了解人工智能和机器学习算法的可解释性技术。

机器学习透明性的什么和怎样：使用可互操作算法组件构建定制可解释性工具

What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components

As data-driven predictive models are increasingly used to inform decisions,
it has been argued that decision makers should provide explanations that help
individuals understand what would have to change for these decisions to be
beneficial ones. However, there has been little discussion on the possibility
that individuals may use the above counterfactual explanations to invest effort
strategically and maximize their chances of receiving a beneficial decision. In
this paper, our goal is to find policies and counterfactual explanations that
are optimal in terms of utility in such a strategic setting. We first show
that, given a pre-defined policy, the problem of finding the optimal set of
counterfactual explanations is NP-hard. Then, we show that the corresponding
objective is nondecreasing and satisfies submodularity and this allows a
standard greedy algorithm to enjoy approximation guarantees. In addition, we
further show that the problem of jointly finding both the optimal policy and
set of counterfactual explanations reduces to maximizing a non-monotone
submodular function. As a result, we can use a recent randomized algorithm to
solve the problem, which also offers approximation guarantees. Finally, we
demonstrate that, by incorporating a matroid constraint into the problem
formulation, we can increase the diversity of the optimal set of counterfactual
explanations and incentivize individuals across the whole spectrum of the
population to self improve. Experiments on synthetic and real lending and
credit card data illustrate our theoretical findings and show that the
counterfactual explanations and decision policies found by our algorithms
achieve higher utility than several competitive baselines.

本文旨在在战略环境下寻找最优的政策和对策解释，包括 NP 难的问题，非降性和子模性，用标准贪心算法获得近似保证。最后，我们表明通过将拟阵约束加入问题的制定中，我们可以提高对策解释的最优集合的多样性，并激励整个人口谱上的个体自我改进。

决策、反事实解释与战略行为

Decisions, Counterfactual Explanations and Strategic Behavior

We are witnessing an increasing use of data-driven predictive models to
inform decisions. As decisions have implications for individuals and society,
there is increasing pressure on decision makers to be transparent about their
decision policies. At the same time, individuals may use knowledge, gained by
transparency, to invest effort strategically in order to maximize their chances
of receiving a beneficial decision. Our goal is to find decision policies that
are optimal in terms of utility in such a strategic setting. To this end, we
first characterize how strategic investment of effort by individuals leads to a
change in the feature distribution. Using this characterization, we first show
that, in general, we cannot expect to find optimal decision policies in
polynomial time and there are cases in which deterministic policies are
suboptimal. Then, we demonstrate that, if the cost individuals pay to change
their features satisfies a natural monotonicity assumption, we can narrow down
the search for the optimal policy to a particular family of decision policies
with a set of desirable properties, which allow for a highly effective
polynomial time heuristic search algorithm using dynamic programming. Finally,
under no assumptions on the cost individuals pay to change their features, we
develop an iterative search algorithm that is guaranteed to find locally
optimal decision policies also in polynomial time. Experiments on synthetic and
real credit card data illustrate our theoretical findings and show that the
decision policies found by our algorithms achieve higher utility than those
that do not account for strategic behavior.

研究表明，通过使用数据驱动的预测模型作出的决策越来越多，对于这些决策的影响，对于个人和社会，决策者透明公开的政策也就越来越重要。我们的研究目标是找到在具有策略性投资的情况下，对于实用性最优的决策策略，在我们的研究中，我们首先表征了个体策略性努力投资如何导致特征分布的变化，然后提出了一种适用于多种情况的最优决策策略的搜索方法，并根据合成和现实数据的实验结果，说明了我们算法找到的决策策略比没有考虑策略性的决策策略的实用性要高。