Interpretable machine learning has exploded as an area of interest over the
last decade, sparked by the rise of increasingly large datasets and deep neural
networks. Simultaneously, large language models (LLMs) have demonstrated
remarkable capabilities across a wide array of tasks, offering a chance to
rethink opportunities in interpretable machine learning. Notably, the
capability to explain in natural language allows LLMs to expand the scale and
complexity of patterns that can be given to a human. However, these new
capabilities raise new challenges, such as hallucinated explanations and
immense computational costs.
In this position paper, we start by reviewing existing methods to evaluate
the emerging field of LLM interpretation (both interpreting LLMs and using LLMs
for explanation). We contend that, despite their limitations, LLMs hold the
opportunity to redefine interpretability with a more ambitious scope across
many applications, including in auditing LLMs themselves. We highlight two
emerging research priorities for LLM interpretation: using LLMs to directly
analyze new datasets and to generate interactive explanations.

可解释机器学习与大型语言模型相结合，评估解释方法并提出使用语言模型分析数据集和生成交互式解释的两个研究重点。

大语言模型时代的可解释性重新思考

Rethinking Interpretability in the Era of Large Language Models

Explainable recommender systems (RS) have traditionally followed a
one-size-fits-all approach, delivering the same explanation level of detail to
each user, without considering their individual needs and goals. Further,
explanations in RS have so far been presented mostly in a static and
non-interactive manner. To fill these research gaps, we aim in this paper to
adopt a user-centered, interactive explanation model that provides explanations
with different levels of detail and empowers users to interact with, control,
and personalize the explanations based on their needs and preferences. We
followed a user-centered approach to design interactive explanations with three
levels of detail (basic, intermediate, and advanced) and implemented them in
the transparent Recommendation and Interest Modeling Application (RIMA). We
conducted a qualitative user study (N=14) to investigate the impact of
providing interactive explanations with varying level of details on the users'
perception of the explainable RS. Our study showed qualitative evidence that
fostering interaction and giving users control in deciding which explanation
they would like to see can meet the demands of users with different needs,
preferences, and goals, and consequently can have positive effects on different
crucial aspects in explainable recommendation, including transparency, trust,
satisfaction, and user experience.

通过用户中心、交互式的多级解释模型，提供不同层次的解释，让用户可以根据需求和偏好自定义解释，研究表明这种方法可以提高透明度、信任感、满意度和用户体验。

一个可解释的科学文献推荐系统中不同细节层次的交互式解释

Interactive Explanation with Varying Level of Details in an Explainable  Scientific Literature Recommender System

As the field of explainable AI (XAI) is maturing, calls for interactive
explanations for (the outputs of) AI models are growing, but the
state-of-the-art predominantly focuses on static explanations. In this paper,
we focus instead on interactive explanations framed as conflict resolution
between agents (i.e. AI models and/or humans) by leveraging on computational
argumentation. Specifically, we define Argumentative eXchanges (AXs) for
dynamically sharing, in multi-agent systems, information harboured in
individual agents' quantitative bipolar argumentation frameworks towards
resolving conflicts amongst the agents. We then deploy AXs in the XAI setting
in which a machine and a human interact about the machine's predictions. We
identify and assess several theoretical properties characterising AXs that are
suitable for XAI. Finally, we instantiate AXs for XAI by defining various agent
behaviours, e.g. capturing counterfactual patterns of reasoning in machines and
highlighting the effects of cognitive biases in humans. We show experimentally
(in a simulated environment) the comparative advantages of these behaviours in
terms of conflict resolution, and show that the strongest argument may not
always be the most effective.

本研究针对可解释人工智能（XAI），提出了一种计算论证的交互式解释方法（AXs），以解决模型输出与人类决策之间的不一致性。实验结果表明，AXs 在 XAI 中可以有效地解决不当行为和决策冲突。

通过辩论交流解决冲突的交互式解释

Interactive Explanations by Conflict Resolution via Argumentative Exchanges

Although AI holds promise for improving human decision making in societally
critical domains, it remains an open question how human-AI teams can reliably
outperform AI alone and human alone in challenging prediction tasks (also known
as complementary performance). We explore two directions to understand the gaps
in achieving complementary performance. First, we argue that the typical
experimental setup limits the potential of human-AI teams. To account for lower
AI performance out-of-distribution than in-distribution because of distribution
shift, we design experiments with different distribution types and investigate
human performance for both in-distribution and out-of-distribution examples.
Second, we develop novel interfaces to support interactive explanations so that
humans can actively engage with AI assistance. Using virtual pilot studies and
large-scale randomized experiments across three tasks, we demonstrate a clear
difference between in-distribution and out-of-distribution, and observe mixed
results for interactive explanations: while interactive explanations improve
human perception of AI assistance's usefulness, they may reinforce human biases
and lead to limited performance improvement. Overall, our work points out
critical challenges and future directions towards enhancing human performance
with AI assistance.

本文研究了在挑战性预测任务中人工智能（AI）和人类单独执行和协同执行的准确性，并提出了两种方法来理解实现互补性性能的差距，即设计具有不同分布类型的实验以考虑 AI 性能的分布转移，以及开发支持交互式解释的新界面。在多项实验结果中，研究者发现了关键的挑战和未来方向，以增强人类在 AI 辅助下的表现。