Human-AI collaboration for decision-making strives to achieve team
performance that exceeds the performance of humans or AI alone. However, many
factors can impact success of Human-AI teams, including a user's domain
expertise, mental models of an AI system, trust in recommendations, and more.
This work examines users' interaction with three simulated algorithmic models,
all with similar accuracy but different tuning on their true positive and true
negative rates. Our study examined user performance in a non-trivial blood
vessel labeling task where participants indicated whether a given blood vessel
was flowing or stalled.
Our results show that while recommendations from an AI-Assistant can aid user
decision making, factors such as users' baseline performance relative to the AI
and complementary tuning of AI error types significantly impact overall team
performance. Novice users improved, but not to the accuracy level of the AI.
Highly proficient users were generally able to discern when they should follow
the AI recommendation and typically maintained or improved their performance.
Mid-performers, who had a similar level of accuracy to the AI, were most
variable in terms of whether the AI recommendations helped or hurt their
performance. In addition, we found that users' perception of the AI's
performance relative on their own also had a significant impact on whether
their accuracy improved when given AI recommendations. This work provides
insights on the complexity of factors related to Human-AI collaboration and
provides recommendations on how to develop human-centered AI algorithms to
complement users in decision-making tasks.

研究了用户与三个模拟算法模型的交互，发现较低水平的用户虽然能从 AI 建议中受益，但是他们的决策水平却没能达到 AI 的精度，高水平用户则通常能辨别何时应该遵循 AI 建议并保持或提高其性能，而中等水平者则最不稳定，AI 建议会对其性能产生帮助或伤害，此外，用户对 AI 性能的感知也对决策的精度影响非常大。该研究提供了关于人工智能协作相关复杂因素的见解，并提出了如何开发以人为本的人工智能算法以辅助用户在决策任务中的建议。

推进人工智能和人类互补性：用户专业知识和算法调优对共同决策的影响

Advancing Human-AI Complementarity: The Impact of User Expertise and Algorithmic Tuning on Joint Decision Making

A key challenge facing natural language interfaces is enabling users to
understand the capabilities of the underlying system. We propose a novel
approach for generating explanations of a natural language interface based on
semantic parsing. We focus on counterfactual explanations, which are post-hoc
explanations that describe to the user how they could have minimally modified
their utterance to achieve their desired goal. In particular, the user provides
an utterance along with a demonstration of their desired goal; then, our
algorithm synthesizes a paraphrase of their utterance that is guaranteed to
achieve their goal. In two user studies, we demonstrate that our approach
substantially improves user performance, and that it generates explanations
that more closely match the user's intent compared to two ablations.

本文提出了一种基于语义分析的新方法来生成自然语言界面的解释，重点是提供后续解释，描述如何最小修改发言以实现用户的目标。在两项用户研究中，我们证明了我们的方法可以显著提高用户的性能，并且与两种去除实验相比，可以生成更符合用户意图的解释。

自然语言界面的反事实解释

Counterfactual Explanations for Natural Language Interfaces

Explainable machine learning and artificial intelligence models have been
used to justify a model's decision-making process. This added transparency aims
to help improve user performance and understanding of the underlying model.
However, in practice, explainable systems face many open questions and
challenges. Specifically, designers might reduce the complexity of deep
learning models in order to provide interpretability. The explanations
generated by these simplified models, however, might not accurately justify and
be truthful to the model. This can further add confusion to the users as they
might not find the explanations meaningful with respect to the model
predictions. Understanding how these explanations affect user behavior is an
ongoing challenge. In this paper, we explore how explanation veracity affects
user performance and agreement in intelligent systems. Through a controlled
user study with an explainable activity recognition system, we compare
variations in explanation veracity for a video review and querying task. The
results suggest that low veracity explanations significantly decrease user
performance and agreement compared to both accurate explanations and a system
without explanations. These findings demonstrate the importance of accurate and
understandable explanations and caution that poor explanations can sometimes be
worse than no explanations with respect to their effect on user performance and
reliance on an AI system.

本文研究了可解释人工智能模型对用户行为和性能的影响，在一项对说明性活动识别系统的控制用户研究中发现，低真实性的解释与准确的解释和没有解释的系统相比，会显着降低用户的性能和协议。研究结果表明，精确而易理解的解释至关重要，而糟糕的解释有时比没有解释更糟。