Evaluating the potential of a prospective candidate is a common task in
multiple decision-making processes in different industries. We refer to a
prospect as something or someone that could potentially produce positive
results in a given context, e.g., an area where an oil company could find oil,
a compound that, when synthesized, results in a material with required
properties, and so on. In many contexts, assessing the Probability of Success
(PoS) of prospects heavily depends on experts' knowledge, often leading to
biased and inconsistent assessments. We have developed the framework named KARA
(Knowledge-augmented Risk Assessment) to address these issues. It combines
multiple AI techniques that consider SMEs (Subject Matter Experts) feedback on
top of a structured domain knowledge-base to support risk assessment processes
of prospect candidates in knowledge-intensive contexts.

该研究提出了一个名为 KARA 的框架，它结合了多种人工智能技术和领域知识库，并考虑到专家反馈，以支持知识密集型背景下对潜在候选人的风险评估过程。

知识增强型风险评估（KaRA）：一种支持知识密集型候选者风险评估的混合智能框架

Knowledge-augmented Risk Assessment (KaRA): a hybrid-intelligence framework for supporting knowledge-intensive risk assessment of prospect candidates

Amazon Customer Service provides real-time support for millions of customer
contacts every year. While bot-resolver helps automate some traffic, we still
see high demand for human agents, also called subject matter experts (SMEs).
Customers outreach with questions in different domains (return policy, device
troubleshooting, etc.). Depending on their training, not all SMEs are eligible
to handle all contacts. Routing contacts to eligible SMEs turns out to be a
non-trivial problem because SMEs' domain eligibility is subject to training
quality and can change over time. To optimally recommend SMEs while
simultaneously learning the true eligibility status, we propose to formulate
the routing problem with a nonparametric contextual bandit algorithm (K-Boot)
plus an eligibility control (EC) algorithm. K-Boot models reward with a kernel
smoother on similar past samples selected by $k$-NN, and Bootstrap Thompson
Sampling for exploration. EC filters arms (SMEs) by the initially
system-claimed eligibility and dynamically validates the reliability of this
information. The proposed K-Boot is a general bandit algorithm, and EC is
applicable to other bandits. Our simulation studies show that K-Boot performs
on par with state-of-the-art Bandit models, and EC boosts K-Boot performance
when stochastic eligibility signal exists.

本文提出了一种非参数情境赌博算法 (K-Boot) 和资格控制 (EC) 算法的路由问题，以优化推荐主题专家 (SME) 的效率，并同时学习真实的 SME 资格情况，实验结果表明，当存在随机资格信号时，EC 可以提高 K-Boot 的性能。

具有分支资格控制的非参数背景赌博机在客户服务路由中的应用

A Nonparametric Contextual Bandit with Arm-level Eligibility Control for  Customer Service Routing

Across a multitude of work environments, expert knowledge is imperative for
humans to conduct tasks with high performance and ensure business success.
These humans possess task-specific expert knowledge (TSEK) and hence, represent
subject matter experts (SMEs). However, not only demographic changes but also
personnel downsizing strategies lead and will continue to lead to departures of
SMEs within organizations, which constitutes the challenge of how to retain
that expert knowledge and train novices to keep the competitive advantage
elicited by that expert knowledge. SMEs training novices is time- and
cost-intensive, which intensifies the need for alternatives. Human-AI
collaboration (HAIC) poses a way out of this dilemma, facilitating alternatives
to preserve expert knowledge and teach it to novices for tasks conducted by
SMEs beforehand. In this workshop paper, we (1) propose a framework on how HAIC
can be utilized to train novices on particular tasks, (2) illustrate the role
of explicit and tacit knowledge in this training process via HAIC, and (3)
outline a preliminary experiment design to assess the ability of AI systems in
HAIC to act as a trainer to transfer TSEK to novices who do not possess prior
TSEK.

本文旨在提出一个基于人工智能（AI）与人类的协同学习模型，以有效培训新手完成由专家完成的任务，从而保留专家知识和传授给新手，解决专家流失、培训时间和成本高昂的问题。