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

Explainable artificial intelligence is a research field that tries to provide
more transparency for autonomous intelligent systems. Explainability has been
used, particularly in reinforcement learning and robotic scenarios, to better
understand the robot decision-making process. Previous work, however, has been
widely focused on providing technical explanations that can be better
understood by AI practitioners than non-expert end-users. In this work, we make
use of human-like explanations built from the probability of success to
complete the goal that an autonomous robot shows after performing an action.
These explanations are intended to be understood by people who have no or very
little experience with artificial intelligence methods. This paper presents a
user trial to study whether these explanations that focus on the probability an
action has of succeeding in its goal constitute a suitable explanation for
non-expert end-users. The results obtained show that non-expert participants
rate robot explanations that focus on the probability of success higher and
with less variance than technical explanations generated from Q-values, and
also favor counterfactual explanations over standalone explanations.

本文探讨了透明度逐渐成为自主智能系统的必要品质，提供了人性化的基于成功概率的解释方法，并通过用户调查证明该种解释对于非专业人士更易理解和接受。