Prior work has established the importance of integrating AI ethics topics
into computer and data sciences curricula. We provide evidence suggesting that
one of the critical objectives of AI Ethics education must be to raise
awareness of AI harms. While there are various sources to learn about such
harms, The AI Incident Database (AIID) is one of the few attempts at offering a
relatively comprehensive database indexing prior instances of harms or near
harms stemming from the deployment of AI technologies in the real world. This
study assesses the effectiveness of AIID as an educational tool to raise
awareness regarding the prevalence and severity of AI harms in socially
high-stakes domains. We present findings obtained through a classroom study
conducted at an R1 institution as part of a course focused on the societal and
ethical considerations around AI and ML. Our qualitative findings characterize
students' initial perceptions of core topics in AI ethics and their desire to
close the educational gap between their technical skills and their ability to
think systematically about ethical and societal aspects of their work. We find
that interacting with the database helps students better understand the
magnitude and severity of AI harms and instills in them a sense of urgency
around (a) designing functional and safe AI and (b) strengthening governance
and accountability mechanisms. Finally, we compile students' feedback about the
tool and our class activity into actionable recommendations for the database
development team and the broader community to improve awareness of AI harms in
AI ethics education.

AI 伦理教育的主要目标之一是提高人们对人工智能危害的认识，而本研究评估了 AI Incident Database (AIID) 作为提高社会高风险领域中人工智能危害的认知度的教育工具的有效性，发现使用该数据库可以帮助学生更好地理解人工智能危害的严重性和紧迫性，并提出了改进教育工具的建议。

以 AI 事故数据库作为提高对 AI 危害意识的教育工具：对其效果、局限性和未来改进的课堂探索

The AI Incident Database as an Educational Tool to Raise Awareness of AI  Harms: A Classroom Exploration of Efficacy, Limitations, & Future  Improvements

When we face patients arriving to a hospital suffering from the effects of
some illness, one of the main problems we can encounter is evaluating whether
or not said patients are going to require intensive care in the near future.
This intensive care requires allotting valuable and scarce resources, and
knowing beforehand the severity of a patients illness can improve both its
treatment and the organization of resources. We illustrate this issue in a
dataset consistent of Spanish COVID-19 patients from the sixth epidemic wave
where we label patients as critical when they either had to enter the intensive
care unit or passed away. We then combine the use of dynamic Bayesian networks,
to forecast the vital signs and the blood analysis results of patients over the
next 40 hours, and neural networks, to evaluate the severity of a patients
disease in that interval of time. Our empirical results show that the
transposition of the current state of a patient to future values with the DBN
for its subsequent use in classification obtains better the accuracy and g-mean
score than a direct application with a classifier.

本文使用动态贝叶斯网络和神经网络结合的方法，预测患者 40 小时内的生命体征和血液分析结果，并分类评估其疾病严重程度，以便将更有限的资源分配给需要重症监护的患者。

利用动态贝叶斯网络和神经网络对新冠病毒患者的病情严重程度进行分类

Classifying the evolution of COVID-19 severity on patients with combined dynamic Bayesian networks and neural networks

In clinical conversational applications, extracted entities tend to capture
the main subject of a patient's complaint, namely symptoms or diseases.
However, they mostly fail to recognize the characterizations of a complaint
such as the time, the onset, and the severity. For example, if the input is "I
have a headache and it is extreme", state-of-the-art models only recognize the
main symptom entity - headache, but ignore the severity factor of "extreme",
that characterizes headache. In this paper, we design a two-stage approach to
detect the characterizations of entities like symptoms presented by general
users in contexts where they would describe their symptoms to a clinician. We
use Word2Vec and BERT to encode clinical text given by the patients. We
transform the output and re-frame the task as multi-label classification
problem. Finally, we combine the processed encodings with the Linear
Discriminant Analysis (LDA) algorithm to classify the characterizations of the
main entity. Experimental results demonstrate that our method achieves 40-50%
improvement on the accuracy over the state-of-the-art models.

本文介绍了一种基于 Word2Vec 和 BERT 进行预处理及 LDA 算法进行分类的方法，用于通过临床对话应用程序检测广泛用户提出的主体实体的特征，包括症状、发作时间和严重性等，并在多标签分类问题中对处理后的编码进行组合，实现了 40-50% 的诊断准确度改进。