The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

Recent advances in deep learning have led to the development of models approaching human level of accuracy. However, healthcare remains an area lacking in widespread adoption. The safety-critical nature of healthcare results in a natural reticence to put these black-box deep learning models into practice. In this paper, we explore interpretable methods for a clinical decision support system, sleep staging, based on physiological signals such as EEG, EOG, and EMG. A recent work has shown sleep staging using simple models and an exhaustive set of features can perform nearly as well as deep learning approaches but only for certain datasets. Moreover, the utility of these features from a clinical standpoint is unclear. On the other hand, the proposed framework, NormIntSleep shows that by representing deep learning embeddings using normalized features, great performance can be obtained across different datasets. NormIntSleep performs 4.5% better than the exhaustive feature-based approach and 1.5% better than other representation learning approaches. An empirical comparison between the utility of the interpretations of these models highlights the improved alignment with clinical expectations when performance is traded-off slightly.

最近基于深度学习的临床决策支持系统的准确性是有希望的。但是，缺乏模型可解释性仍然是医疗保健中人工智能广泛采用的障碍。使用睡眠作为案例研究，我们提出了一种可推广的方法，将临床解释性与黑盒深度学习得出的高精度相结合。多聚词（PSG）的临床医生确定的睡眠阶段仍然是评估睡眠质量的金标准。但是，专家的PSG手册注释既昂贵又过时。我们建议使用嵌入式，规则和功能来读取PSG的农奴，可解释的睡眠分期。农奴通过从AASM手册中得出的有意义的特征来解释分类的睡眠阶段，用于睡眠和相关事件的评分。在农奴中，从卷积和复发性神经网络的混合体获得的嵌入被转移到可解释的特征空间。这些代表性的可解释功能用于训练简单的模型，例如浅决策树进行分类。模型结果将在两个公开可用的数据集上进行验证。农奴超过了可解释的睡眠分期的当前最新时间。 Serf使用梯度增压树作为分类器，在当前最新的黑盒模型的2％以内，获得了0.766 $ \ kappa $和0.870 AUC-ROC。