Interpretable Machine Learning for life science data

最近对人工智能（AI）应用的兴趣激增，为加拿大工业带来了几个新的机会。虽然最近在许多关键部门出现了一些成功的应用，但在其他部门，如生命科学和生物信息学，仍有重要的障碍有待解决，在这些部门，创新可能会产生重大的社会和伦理影响。其中一个问题是可解释性。事实上，大多数人工智能应用程序都是用机器学习（ML）范式开发的，它包括设计算法来自己学习任务，而不是明确地编程解决方案。对于更困难的任务，我们通常赋予学习算法更大的能力将数据转换为合适的表示。尽管这允许执行任务，但它是有代价的：在容量和模型的有效可解释性之间存在反比关系，这可能使其成为人类难以理解的不透明模型。

The recent surge of interest in artificial intelligence (AI) applications has brought to light several new opportunities for the Canadian industries. Although several successful applications have recently emerged in many key sectors, important hindrances remain to be addressed in other sectors such as life science and bioinformatics, where innovations may have significant social and ethical impacts. One such issue is interpretability. Indeed, most AI applications are developed with the Machine Learning (ML) paradigm, which consists in designing algorithms to learn a task by themselves rather than explicitly programming the solution. For harder tasks, we often grant the learning algorithm with greater capacity of transforming the data to a suitable representation. Although this permits the task to be performed, it comes at a cost: there exists an inverse relationship between the capacity and the effective interpretability of the model, potentially making it an opaque model difficult to understand by a human. Motivations for interpretable ML methods are numerous: build trust by making the model challengeable, contribute to acceptance by explaining decisions, serve as a diagnostic tool to drive future data collections, help in certification processes to uncover corner cases, assess the presence of undesired biases in the model to ensure fairness and reveal obfuscated decision mechanisms for knowledge discovery. All these aspects are crucial and are part of the long-term goal of the research program to reach a unified methodology and general understanding of ML interpretability. The field of bioinformatics and life science in general offers great potential for research on ML interpretability. In fact, the field is facing some specific realities that make the problems more difficult than in other areas where ML is already fruitful. For instance, the amount of available data can be limited and costly to gather; the data might originate from different labs, making the fusion of multiple dataset a nontrivial task; over- and under-representation of some populations can lead to unacceptable biases that must be identified and compensated; and potential discoveries made by ML algorithms must be humanly intelligible to be used. The present project proposes to address these issues by developing specialized ML methods by covering the bias, diagnostic and knowledge discovery aspects of interpretability. It is expected that advances in ML interpretability will contribute to the general acceptance and trust of using AI in life science. The initial impact will be to promote the use of ML to accelerate life science research by making it more cost effective and scalable. The improved practices will eventually foster the development of AI applications that will be beneficial to Canadians once introduced in the healthcare systems.