Explainable Machine Learning for Scientific Insights and Discoveries (1905.08883v3)

Abstract: Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively recent development is the uptake of machine learning in the natural sciences, where the major goal is to obtain novel scientific insights and discoveries from observational or simulated data. A prerequisite for obtaining a scientific outcome is domain knowledge, which is needed to gain explainability, but also to enhance scientific consistency. In this article we review explainable machine learning in view of applications in the natural sciences and discuss three core elements which we identified as relevant in this context: transparency, interpretability, and explainability. With respect to these core elements, we provide a survey of recent scientific works that incorporate machine learning and the way that explainable machine learning is used in combination with domain knowledge from the application areas.

Explainable Machine Learning for Scientific Insights and Discoveries

The paper "Explainable Machine Learning for Scientific Insights and Discoveries" by Ribana Roscher et al. investigates the framework of explainable ML within the context of the natural sciences, emphasizing the necessity of transparency, interpretability, and explainability. These elements are crucial for ensuring that the outcomes generated from ML models are scientifically valid and can lead to novel discoveries.

Core Concepts: Transparency, Interpretability, and Explainability

The research highlights three key components:

1. Transparency: This involves a clear understanding of how a ML model is built, including its structure, algorithmic processes, and parameter extraction. A model is considered transparent when its workings are accessible and comprehensible to its designers. Transparency is an essential attribute as it allows for reproducibility and an understanding of the model's decision-making pathways.
2. Interpretability: This refers to the ability to present ML model outputs or components in a comprehensible manner. It ensures the model's decisions can be understood and rationalized by humans, which is particularly important for applications that require critical insights from data.
3. Explainability: Here, the focus is on illuminating the features that contribute to a machine's decision, linking these back to domain knowledge. Explainability not only aids in justifying decisions made by the ML model but also facilitates the discovery of new knowledge.

Integration with Domain Knowledge

The research underscores the importance of integrating domain knowledge into ML models to ensure scientific consistency. Domain knowledge can be embedded in various stages of the ML pipeline, such as feature engineering, model design, and the learning algorithm. This integration not only enhances the explainability of models but also mitigates issues arising from lack of data by embedding known constraints and laws of nature within models.

Survey of ML Applications in Natural Sciences

Roscher et al. provide a comprehensive survey of how ML is utilized in the natural sciences to derive scientific outcomes. The survey distinguishes between various approaches to utilizing ML, emphasizing those that incorporate domain knowledge and those that do not.

• Group 1 Approaches: These methods focus primarily on the prediction of intuitive outcomes without integrating domain knowledge or achieving interpretability. They are basic ML models with limited explainability regarding the underlying scientific processes.
• Group 2 Approaches: These methods incorporate domain knowledge in the design process, enabling models to align with scientific principles, but still primarily focus on outcome prediction. Scientific parameters or properties are used to guide interpretations.
• Group 3 Approaches: Here, interpretation tools like attention maps or feature importance plots enhance the understanding of the model's decisions, offering a layer of scientific explainability.
• Group 4 Approaches: These focus on the model's internal structure itself, designed to be interpretable and scientifically explainable. These methods aim for transparent and consistent scientific insights, aligning closely with the goals of scientific discovery and understanding.

Implications and Future Directions

The paper suggests several implications for the use of explainable ML in scientific research.

1. Scientific Consistency: The integration of domain knowledge enhances the scientific validity of model outcomes, instilling confidence in the results.
2. Discovery and Insight: Explainable models pave the way for new scientific insights, offering researchers the means to explore complex datasets and derive meaningful conclusions.
3. Future Developments: The paper indicates future research efforts should focus on refining ML models to improve both their interpretability and explainability. Furthermore, mechanisms for embedding causal inference within ML models are hinted at as a promising area for exploration.

In conclusion, Roscher et al. articulate a structured approach to embedding ML within scientific inquiry, presenting pathways for models not just to make accurate predictions, but also to be understood and trusted by domain experts. Their work offers a detailed roadmap for researchers looking to apply ML to complex scientific problems, ensuring outcomes that are not only technically accurate but scientifically sound.