Interpretability of Machine Learning Solutions in Industrial Decision Engineering

Kolyshkina, Inna; Simoff, Simeon

doi:10.1007/978-981-15-1699-3_13

Cited by 9 publications

(20 citation statements)

References 11 publications

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“…The most typical changes, related to data availability, quality, and analysis findings, occur mostly at stages 2-4, as shown in Figure 1. This is illustrated in our case study and was discussed in detail in the study by Kolyshkina and Simoff (2019). Less often changes occur at stages 5-7 in Figure 1.…”

Section: Crisp-ml Methodology-toward Interpretability-centric Creation Of ML Solutionssupporting

confidence: 62%

“…In order to standardize the expectations for interpretability, some of these requirements have been addressed in the recently proposed CRISP-ML methodology (Kolyshkina and Simoff, 2019 ). In section 3, we will briefly discuss the major concepts differentiating CRISP-ML methodology.…”

Section: Challenges and Opportunities In Creating Methodologies Which Consistently Embed Interpretabilitymentioning

confidence: 99%

“…In section 3, we will briefly discuss the major concepts differentiating CRISP-ML methodology. The CRISP-ML approach includes the concepts of necessary level of interpretability (NLI) and interpretability matrix (IM), described in detail by Kolyshkina and Simoff ( 2019 ), and therefore aligns well with the view of health researchers that the choice of interpretable models depends upon the application and use case for which explanations are required (Ahmad et al, 2018 ). To illustrate that, in section 4, we present a use case in the public health field that illustrates the typical challenges met and the ways CRISP-ML helped to address and resolve them.…”

Section: Challenges and Opportunities In Creating Methodologies Which Consistently Embed Interpretabilitymentioning

confidence: 99%

“…The CRISP-ML methodology (Kolyshkina and Simoff, 2019 ) of building interpretability of an ML solution is based on revision and update of CRISP-DM to address the opportunities discussed in section 2. It follows the CRISP-DM approach in terms of being industry-, tool-, and application-neutral.…”

Section: Crisp-ml Methodology—toward Interpretability-centric Creation Of ML Solutionsmentioning

confidence: 99%

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Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach

Kolyshkina¹,

Simoff

2021

Front. Big Data

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Public healthcare has a history of cautious adoption for artificial intelligence (AI) systems. The rapid growth of data collection and linking capabilities combined with the increasing diversity of the data-driven AI techniques, including machine learning (ML), has brought both ubiquitous opportunities for data analytics projects and increased demands for the regulation and accountability of the outcomes of these projects. As a result, the area of interpretability and explainability of ML is gaining significant research momentum. While there has been some progress in the development of ML methods, the methodological side has shown limited progress. This limits the practicality of using ML in the health domain: the issues with explaining the outcomes of ML algorithms to medical practitioners and policy makers in public health has been a recognized obstacle to the broader adoption of data science approaches in this domain. This study builds on the earlier work which introduced CRISP-ML, a methodology that determines the interpretability level required by stakeholders for a successful real-world solution and then helps in achieving it. CRISP-ML was built on the strengths of CRISP-DM, addressing the gaps in handling interpretability. Its application in the Public Healthcare sector follows its successful deployment in a number of recent real-world projects across several industries and fields, including credit risk, insurance, utilities, and sport. This study elaborates on the CRISP-ML methodology on the determination, measurement, and achievement of the necessary level of interpretability of ML solutions in the Public Healthcare sector. It demonstrates how CRISP-ML addressed the problems with data diversity, the unstructured nature of data, and relatively low linkage between diverse data sets in the healthcare domain. The characteristics of the case study, used in the study, are typical for healthcare data, and CRISP-ML managed to deliver on these issues, ensuring the required level of interpretability of the ML solutions discussed in the project. The approach used ensured that interpretability requirements were met, taking into account public healthcare specifics, regulatory requirements, project stakeholders, project objectives, and data characteristics. The study concludes with the three main directions for the development of the presented cross-industry standard process.

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Section: Crisp-ml Methodology-toward Interpretability-centric Creation Of ML Solutionssupporting

confidence: 62%

Section: Challenges and Opportunities In Creating Methodologies Which Consistently Embed Interpretabilitymentioning

confidence: 99%

Section: Challenges and Opportunities In Creating Methodologies Which Consistently Embed Interpretabilitymentioning

confidence: 99%

Section: Crisp-ml Methodology—toward Interpretability-centric Creation Of ML Solutionsmentioning

confidence: 99%

See 2 more Smart Citations

Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach

Kolyshkina¹,

Simoff

2021

Front. Big Data

Self Cite

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“…State of the art machine learning techniques rely on data abstractions which reduce interpretability. In recent years, there has been an increased demand of interpretable models within different industrial sectors [12]. By using association rules, the acoustic patterns which signal future faults can be easily retrieved.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection and event mining in cold forming manufacturing processes

Avendano

Caljouw

Deschrijver

et al. 2020

Int J Adv Manuf Technol

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Predictive maintenance is one of the main goals within the Industry 4.0 trend. Advances in data-driven techniques offer new opportunities in terms of cost reduction, improved quality control, and increased work safety. This work brings data-driven techniques for two predictive maintenance tasks: anomaly detection and event prediction, applied in the real-world use case of a cold forming manufacturing line for consumer lifestyle products by using acoustic emissions sensors in proximity of the dies of the press module. The proposed models are robust and able to cope with problems such as noise, missing values, and irregular sampling. The detected anomalies are investigated by experts and confirmed to correspond to deviations in the normal operation of the machine. Moreover, we are able to find patterns which are related to the events of interest.

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CrabNet for Explainable Deep Learning in Materials Science: Bridging the Gap Between Academia and Industry

Wang

Mahmoud

Czasny

et al. 2022

Integr Mater Manuf Innov

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Despite recent breakthroughs in deep learning for materials informatics, there exists a disparity between their popularity in academic research and their limited adoption in the industry. A significant contributor to this “interpretability-adoption gap” is the prevalence of black-box models and the lack of built-in methods for model interpretation. While established methods for evaluating model performance exist, an intuitive understanding of the modeling and decision-making processes in models is nonetheless desired in many cases. In this work, we demonstrate several ways of incorporating model interpretability to the structure-agnostic Compositionally Restricted Attention-Based network, CrabNet. We show that CrabNet learns meaningful, material property-specific element representations based solely on the data with no additional supervision. These element representations can then be used to explore element identity, similarity, behavior, and interactions within different chemical environments. Chemical compounds can also be uniquely represented and examined to reveal clear structures and trends within the chemical space. Additionally, visualizations of the attention mechanism can be used in conjunction to further understand the modeling process, identify potential modeling or dataset errors, and hint at further chemical insights leading to a better understanding of the phenomena governing material properties. We feel confident that the interpretability methods introduced in this work for CrabNet will be of keen interest to materials informatics researchers as well as industrial practitioners alike.

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Interpretability of Machine Learning Solutions in Industrial Decision Engineering

Cited by 9 publications

References 11 publications

Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach

Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach

Anomaly detection and event mining in cold forming manufacturing processes

CrabNet for Explainable Deep Learning in Materials Science: Bridging the Gap Between Academia and Industry

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