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How can you improve machine learning explainability in operations research?

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Machine learning (ML) is a powerful tool for operations research (OR), which is the science of applying mathematical models and methods to optimize complex decisions and systems. However, ML models often lack explainability, meaning that they are not transparent or interpretable enough to understand how and why they make certain predictions or recommendations. This can limit their trustworthiness, acceptance, and usability in OR applications, especially when they involve human factors, ethical issues, or high-stakes outcomes. In this article, you will learn how to improve ML explainability in OR by following some practical tips and techniques.

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Leandro Ferreira

Senior Data Scientist | Data Science, Machine Learning, MLOps

1 Define your explainability goals

Before you start building or deploying your ML model, you should define your explainability goals and expectations. Explainability is not a one-size-fits-all concept, and it depends on the context, purpose, and audience of your OR problem. For example, you might want to explain your model to different stakeholders, such as decision-makers, customers, regulators, or researchers, who have different levels of expertise, interests, and concerns. You should also consider the trade-offs between explainability and other aspects of your model, such as accuracy, complexity, scalability, or privacy. By defining your explainability goals, you can choose the most suitable ML techniques and evaluation methods for your OR problem.

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Leandro Ferreira

Senior Data Scientist | Data Science, Machine Learning, MLOps
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Define your explainability goals in machine learning by clearly articulating the level of understanding needed for your model's predictions. Determine whether you require a high-level overview or a detailed explanation of how the model makes decisions. Consider factors such as stakeholder requirements, regulatory compliance, and ethical considerations. Clarifying these goals helps tailor your explainability approach to meet specific needs and expectations.

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2 Choose appropriate ML techniques

There are many ML techniques that can enhance explainability in OR, depending on the type and structure of your model. For instance, simple or linear models such as logistic regression or decision trees are easier to understand than complex or nonlinear models like neural networks or random forests. Feature selection or dimensionality reduction methods like principal component analysis or Lasso regression can reduce the number of variables or features in your model and identify the most relevant ones. Regularization or pruning methods like ridge regression or cost-complexity pruning can prevent overfitting and simplify your model by penalizing or removing unnecessary parameters or branches. Additionally, explainable AI (XAI) methods such as local interpretable model-agnostic explanations (LIME) or Shapley values can provide local or global explanations for your model by approximating or decomposing its predictions or outcomes.

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Leandro Ferreira

Senior Data Scientist | Data Science, Machine Learning, MLOps
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Choose the right techniques to explain machine learning (ML) models by considering interpretability needs and data complexity. Options include feature importance, partial dependence plots, individual instance explanations, model-agnostic methods, global model explanations, and rule-based models. Strive for a balance between interpretability and performance while ensuring transparency and trustworthiness in your ML models.

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3 Evaluate and communicate your explanations

After you have chosen your ML techniques, you should evaluate and communicate your explanations to your intended audience. To assess the quality, validity, and usefulness of your explanations, use both quantitative and qualitative methods. Metrics such as accuracy, precision, recall, or fidelity can measure the performance, consistency, or reliability of your explanations in relation to your model or data. Visualizations like plots, charts, or heatmaps can illustrate or highlight the patterns, trends, or relationships in your explanations. Narratives like scenarios, examples, or analogies can convey or contextualize the meaning of your explanations in a simple and engaging way. Following these tips and techniques will help you improve ML explainability in OR and make your models more transparent, interpretable, and trustworthy for OR applications.

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4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Adam J. Hepworth, PhD

Director | Global Military AI Expert | Tech Innovation | Artificial Intelligence | Decision Science | Operations Research | Robotics & Autonomous Systems | Visiting Fellow
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ML model explainability remains a challenging problem in balancing the need for intuitive and interpretable algorithms with more complex and opaque models that offer increased capabilities. By integrating simplified models, engaging with domain experts, and utilising post-hoc explanation tools, OR analysts can ensure that transparent and understandable ML insights inform strategic decisions. This approach enhances trust in the models and ensures that decisions are made with a clear understanding of the underlying data-driven processes. Embedding explainability principles within model development processes ensures that the models developed are not only effective but also transparent and justifiable.

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How can you improve machine learning explainability in operations research?

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1 Define your explainability goals

2 Choose appropriate ML techniques

3 Evaluate and communicate your explanations

4 Here’s what else to consider

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How can you improve machine learning explainability in operations research?

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1 Define your explainability goals

2 Choose appropriate ML techniques

3 Evaluate and communicate your explanations

4 Here’s what else to consider

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