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How can you measure the robustness of a machine learning model?

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Machine learning models are increasingly used in various applications, such as image recognition, natural language processing, and recommendation systems. However, how can you ensure that your model is robust, meaning that it can handle different types of data, noise, and uncertainty, without compromising its performance and reliability? In this article, you will learn about some metrics and methods that can help you measure the robustness of a machine learning model.

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Akshat Gupta

machine learning engineer | gen-ai | diffusion models | llms
Ainulla Khan

Applied Researcher | Artificial Intelligence Laboratory | Fujitsu Research | Masters at Indian Institute of Technology…
Serena Lazzaro

1 Accuracy and error rate

One of the most basic and common metrics to evaluate a machine learning model is accuracy, which is the proportion of correct predictions over the total number of predictions. Accuracy can be calculated for different subsets of data, such as training, validation, and test sets, to assess how well the model fits and generalizes. However, accuracy alone is not enough to measure robustness, as it does not account for the complexity and diversity of the data, the distribution of the classes, and the types and costs of errors. Therefore, you should also consider the error rate, which is the proportion of incorrect predictions over the total number of predictions, and its components, such as false positives and false negatives, which can have different implications depending on the context.

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2 Sensitivity and specificity

Another pair of metrics that can help you measure the robustness of a machine learning model is sensitivity and specificity, which are especially useful for binary classification problems, where the model has to decide between two possible outcomes, such as spam or not spam, positive or negative, etc. Sensitivity, also known as recall or true positive rate, is the proportion of positive cases that are correctly identified by the model. Specificity, also known as true negative rate, is the proportion of negative cases that are correctly rejected by the model. Sensitivity and specificity can be used to analyze how the model handles different types of data, such as balanced or imbalanced, noisy or clean, etc., and how it trades off between precision and recall.

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Ainulla Khan

Applied Researcher | Artificial Intelligence Laboratory | Fujitsu Research | Masters at Indian Institute of Technology Madras
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While during initial stage it could be fine if the sensitivity is defined as mentioned in the article. But considering a scenario where we are also concerned about the final deployment it is also worthwhile to define sensitivity with respect to the volume of data which is being fed for inferencing. Performance issues would occur under either of situations consisting of high volume or low volume of data.

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Serena Lazzaro
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Without a doubt, I agree that sensitivity comes before robustness when we talk about algorithms (AI). However, to identify the WHY, the risks must be clear (otherwise it is not an analytical scope). After defining sensitivity with the use of a limited number of test samples, we need a training sample size that is "representative" of the Yes/No belonging to the clusters we are focusing on. Otherwise, we cannot evaluate the algorithm's robustness, which affects resilience to attack and security, fullback plan and general safety, reliability and reproducibility.

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3 Robustness curves

A more visual and comprehensive way to measure the robustness of a machine learning model is to use robustness curves, which are plots that show how the model's performance changes as a function of some parameter that affects the data quality or quantity. For example, you can plot the accuracy or error rate of the model against the noise level, the missing values, the outliers, the data size, the feature selection, etc., and compare how different models behave under different scenarios. Robustness curves can help you identify the strengths and weaknesses of your model, the optimal and worst-case scenarios, and the trade-offs and thresholds that affect your model's robustness.

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4 Adversarial testing

Another method that can help you measure the robustness of a machine learning model is adversarial testing, which is a technique that involves creating and applying malicious or perturbed inputs to the model, with the intention of fooling or degrading its performance. Adversarial testing can help you assess how resilient your model is to attacks, such as evasion, poisoning, extraction, etc., and how it responds to unexpected or extreme situations. Adversarial testing can also help you improve your model's robustness, by exposing its vulnerabilities, generating feedback, and enhancing its learning and adaptation capabilities.

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Akshat Gupta

machine learning engineer | gen-ai | diffusion models | llms
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There are various adversarial methods: 1. Fast Gradient Sign Method(FGSM): Developed by Ian Goodfellow et al. in 2014, it is a key technique in adversarial machine learning. It creates adversarial examples by adding small perturbations to input data, using the neural network's loss gradients. This method aims to lead AI models to misclassify inputs. FGSM is crucial for exposing vulnerabilities in machine learning models and enhancing cybersecurity, but its simplicity and one-step approach can limit its effectiveness against robust defences. Nonetheless, FGSM remains a vital tool in advancing AI security and understanding adversarial threats.

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5 Explainability and interpretability

Finally, one of the most important aspects of measuring the robustness of a machine learning model is explainability and interpretability, which are the abilities to understand and communicate how the model works, why it makes certain decisions, and what are the factors and consequences of its predictions. Explainability and interpretability can help you verify and validate your model's logic, assumptions, and outputs, as well as identify and correct any errors, biases, or inconsistencies that may affect its robustness. Explainability and interpretability can also help you build trust and confidence in your model, by making it more transparent, accountable, and ethical.

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Akshat Gupta

machine learning engineer | gen-ai | diffusion models | llms
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Explainability and interpretability are vital for the robustness of machine learning models, enabling an understanding of how these models make decisions and what influences their predictions. They are crucial for validating a model's logic, identifying and correcting biases, and ensuring models are ethical and align with societal values. Additionally, they build trust, especially in sensitive fields like healthcare and finance, and are essential for meeting regulatory requirements like GDPR. Explainability and interpretability not only enhance model robustness but also foster collaboration and responsible AI deployment, making them indispensable in the ethical and sustainable advancement of AI technology.

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6 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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Ainulla Khan

Applied Researcher | Artificial Intelligence Laboratory | Fujitsu Research | Masters at Indian Institute of Technology Madras
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It could also be beneficial if we define what kind of robustness is/are being evaluated and the design of tests for handling the problem scenarios more systematically. For example, under datasets we have in-distribution and out-of-distribution, the test datasets developed for each case would vary. So here testing the model for various generalization scenarios would help us to evaluate our model more precisely.

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How can you measure the robustness of a machine learning model?

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4

5

6

1 Accuracy and error rate

2 Sensitivity and specificity

3 Robustness curves

4 Adversarial testing

5 Explainability and interpretability

6 Here’s what else to consider

Software Testing

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How can you measure the robustness of a machine learning model?

1

2

3

4

5

6

1 Accuracy and error rate

2 Sensitivity and specificity

3 Robustness curves

4 Adversarial testing

5 Explainability and interpretability

6 Here’s what else to consider

Software Testing

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