Machine Learning Blackbox Algorithms in Healthcare: How to Mitigate them
Authors:Authors: Amina Khalpey, PhD, Brynne Rozell BS, Zain Khalpey, MD, PhD, FACS
Machine Learning (ML) is a type of AI that is focused on the development of computer programs that can access data and use it to learn for themselves. In ML, algorithms are used to learn from data without being explicitly programmed. This allows machines to learn from data and create their own models and algorithms to identify patterns and make decisions. ML is used in applications such as natural language processing, robotics, and computer vision. Machine learning algorithms have become an integral part of healthcare, ranging from clinical decision-making to personalized medicine. However, many of these algorithms, particularly those using black box techniques, are often difficult to interpret, making it challenging to understand how they arrive at their predictions. Black box algorithms have been shown to be potentially dangerous in healthcare because they can lead to biased and inaccurate predictions that may adversely affect patient outcomes. Therefore, it is crucial to understand the risks associated with black box algorithms and how they can be mitigated to improve their safety and efficacy.
What Are Black Box Algorithms?
A black box algorithm is a type of machine learning algorithm that generates predictions or decisions without providing a clear explanation of how it arrived at those outcomes. This lack of transparency makes it difficult to understand why a particular decision was made or how the algorithm reached its conclusion. Black box algorithms can be contrasted with more transparent algorithms, which provide insight into how the algorithm arrives at its decision.
How Black Box Algorithms Work
One example of a black box algorithm is a neural network, which is a type of machine learning algorithm that is modeled after the human brain. Neural networks consist of multiple layers of interconnected nodes, each of which performs a specific function. These nodes use mathematical calculations to transform input data into output data, and the network is trained using large datasets to optimize its performance. However, because of the complexity of neural networks, it can be difficult to understand how the network arrived at a particular output, making it a black box algorithm.
Why Are Black Box Algorithms Dangerous in Healthcare?
Black box algorithms can be dangerous in healthcare for several reasons. First, they can be biased, leading to inaccurate or unfair predictions. Bias can arise in many ways, such as through the use of biased training data or biased algorithmic design. For example, if a machine learning algorithm is trained on a dataset that is not representative of the population it is meant to serve, it may produce inaccurate predictions for that population. Similarly, if an algorithm is designed to prioritize certain outcomes over others, it may produce unfair predictions that disadvantage certain groups of patients. Second, black box algorithms can be unpredictable, making it difficult to understand how they will perform in different situations. This unpredictability can be particularly dangerous in healthcare, where decisions can have life-or-death consequences. For example, if a black box algorithm is used to diagnose a patient, it may produce inaccurate results that lead to inappropriate treatment decisions. Similarly, if an algorithm is used to predict patient outcomes, it may produce inaccurate predictions that lead to inappropriate resource allocation. Finally, black box algorithms can be difficult to interpret, making it challenging to understand how they arrive at their predictions. This lack of transparency can make it difficult to identify errors or biases in the algorithm, as well as to explain its decisions to patients or clinicians. This can erode trust in the algorithm and the healthcare system more broadly, making it less likely that patients will accept or follow its recommendations.
Strategies to Mitigate The Risks Associated with Black Box Algorithms
Improving transparency:
One of the most effective ways to mitigate the risks associated with black box algorithms is to improve their transparency. This can be done in several ways, such as by providing explanations for how the algorithm arrived at its decision or by making the underlying code available for inspection. Improving transparency can help to identify errors or biases in the algorithm, as well as to build trust in its predictions.
Using interpretable models:
Another strategy for mitigating the risks associated with black box algorithms is to use more interpretable models. These models, such as decision trees or logistic regression, are designed to be more transparent, making it easier to understand how they arrived at their predictions. While these models may be less accurate than black box models, they are often more reliable and can be used to provide insight into how an algorithm arrived at its decision. Moreover, interpretable models can help to identify any biases or errors that may be present in the data or the algorithm.
Assessing bias:
It is essential to assess the bias that may be present in an algorithm, particularly when dealing with healthcare data. Bias can arise in several ways, such as through the use of biased training data or biased algorithmic design. Assessing bias involves identifying any factors that may influence the algorithm’s decision-making process and taking steps to mitigate them. For example, it may involve using more diverse and representative training data or adjusting the algorithm’s parameters to account for different patient populations.
Regular monitoring:
Regular monitoring of black box algorithms is essential to ensure that they are performing as expected and that any errors or biases are identified and addressed promptly. Monitoring involves tracking the algorithm’s performance over time and comparing it to expected outcomes to identify any discrepancies. Regular monitoring can help to improve the algorithm’s performance and identify any issues that may arise.
Involving clinicians and patients:
Finally, involving clinicians and patients in the development and use of black box algorithms is essential to ensure that their needs and perspectives are taken into account. Clinicians can provide valuable input on the algorithm’s design and use, while patients can provide feedback on how they perceive the algorithm’s predictions and how they impact their healthcare decisions. Involving stakeholders can help to build trust in the algorithm and ensure that it is used appropriately.
Using Black Box Algorithms Strategically in Healthcare
Black box algorithms can be dangerous in healthcare due to their potential for bias, unpredictability, and lack of transparency. However, there are several strategies that can be used to mitigate these risks, such as improving transparency, using interpretable models, assessing bias, regular monitoring, and involving clinicians and patients. It is essential to implement these strategies to ensure that black box algorithms are safe and effective in healthcare, and that they do not adversely affect patient outcomes. As machine learning continues to play an increasingly important role in healthcare, it is crucial that we continue to develop and refine our understanding of the risks associated with black box algorithms and how to mitigate them.
