Challenges for regulated sectors in adopting Machine Learning and potential solutions
How does Machine Learning work?
Machine learning is a method of teaching computers to learn from data without being explicitly programmed. There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
Supervised learning: In supervised learning, a model is trained on a labeled dataset, where the correct output (label) is already known. The model is then used to make predictions on new, unseen data. Examples of supervised learning include linear regression and logistic regression.
Unsupervised learning: In unsupervised learning, the model is trained on an unlabeled dataset and must find patterns and relationships on its own. Examples of unsupervised learning include clustering and dimensionality reduction.
Reinforcement learning: In reinforcement learning, a model learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or punishments.
In all cases, the process of Machine Learning starts with collecting data, cleaning and preprocessing it, then the data is used to train a model. After the model is trained, it is tested on unseen data to evaluate its performance, and if necessary, it’s refined. Finally, the model is deployed and used to make predictions or decisions in the real-world.
The core idea behind machine learning is to train a model on a large dataset, so that it can learn the underlying patterns and relationships within the data, and then use this knowledge to make predictions or decisions about new, unseen data. The quality of the model predictions or decision depends on the quality of the training data, the choice of the model and the tuning of the model’s parameters.
Why is Machine Learning a challenge for regulated sectors?
There are several reasons why machine learning can be difficult to adopt for regulated sectors, such as healthcare, finance, and government:
Compliance: Regulated sectors are subject to strict compliance regulations, such as HIPAA in healthcare and GDPR in finance, which can make it difficult to use machine learning techniques that involve collecting and processing large amounts of data.
Data privacy: In regulated sectors, data privacy is a major concern and organizations must take extra care to protect sensitive information. This can make it difficult to use machine learning techniques that require large amounts of data, such as deep learning.
Data quality: Regulated sectors often have complex data quality requirements, such as ensuring data is accurate and complete. This can make it difficult to use machine learning techniques that are sensitive to missing or incorrect data.
Explainability: Many regulated sectors require that decisions made by automated systems can be explained and audited, which can be difficult to achieve with some machine learning models, such as deep learning models, that are considered as black boxes.
Governance: Regulated sectors often have strict governance requirements, such as maintaining a chain of custody for data and maintaining records of all decisions made by automated systems. This can be difficult to achieve with machine learning systems, which are often highly distributed and automated.
Limited access to data: Regulated sectors often have limited access to data, which can be difficult to work with machine learning techniques that require large amounts of data.
Security: Regulated sectors often have strict security requirements, such as maintaining secure communication channels and protecting against data breaches. This can be difficult to achieve with machine learning systems, which are often highly distributed and automated.
Overall, regulated sectors face additional challenges compared to other sectors when it comes to adopting Machine Learning. This is why it’s important for organizations in regulated sectors to have a clear understanding of the regulations and compliance requirements that apply to them, and to work closely with regulatory bodies and experts in data privacy, security, and governance when developing machine learning solutions.
How can regulated sectors start their Machine Learning journey?
Here are a few ways that regulated sectors can start using machine learning:
Start small: Regulated sectors can start by piloting small machine learning projects that are closely aligned with their compliance and regulatory requirements. For example, a healthcare organization might start by using machine learning to improve patient triage in an emergency department.
Work closely with regulatory bodies: Regulated sectors should work closely with regulatory bodies to ensure that their machine learning projects are compliant with all relevant regulations. This can involve getting pre-approval for specific projects or working with regulatory bodies to develop new guidelines for machine learning.
Focus on data quality: Regulated sectors should ensure that their data is of high quality and that it meets all relevant compliance and regulatory requirements. This can involve implementing data quality checks and working with experts in data privacy and security.
Prioritize explainability: Regulated sectors should prioritize explainability when developing machine learning models, so that the decisions made by the models can be easily audited and understood. This can involve using interpretable models, such as decision trees, or developing methods for visualizing and explaining the decisions made by more complex models.
Implement robust governance and security measures: Regulated sectors should implement robust governance and security measures to ensure that their machine learning systems are compliant with all relevant regulations and that they protect against data breaches and other security threats.
Develop an MLOps pipeline: Developing an MLOps pipeline allows regulated sectors to automate the process of building, testing, and deploying machine learning models in a way that is compliant with all relevant regulations.
Leverage cloud-based ML platform: Cloud-based ML platforms, such as Amazon SageMaker, Google AI Platform, and Microsoft Azure Machine Learning, offer fully managed ML platforms that include tools for building, deploying, and monitoring machine learning models. These platforms allow data scientists and machine learning engineers to focus on model development and leave the operational aspects to the cloud provider.
Hire experts: Hiring experts in machine learning, data privacy, security, and governance can help regulated sectors navigate the complexities of using machine learning in a compliant and secure way.
By following these best practices, regulated sectors can start using machine learning in a way that is compliant with all relevant regulations and that protects the privacy and security of sensitive data.
