Machine Learning Governance

As your organization starts to experiment with Machine learning (ML) systems, consider creating an ML governance framework that adheres to the responsible Machine learning principles and considers other key factors including bias evaluation, model explainability, human-based model assessment, reproducible operations, privacy, security, and regulatory compliance.

According to Johannes Drooghaag, CEO Spearhead Management:

Regulated industries will face growing challenges with auditing of AI/ML, especially in regards of the complexity of algorithms, data, purpose, and utilization.

The Institute for Ethical AI and Machine Learning, a UK-based research center, offers an eight principles framework that can be adopted as is or utilized with adjustments relevant to your organization. These principles are built considering all the phases of the machine learning systems including development, deployment, and operations. Any organization can take these principles and create its own version of the machine learning governance framework.

A typical machine learning governance framework within an organization should include the following core elements along with any others pertaining to your organization.

Human evaluation

Humans should review all machine learning processes and models to assess the accuracy of the results and predictions that a model may be making. This stems from the fact that models are not perfect, and we want to make sure that humans review them for the correctness of the results. This is where a data scientist or business analyst can double-check the results produced by machine learning models rather than putting their blind trust in the machine learning model results. Complementing human validation with explainability output from the machine learning tools being utilized will help in making sure that models are producing results and making predictions that are fair, ethical, and can be explained.

Bias assessment

Bias and ethical issues are some of the key issues that you may face when working with ML models. These biases can enter the system primarily due to the data set or the algorithms utilized in training or building the model. You should have processes in place to evaluate the models and how they are going to be used to make sure that they will not make unfair or unethical decisions.

Model interpretability and explainability

Transparency should be a key consideration when using machine learning models. You should have complete insights into any final model used in any of your systems and have the know-how to explain the entire model’s behavior or why it is making certain predictions. Such explainability and interpretability of ML models ensure that the model is not making biased or unethical decisions and predictions. As an example, when utilizing AutoML in Azure Machine Learning and it comes up with a model recommendation, it also has corresponding information that explains model behavior. Azure machine learning offers tools and services for building models with explainability output.

Reproducible operations

Reproducibility is when you can run your model with different datasets and get the same or similar results. Reproducibility of machine learning models helps with smooth operations as you deploy the model in your AI systems and continue to improve it. Reproducible models help reduce errors and improve operational efficiencies as you move your models from development to production. Reproducible models also lend themselves to be easily scaled as business needs grow.??

Privacy and security

There should be processes to handle, protect, and store the data generated from users of the machine learning systems. All these measures must consider the privacy of the users’ data as they interact with the machine learning systems. Security should be part of the entire end-to-end journey of a machine learning system, from access to processing to data storage. Azure Machine Learning provides the responsible ML framework, to warrant the privacy and security of ML models.

Again, according to Johannes Drooghaag

Most organizations still underestimate their responsibilities towards managing, filtering, and securing the data to feed AL/ML-based systems and applications.

Regulatory compliance

Depending on your industry, you may have to maintain specific compliance standards for your ML systems. Make sure that the machine learning systems are compliant with all the regulations of your industry in your specific locality and country. Create and document how the machine learning system is compliant with any of your industry’s required regulations.?

Summary

Machine learning governance is a key part of any organization planning to or currently utilizing Machine learning models for building intelligent AI apps for both internal use or for their customers. You want to make sure that you build apps, utilizing the Machine learning models, which are going to be fair, unbiased, and will not adversely impact the people who are going to use these apps.