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Best practices for developing governable AI


Building and deploying strong, robust artificial intelligence (AI) and machine learning (ML) models is complex and challenging work. If you are like many data science and machine learning leaders that I have spoken to lately, you are having conversations with other teams about the governance of your systems.

It’s hard to do that and do your job of getting models into production. So let’s talk about what you can do as a technical organization to make AI governance easier both for your team and your business partners, who are key stakeholders in the governance process.

Key design principles

At a high level, to ensure that we have models that are governable and can be assured, we want to make sure model artifacts exhibit the following three principles:

  • Context: After the initial exploratory stages of model development, the business reasons, scope, risks, limitations, and data modeling approaches are well-defined and fully documented prior to a model going into production.
  • Verifiability: Every business and technical decision and step in the model development process should be able to be verified and interrogated. An ML model pipeline should never be a completely “black box” even if a black box algorithm is used. Understanding where the data came from, how it was processed, and what regulatory considerations exist are paramount for building a verifiable model. Model code should be constructed and documented in a way that is comprehensible to someone who hasn’t looked at the code before. The model should be built so that reperforming individual transactions is possible, using containerized architectures, serialization (via pickle, or equivalent), and preprocessing techniques that are deterministic (e.g., Scikit-learn one-hot encoding with a random seed and serialized).
  • Objectivity: The gold standard of governance is when any ML application can be reasonably evaluated and understood by an objective individual or party not involved in the model development. If an ML system is built with the prior two principles of context and verifiability, it is far more likely that your business partners can act effectively as that second-line and third-line objective party to evaluate it and greenlight your work to go into production.

Key capabilities to incorporate into models

Due to the ever-evolving landscape of open source libraries, vendors, and approaches to building ML models as well as the shortage of qualified ML engineers, there is a significant lack of industry best practices for creating deployable, maintainable, and governable ML models.

When developing ML models with governance in mind, the most important considerations are reperformance, version control, interpretability, and ease of deployment and maintainability.

Reperformance

Reperformance is the ability to reperform or reproduce a transaction or a model training and obtain identical results. Much has been said about the “reproducibility crisis” in science, and the AI/ML community is not immune from this criticism.

Copyright © 2021 IDG Communications, Inc.



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