Machine learning (ML) is gaining more popularity with both end-users and businesses, as the discipline keeps breaking new boundaries with respect to performance and associated value. Still, ML is a relatively new discipline for production systems, and it requires adopting a specialized ecosystem of processes and technologies that are evolving at a fast pace.
As teams aim to stay up-to-date, complex models are often productionized as black boxes or as in-house solutions that are not fully tested or explained. This setting leads to risks in the use and behavior of the model, which can affect user experience and business revenue. Organizations need to assess the risk of their production ML systems as part of overall risk management to ensure their actual value.
This article introduces the concept of risk management for ML and what technical risks come with ML models. We’ll also discuss how these risks can affect your business and how to adopt a successful risk management framework to mitigate them.
Machine learning risk management involves processes to define, measure, control, and mitigate the risks inherent in ML systems. These processes encompass data, infrastructure, and people.
Risk management aims to proactively and seamlessly detect risks inside ML systems before they affect users and stakeholders. The risks can be related to both the model itself and the production processes. (To learn more about production processes, please refer to “What is Model Management?”)
Below, we discuss the several risks at play when it comes to ML systems.
Models learn the patterns inside the data to perform their predictions. To ensure the model behaves as desired, the datasets need to be cleaned from the presence of noisy and biased data and not misuse PII data.
Model inference provides predictions from a trained ML model. These predictions can be misinterpreted or misused in a couple of cases. First, this can occur when inference is misaligned to model training, i.e., the data distributions and/or data transformations are not an exact match. It can also happen if the definition of the model output is misaligned with business objectives, e.g., the model optimizes for conversion rate instead of click-through rate.
Developing machine learning models requires a variety of design assumptions and decisions to be made. Incorrect logic and/or inconsistent assumptions can cause the model to underfit or overfit for specific or all data slices.
Models can be misused by end-users or external parties, such as adversarial attacks aiming to trick ML models by providing deceptive input. Not mitigating these risks is likely to lead to poor model performance during experimentation and testing at best, or for end users at worst.
Managing risk related to machine learning models and systems is vital for teams because it provides:
If ML is a relatively new discipline for businesses, risk management for ML is even newer. No specific risk management framework exists for ML, and this topic is not particularly spoken about within the community.
Still, we can take inspiration from other domains to create a risk management program for machine learning. One of the most used risk management frameworks outside of ML is the ISO 31000 standard, created by the International Organization for Standardization.
Organizations of all sizes can implement ISO 31000’s set of principles, along with its framework and process, for managing risk; if desired or required, they can even obtain certification.
We recommend reading the official documentation for detailed information, but below, you can find a high-level overview of the seven main steps comprising the ISO 31000 risk management framework.
Define the objectives, external and internal parameters, scope, and risk criteria; these should be agreed upon by all stakeholders.
For ML, stakeholders are data scientists as the owners of model definition and training, machine learning engineers as the owners of the production model and data pipelines, and compliance officers as the experts in risk management and governance.
Create a register to record all risks and update it regularly. Entries should include risk sources, areas of impact, events, causes, and potential consequences.
Deep dive into each identified risk by expanding on the assessment of consequences, likelihood, and threat levels for each.
Every organization has a unique balance between risk appetite and tolerance. You may choose to accept a risk, transfer a risk, mitigate it, or avoid it altogether. Aligning your risk policies to your existing business strategy and objectives ensures that ML initiatives maximize value.
The exact definition of the processes on how to respond to the risks should be based on the expected cost and return for each treatment option.
Communication should happen between internal and external stakeholders across all steps related to ML risk management. Full transparency, clear assignment of roles and responsibilities, and a proactive attitude are key to your success
Make sure to regularly monitor all processes and results, and adjust these as necessary.
Creating and maintaining a successful machine learning risk analysis and management framework is likely to require some upfront investment in skills, time, and money.
To support the initiative, we recommend taking advantage of an end-to-end MLOps platform like Iguazio that can provide the infrastructure and automation necessary to seamlessly deploy management processes.
