How to build trust in your ML models

It’s no secret that AI and ML are integral to our future, with teams building algorithms for everything from robotic surgery to sorting fresh produce. But many of us are also skeptical about predictions made by deep learning models. Consumers often find recommendation algorithms as mysterious and daunting as they are helpful. On the enterprise side, leadership and stakeholders place less trust on model outputs when they don’t know how the model produced them. This presents a tough challenge for ML teams who want to build transformative models to support major operations throughout the organization. Let’s explore a few ways that teams can ensure that their models are trusted within their enterprise.

AI regulations

While AI is still a fledgling technology, it has proliferated our lives to the point where governments across the world are setting guidelines and regulations for AI, primarily to protect people from the effects of biased algorithms and personal data usage. Many of these regulations will require AI risk assessments, accountability from companies employing AI, and continuous review processes. While these requirements might require more work to meet, they are also important steps to ensure that both consumers and stakeholders within the business can trust AI.

Model governance

For existing models, it’s important to set up a thorough governance system that regularly tests its output to the “ground truth” or ideal results to find and fix any model degradation. Completing regular goodness of fit analyses, fixing any issues that may be causing degradation, and sharing the findings with your stakeholders and broader organization will go a long way to improving transparency and setting expectations to what the model can be expected to achieve, which will build their trust not only in the model itself, but with your ML team as a whole.

Teams can also employ third-party auditors to test and analyze their models’ performance and efficacy, which can provide additional proof of quality that might be necessary for use cases that deal with customer data. As your models continue to perform well over time, the number of stakeholders that rely on them will likely increase.

While following regulations and setting up governance structures can help garner trust in your models after deployment, many important trust-building actions should take place as the model is designed and trained.

Building trustworthy ML

Creating ML models that are trusted to work by their primary users — whether that’s customers, business leadership, or other departments in your enterprise — requires two important steps that should be taken during the development and training process.

Transparency and collaboration. Teams should ensure that they are in agreement with the primary users of the model on their use case, requirements, and the limitations of what the model can do. Stakeholders should also be involved throughout the model development, training data collection and creation, and training processes. When a model’s users know how it was trained and how well it performs, they are more likely to consider it a reliable tool.

For example, Labelbox customer NASA JPL integrated scientists, the model's primary users, into the ML pipeline. The team coordinated with scientists to review model outputs and verify its efficacy with each iteration. "They won't trust it if it seems like a black box," said Jake Lee, a data scientist at the lab.

High-quality training data.  Many distrust AI due to the potential for unforeseen biases that models may pick up through the training process. To combat this, teams can put together a diverse labeling workforce — this is particularly important when the model input is going to be images or depictions of people — and generally prioritize high-quality training data. To improve training data quality, teams can:

  1. Employ quality management processes such as consensus and benchmarking
  2. Use analytics to find and improve processes that produce lower quality labels
  3. Monitor model performance over iterations to pinpoint errors and ensure that the model is “fed” only on data that will significantly improve its performance

Investing in a training data platform like Labelbox can help ML teams use these methods and more to quickly create high quality training data. Training models on high-quality training data will increase performance — and once their accuracy and precision analytics are shared with stakeholders and the enterprise as a whole, your model is more likely to gain their trust.

Learn more about how a training data platform can help your ML team improve its labeling operations over an in-house labeling solution.

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