Building Trustworthy AI with MLOps: Data Robot's Solution

Table of Contents

1. Introduction
2. Trust in AI
   1. Transparency
   2. Accuracy
   3. Robustness and Stability
   4. Speed
3. Trust in AI Operations
   1. Compliance
   2. Security
   3. Humility
   4. Governance and Monitoring
   5. Business Rules
4. Trust in the Ethics of the Workflow
   1. Privacy
   2. Bias and Fairness
   3. Explainability
   4. Impact Assessment
5. Building Trustworthy AI with ML Ops
   1. Deployment
   2. Monitoring
   3. Model Management
   4. Governance
   5. Continuous AI
   6. Trusted AI
6. Conclusion

Building Trustworthy AI with ML Ops

Artificial intelligence (AI) has become a buzzword in many industries, promising to deliver tremendous value to companies. However, there are numerous stories of lost public trust in AI and concerns around bias and fairness. Trust is not automatically baked into AI systems, and it must be earned through transparency, accuracy, and accountability.

In this article, we will explore the components of trust in AI systems and how bringing better transparency and accountability into your AI workflows can augment your decision-making and help you answer key questions for your organization. We will also discuss ML Ops, an automated system that streamlines all the model life cycle steps from its training and development through its production life, governance, and compliance.

Trust in AI

Trust is a quality that is earned, not given. When it comes to technology, trust in a solution must be earned through transparency, accuracy, and accountability. For companies that are or want to become AI-driven, this process of evaluation and assessment is an essential part of ensuring that they can trust their AI to help them make the most critical decisions. In many cases, it's also important for customers, suppliers, and vendors to know that the systems behind an organization's decision-making process are fundamentally trustworthy and secure.

Transparency

Transparency is a critical component of trust in AI. It's important to be able to peek behind the curtain to form a questive understanding of the underlying processes that drive AI systems. The assessment of a model's performance is Based, in part, on its ability to make predictions based on your data. Therefore, it's essential to understand the origin of this data, including how it was collected, how it was curated, and whether it contains a large number of missing values. It's also important to evaluate the accuracy of your model throughout the development process and being able to communicate the results.

Accuracy

Accuracy is another critical component of trust in AI. It's important to evaluate the accuracy of your model throughout the development process, being able to select the appropriate accuracy metric for the use case, and being able to communicate the results are both very critical. Other things to consider include using the cross-foundation to test your model's effectiveness on different segments of the training data and asking what trust signals are appropriate to define and communicate the performance of your model.

Robustness and Stability

A modern production must be reliable and stable to Continue delivering value. Continuously monitoring a model in production for data drift, changes in accuracy, and system responsiveness are very critical and crucial to building a trustworthy system. Therefore, it's essential to ensure that your model will behave in consistent and predictable ways over time.

Speed

The speed of the model scoring directly impacts how it can be used in your business processes. Therefore, it's important to consider how the speed of the machinery model should inform model selection criteria and AI system design. Based on customer experience or requirements, you might want to choose faster and not necessarily the most accurate model.

Trust in AI Operations

A trustworthy AI system is one that takes into account the infrastructure of a software and the people that operationalize the model. It's essential to ask how reliable the system that my model is deployed on is. Compliance, security, humility, governance and monitoring, and business rules are all critical components to consider in a reliable system.

Compliance

Depending on your industry, you might have regulatory requirements that you need to Align the model performance with when putting a model into production. Therefore, it's essential to facilitate the verification and validation of your AI system.

Security

In most cases, there's a lot of sensitive information about the company, such as revenue, employee performance, salary, sales leads, etc., that can be part of your training data. Therefore, it's essential to implement best practices to keep your data and models secure.

Humility

Not all modern predictions are made with the same level of confidence. A trustworthy system knows when to be humble. Therefore, it's essential to consider how your system should respond to uncertainty.

Governance and Monitoring

Without good governance, the best models might still result in undesired or unintended behavior. Good governance and monitoring are essential to ensuring that your AI system delivers the value you need in production. Therefore, it's essential to support principles like disclosure and accountability in the management of your AI system.

Business Rules

Integrating AI into an existing process is essential to ensure that it delivers the most value to your business. Therefore, it's essential to make an AI system a value member of your team and a seamless part of your business process.

Trust in the Ethics of the Workflow

There is no one-size-fits-all approach to AI ethics, especially since AI systems and the data that they use can span borders and cultures, with multiple stakeholders and complex social implications. AI becomes embedded in every area of your business. Ethical AI questions center around privacy, bias and fairness, explainability, and impact assessment.

Privacy

Individual privacy is a fundamental right, but it's also complicated by the use and exchange of data. Therefore, it's essential to understand what role AI plays in the management of sensitive data.

Bias and Fairness

AI bias is a difficult topic to navigate due to both its complexity and the social implications of what it means to be fair in decision making. Therefore, it's essential to consider how AI systems can be used to promote fairness and equity in your decision-making.

Explainability

Explainability is one of the most powerful ways to build trust between a user and an AI model. Therefore, it's essential to facilitate trust through explainability between machine and human decision-makers.

Impact Assessment

An impact assessment is a powerful tool that can help You think systematically about how to identify the desired behavior of your system that will reflect your organization's values.

Building Trustworthy AI with ML Ops

ML Ops is an automated system that streamlines all the model life cycle steps from its training and development through its production life, governance, and compliance. It helps build the bridge between systems and teams to ensure that your machine learning will fulfill its promise and could lead to lack of confidence and trust in your models.

Deployment

DataRobot ML Ops simplifies model deployments by streamlining the process between modeling and production. It allows you to easily deploy any model into your production environment of your choice.

Monitoring

DataRobot ML Ops allows both production and AI teams to monitor models in ways specific to machine learning. This robust monitoring infrastructure is able to practically monitor data drift, feature importance, and model accuracy issues.

Model Management

DataRobot ML Ops allows for a production model lifecycle management system that automates processes such as champion-challenger gating, troubleshooting and triage, hot swap model of approvals. It also offers a secure workflow to ensure efficient management of your model's lifecycle as you Scale.

Governance

DataRobot ML Ops offers an enterprise-grade production model governance to maintain complete more linear tracking such as approvals, model interactions, and versions deployed. Being able to Create these reports on demand essentially allows you to always be on top and don't have to worry about that.

Continuous AI

Continuous AI combines the best of automated machine learning with the best of machine learning operations to continually improve models over their full life cycle. With continuous AI, you can create multiple envelopes strategies to refresh your production models based on schedule or even when an event occurs such as data drift or a drop in accuracy.

Trusted AI

DataRobot ML Ops has built specific features under trusted AI, including bias and fairness, humility rules, and prediction explanations. These features allow you to detect bias via fairness metrics and solving racial discrimination for models in production.

Conclusion

In conclusion, building trustworthy AI with ML Ops is essential to ensure that your machine learning will fulfill its promise and could lead to lack of confidence and trust in your models. By following the components of trust in AI systems and bringing better transparency and accountability into your AI workflows, you can augment your decision-making and help you answer key questions for your organization.