Many enterprises want explainability for third-party AI models, including generative AI — but the black box dilemma means they’re simply not explainable or require lots of coordination with a vendor. Explainable AI (XAI), explainability, and interpretability for these models are critical challenges, yet proven tools and strategies can help overcome them. Despite these hurdles, there are real ways to build trust with GenAI and third-party vendor models.
Check out this webinar, featuring ModelOp CTO Jim Olsen, to learn about XAI, ways to build trust with GenAI, and assessing AI usage in your organization. Jim will discuss critical topics including traceability, tracking RAG resources, and managing attestations — providing approachable steps for enabling explainable AI capabilities within your enterprise.
Transcript
Chapter 1: Ensuring Fairness in AI (Jim Olsen)
An essential aspect of AI models is ensuring fairness. We want to make sure our models make ethical and unbiased predictions, not only toward protected classes but also for all customers. For instance, commercially used models must deliver consistent experiences.
Another major concern in explainable AI is privacy. With regulations like GDPR, it's critical to understand the data used in models. For foundational models, where data origins are often unclear, there's a risk of inadvertently disclosing private or protected information. Confidence in protecting privacy is a cornerstone of building trust in AI systems.
Chapter 2: Interpreting Traditional AI Models
Traditional AI models have been around for a while, and many are inherently interpretable by design. The complexity of a model often determines how easily its processes can be understood. For instance, decision trees allow users to trace paths through nodes, while logistic regression provides a clear mathematical framework.
These models are particularly prevalent in sectors like finance, where interpretability ensures defensibility. By understanding how these models operate, we can confidently verify their consistency and reliability.
Chapter 3: Understanding Neural Networks and Interpretability
Neural networks introduce unique challenges for interpretability. These models often classify data or make predictions based on input features. Post-hoc interpretability techniques, such as Shapley Additive Explanations (SHAP), are commonly used to determine the importance of various features.
SHAP, for example, applies game theory to identify the contribution of each feature to a prediction. Similarly, proxy models—like mapping a logistic regression onto a neural network—can help translate complex outputs into more understandable formats. These techniques build trust by illuminating the inner workings of neural networks.
Chapter 4: Challenges of Generative AI Interpretability
Generative AI models, such as those used for natural language processing, are often considered inherently uninterpretable. While they produce fluent outputs, these models can lack factual accuracy, making them both persuasive and potentially misleading.
For example, generative AI might struggle with simple math problems or hallucinate entirely false information. Addressing these risks involves strategies to mitigate the fear of errors and instill user confidence in deploying generative models for practical purposes.
Chapter 5: Emerging Techniques for AI Explainability
New experimental techniques aim to enhance the interpretability of AI systems. Self-explanation methods, for example, encourage models to break down their reasoning, while visualization techniques offer insights into the factors influencing outcomes.
Reinforcement learning and additional training tailored to specific tasks are becoming more common. However, these methods are still developing and may not always deliver reliable results. Despite their limitations, such techniques represent promising steps toward making AI systems more transparent and trustworthy.
Chapter 6: Building Trust through Model Documentation
Explainability and interpretability are key to understanding how a model makes specific decisions and generates outputs. By making model behavior comprehensible, organizations can boost confidence in AI systems, ensuring they are both defensible and reliable—especially during audits or compliance checks.
For less inherently interpretable models, providing traceability into their usage, approval process, and governance is critical. This includes documenting every step from onboarding to deployment. Such documentation ensures accountability and compliance, offering a clear record of the model’s journey through the organization.
The process of creating proper documentation can be time-consuming and prone to errors when done manually. Additionally, as models are updated over time, their documentation often becomes outdated or incomplete. To address this, automated solutions like ModelOp Center enable the generation of fresh, accurate documentation by leveraging data gathered throughout the modeling process.
Different audiences require different levels of detail. For example, data scientists may need in-depth review documents, while end-users benefit from concise, consistent formats like model cards. Automating the creation of these tailored documents not only saves time but also reinforces trust by ensuring the documentation is both comprehensive and accessible.
By simplifying and standardizing documentation, organizations can foster greater transparency, build trust with users, and maintain compliance with regulations—critical steps in the journey toward effective AI governance.
Chapter 7: Establishing Baseline Metrics for Trust
Establishing consistent baseline metrics is crucial for building trust in AI systems. By automating the collection of baseline data before deploying a model, organizations gain a reference point for future comparisons.
For instance, in sentiment analysis or speech recognition models, the initial results may not reveal much on their own. However, continuous monitoring and comparisons against the baseline can highlight drift or unusual behavior, signaling potential issues. For example, if the types of words a model uses shift dramatically from its original output, it may indicate underlying problems.
Baseline metrics also reassure stakeholders that models are being actively monitored and evaluated over time. This process not only builds trust but also provides an audit trail, ensuring accountability for the system's performance and adherence to its intended use.
Chapter 8: Expert Attestations and Oversight
Trust in AI systems can be further bolstered through expert attestations. By having internal experts review and approve models at key stages—such as development, deployment, and post-incident evaluations—organizations demonstrate due diligence.
For instance, experts can identify weaknesses, validate a model's suitability for specific tasks, and approve vendor models with limited transparency. Regular reviews ensure that models continue to perform as intended, even as they age or face changing conditions.
This oversight process includes documenting who reviewed the model, what was evaluated, and why certain decisions were made. Such documentation ensures accountability and provides a clear chain of responsibility, crucial for both internal trust and external audits.
Chapter 9: Oversight and Review of AI Models
Maintaining oversight of AI models is essential, especially as systems become more autonomous. This involves tracking where and how models, such as foundational models like GPT-4 or Llama 2, are used across the organization.
Approval processes should verify that models are suited for their intended use cases and compliant with regulations. Comprehensive oversight ensures that models are not operating unchecked and that their outputs align with organizational standards. Regular reviews and consistent monitoring instill confidence and provide a safety net as AI systems grow in complexity.
Chapter 10: Navigating EU Regulations for AI Usage
Compliance with regulations, such as the EU AI Act, is a top priority for organizations using AI. These laws often mandate detailed documentation to prove adherence to standards, particularly when AI is used in high-risk areas like health and safety.
To meet these requirements, organizations need a robust inventory system that tracks use cases and ties them back to foundational models. This system should provide a "single pane of glass" for managing compliance efforts. Beyond legal obligations, having this infrastructure supports smoother upgrades, better oversight, and enhanced decision-making regarding AI systems.
Chapter 11: Monitoring and Testing AI Models
Monitoring and testing are vital for ensuring AI models perform as intended. Automated backtesting can provide consistent evaluations over time, uncovering drift or bias that might compromise the model’s reliability.
Generative AI models, for example, require different testing approaches than traditional predictors. Summarization models might be evaluated for accuracy and relevance, while chatbot models might need bias detection through persona testing. Exception reporting ensures that any issues are promptly identified and addressed, maintaining the integrity of the model.
This proactive approach reassures stakeholders that AI systems are being monitored effectively, reducing the risk of failures and increasing trust in their outcomes.
Chapter 12: Bias Detection in AI Systems
Detecting and addressing bias in AI systems is a critical step toward ensuring fairness and reliability. Bias detection techniques, such as persona-based testing, can help evaluate whether models provide consistent results across different protected classes. For example, altering only the protected class within a persona allows testers to identify disparities in model outputs.
When biases are detected, organizations must generate exception reports to document and address the issue. These reports ensure accountability and guide corrective actions, fostering trust in the system’s performance. Proactively managing bias not only enhances the ethical use of AI but also mitigates potential reputational and legal risks.
Chapter 13: Demonstrating Regulatory Compliance
Regulatory compliance is a cornerstone of responsible AI usage. Organizations must be able to demonstrate that their processes adhere to all relevant regulations, from annual reviews to baseline tests and monitoring.
Solutions like ModelOp Center provide a comprehensive view of every step in the AI lifecycle. This includes the ability to define, modify, and track processes as new regulations emerge. By maintaining detailed records and following well-documented workflows, organizations can satisfy auditors and ensure internal accountability, reinforcing trust in their AI systems.
Chapter 14: Building Trust in AI Solutions
Trust is the foundation of successful AI adoption. Failures in AI can lead to not only financial losses but also reputational damage. For instance, systems that generate erroneous outputs or exhibit unethical behavior undermine confidence in the technology.
To build trust, organizations must prioritize visibility and traceability. This includes having a clear inventory of all AI models, both internal and third-party, and implementing rigorous testing and documentation practices. Trust is not only essential for end-users and auditors but also for developers, ensuring the entire AI ecosystem functions reliably.
Chapter 15: Consequences of AI Failures
The consequences of AI failures can range from inconvenient errors to severe legal and ethical violations. High-profile examples include recruitment systems rejecting older applicants or healthcare algorithms neglecting the needs of marginalized groups.
Such failures highlight the importance of robust governance and oversight. By ensuring models are explainable, interpretable, and aligned with organizational values, companies can mitigate risks and avoid costly fines, reputational harm, or worse—moral transgressions that affect real lives.
Chapter 16: Real-World Implications of AI Errors
AI errors can have significant real-world implications, from financial penalties to public scandals. For example, a lawyer who relied on an AI model to cite non-existent legal precedents faced not only a fine but also professional embarrassment.
These incidents underscore the need for transparency and rigorous validation processes. Organizations must monitor AI systems continuously, maintain thorough documentation, and provide mechanisms for human oversight. Building trust in AI requires a proactive approach to identifying and mitigating risks before they lead to failures.
Chapter 17: Ensuring Explainability and Traceability
Explainability and traceability are key to building trust in AI systems. Automated tools can help organizations implement explainability techniques and ensure adherence to regulations and internal processes.
Traceability involves documenting the entire lifecycle of an AI model, from development to deployment and beyond. This visibility fosters confidence among stakeholders, reassuring them that models are both reliable and compliant. In a complex and rapidly evolving field, explainability and traceability provide the foundation for responsible AI governance.
Chapter 18: Closing Remarks and Future Engagements (Jay Combs)
Thank you for joining this webinar! We appreciate the opportunity to share insights into AI governance and discuss how organizations can navigate its complexities. We’re always available for one-on-one meetings to address your specific needs and challenges around AI governance or generative AI.
As mentioned, the recording and slides from this session will be sent out for your reference. We encourage you to register for future webinars, including our upcoming session in December, where we’ll recap key AI governance trends from 2024 and provide a look ahead to 2025. Stay connected, and we look forward to engaging with you further.
Chapter 19: Looking Ahead: Trends in AI Governance
As we approach 2025, the landscape of AI governance continues to evolve. Our December webinar will explore the major issues and trends shaping the field, along with predictions for the year ahead. From regulatory changes to technological advancements, we’ll highlight the challenges and opportunities that lie ahead for organizations leveraging AI.
We thank you for your time and participation in this webinar series. Your engagement and feedback are invaluable as we collectively work toward responsible and effective AI governance. Until then, we wish you a wonderful Thanksgiving and holiday season. Take care, and we’ll see you at the next session!