Governance, Trustworthiness, and Production-Grade AI: Building the Future of Trustworthy Artificial Intelligence
Governance, trustworthiness, and building reliable systems are crucial for pushing AI development forward. Trustworthy AI development was central in a recent “Chain of Thought” podcast episode hosted by Conor Bronsdon, Head of Developer Relations at Galileo, featuring insights from Sara Hooker, VP of Research at Cohere and Head of Cohere for AI, and Craig Wiley, Senior Director of Product, Mosaic AI at Databricks.
This article explores key insights on AI governance and its crucial role in developing production-ready AI.
The Importance of Governance in AI
Governance is exceedingly vital in AI development. Regulatory measures, like President Biden’s executive order, underscore the need for trustworthy AI systems. Wiley points out that good governance ensures AI systems are accurate, reliable, and meet essential standards.
Challenges in AI Governance
As AI capabilities grow, regulations are tightening, requiring businesses to govern their data and processes carefully amidst increasing regulatory challenges.
"Legislation and compliance is coming," Wiley notes. Highlighting the need for companies to understand their data flows and maintain strict governance of AI applications. The shift towards stricter oversight means companies must proactively align their systems with legal requirements to avoid costly mistakes and protect their reputations.
Managing data lineage is crucial for maintaining compliance and governance in AI systems. Having a high degree of confidence in how data is used is essential. For AI systems handling sensitive information, it's critical to trace data origins and usage throughout the AI lifecycle.
Tracing data origins not only helps with internal audits and optimization but also ensures adherence to evolving regulatory standards.
Without effective AI governance, companies risk operational inefficiencies, data privacy issues, algorithmic biases, and damage to their brand reputation. Inadequate systems can have far-reaching consequences, affecting a company's ability to rely on AI for essential tasks. In today’s environment of increased scrutiny, organizations must ensure every aspect of their AI operations can withstand regulatory examinations and meet public expectations.
Overall, governance in AI is becoming essential as organizations aim to harness AI's potential while protecting their interests in a landscape of rising technological and regulatory demands.
Building Trustworthy AI Systems
As AI becomes a part of high-stakes industries, trustworthiness is paramount. As AI systems require more data, it's vital to understand how data flows and its origins.
"The ability to ensure that whoever's asking a question of the system actually has access or permission to view any of the data used in the system" is key, Wiley points out.
This governance is essential not just for internal data management but also for meeting external compliance needs as data usage regulations tighten.
Building Trust through Evaluation and Monitoring
Trust in AI also comes from thorough evaluation and real-time monitoring. Vikram Chatterji, CEO of Galileo, mentions that the industry is moving toward "evaluation-driven development," where best practices in AI system evaluation are crucial.
Reliable systems need precise AI evaluation methods and a deep understanding of different domains. Evaluations are hard because they require rigorous effort to ensure models are optimized and performing as expected.
Effective monitoring tools and post-deployment monitoring are vital for addressing application performance proactively, especially in sectors like banking and healthcare where reliability is non-negotiable.
Workplace Examples of Trustworthy AI
Trustworthiness in AI brings real benefits to various workplaces. In the financial sector, using an effective LLM evaluation framework can prevent mistakes in data interpretation that affect decision-making.
Regulatory compliance is a reality, and companies need to align their AI models with strict governance standards to protect against data breaches. In healthcare, robust monitoring ensures patient data is handled with care, reducing the risk of bias or inaccuracies. The industry's focus on trust highlights its push to refine AI tools to meet specific, critical needs without compromising reliability or security.
Developing Production-Grade AI
Building on trustworthiness, developing production-grade AI systems comes with its own set of challenges. The transition from concept to reliable systems is filled with obstacles. Wiley points out a key lesson the industry has learned: "Folks found that prompt plus some docs doesn't equal a system that I can bet my company’s reputation on."
This observation highlights the complexity of deploying AI systems that enterprises can depend on.
Creating production-grade AI requires significant investment in both technology and operations. As interest in simple AI prototypes fades, the need for robust, accurate systems that fit seamlessly into existing workflows becomes clear.
Developers need to focus on key performance metrics to ensure their models are optimized for production environments. Wiley also notes that oversimplifying AI's capabilities led to unrealistic expectations of "max simplicity," which conflicted with the need to minimize errors and inconsistencies in these systems.
Toolification and Function Calling
To tackle these challenges, the industry is seeing the rise of "toolification" and function calling. Sarah Hooker believes that tooling will become “much more pronounced, but people will have a tough time evaluating it.” Some of these tools and software don’t necessarily interact with one another (in terms of access, APIs, etc.) making deployment more difficult.
Function calling allows complex tasks to be broken down into smaller, manageable functions, enhancing accuracy and efficiency. These systems can now deploy "purpose-built capabilities" to handle specific areas, improving overall reliability. Adopting monitoring best practices can help developers ensure these modular systems perform reliably in production.
This move toward toolification is expected to lead to stepwise improvement in the accuracy of these systems by focusing on narrower tasks instead of broad, undefined problems.
By leveraging function calling, developers can create more modular systems that are easier to optimize and fine-tune, which is essential for achieving production-grade AI.
Industry-Specific Challenges and Solutions
Different industries face unique challenges when deploying AI, requiring tailored solutions. Organizations need a deep understanding of their domain and must develop evaluation methods that reflect their specific needs.
For example, in insurance, accurately parsing and interpreting complex documents like claims forms is crucial. Having evaluation schemas and the ability to understand that domain is key to producing high-quality results.
Industry-specific solutions often involve integrating domain-specific tools and models, ensuring AI systems are not only accurate but also comply with legal and regulatory requirements. Choosing appropriate monitoring solutions tailored to specific industries can enhance compliance and performance.
By the end of 2025, companies should expect to have clearer strategies to align AI systems with their specific goals, significantly improving their ROI. Such a nuanced approach allows AI to excel in production environments, bridging the gap between experimental prototypes and reliable, industrial-grade systems.
Paving the Way for Trustworthy AI
Looking forward, the insights from our panel highlight the vital role of governance, trustworthiness, and reliable performance in AI systems. Ultimately, the path forward for AI requires ongoing vigilance and adaptability from all stakeholders. Learn more about how Galileo is contributing to trustworthy AI development.
And listen to the full episode for more AI predictions like whether or not 2025 will be the year open-source LLMs catch up with their closed-source rivals and much more.