Interpretable AI

The upside of understanding

Artificial intelligence (AI) may be advancing rapidly, but the ability to explain how it works remains a challenge, particularly with Generative AI (GenAI) and large language models (LLMs). One organization tackling AI interpretability is Anthropic, an AI research, product, and safety company with whom we collaborated to develop this report.

Anthropic’s approach to interpretable AI promises to yield a better understanding of how AI systems work internally. However, the “black box” problem of opaque AI still poses significant hurdles to scaling AI safely, making interpretability even more crucial for operational performance, risk management, and compliance with AI regulations—particularly in regulated industries.

Authors:

Alison Hu

Gina Schaefer

Sanmitra Bhattacharya

Key takeaways

Generative AI and LLMs pose significant interpretability challenges due to their complex and large-scale operations.
LLMs face technical barriers like feature superposition¹ and probabilistic generation, further complicating interpretation.
Increased demand for interpretability requirements in AI regulations
As AI evolves toward autonomous systems, interpretability becomes crucial for understanding and directing their decision-making processes.

Looking inside the LLM: Tackling the interpretability challenge of Generative AI

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Aspects of interpretability

As we explore the essential role of AI interpretability, let’s examine the technical challenges, operational obstacles, regulatory framework, and evolving role of autonomous AI agents.

Technical barriers: Defying traditional interpretability

The scale and nonlinear complexity of AI, especially LLMs, is outpacing our ability to understand them, creating a widening gap between machine knowledge and human comprehension.

Operational barriers: Business stakes of AI interpretability

Although AI interpretability is often linked to trust, its significance extends far beyond that. Interpretability is especially crucial for industries with strict rules, like finance and health care, where new AI technologies make it even harder to prove these systems work correctly.

Regulatory imperatives: Interpretability in compliance

Evolving AI regulations emphasize the importance of proactive interpretability and transparency in AI systems to help mitigate risks and gain competitive advantages.

AI agents: Interpretability in the age of autonomy

AI agents are autonomous systems that plan, reason, and act with minimal human supervision. As they become more commonplace, collaborating with companies that prioritize interpretable AI is crucial for balancing powerful digital automation with essential human oversight.

Validate before you circulate

Interpretable AI in financial services

In this revealing case study, we examine the rigorous validation processes banks
employ to manage model risk and the challenges posed by increasingly complex
GenAI.

Read the full study

The future of interpretable AI

As AI systems continue to evolve toward autonomous decision-making—with minimal human oversight—AI interpretability will become not only a matter of compliance but a fundamental requirement for deploying increasingly complex and independent AI systems.

Organizations that proactively address this challenge, by prioritizing interpretable models and transparent processes, will be better positioned to leverage the transformative potential of AI. Deloitte’s collaboration with Anthropic not only helps organizations unlock this potential, but also underscores our commitment to maintaining trust and accountability through responsible AI.

Learn more about the Deloitte and Anthropic alliance.

¹ Nelson Elhage et., “Toy models of superposition,” Anthropic’s Transformer Circuits Thread, 2022.

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Interpretable AI