This makes a lot of sense, the analogy to human agents is helpful. It sounds like the newer "reasoning" models that explicitly explain steps might be on the path to adequate explainability?
Absolutely! The newer “reasoning” models that explicitly articulate their thought processes are a promising step toward more robust explainability. They can indeed help principals verify that an AI’s logic stays within its authorized scope, much as we do when we ask a human agent, “How did you arrive at this decision?”
That said, for heavily regulated industries—or any situation where the stakes demand airtight auditing—these emerging models may not be the whole story. Think of blockchain or other tamper-proof ledgers as complementary tools that can lock in the AI’s decision-making trail, ensuring it can’t be tampered with after the fact. In highly scrutinized environments, combining a reasoning-based model (providing human-friendly explanations) with a secure, verifiable record of every key input, output, and decision point is what truly meets the strictest compliance standards. It’s a layered approach—one that leverages both interpretable AI methods and immutable storage solutions—to guarantee the traceability and accountability that regulators (and the public) demand.
Wonderfully clear and lots to explore in centuries(!) worth of literature. Do this framework, especially for general agents, make explainability a condition of use?
Great question! Under the legal agency framework, any “general agent” (AI with broad authority) would indeed require a level of explainability that allows the principal—and, if necessary, external regulators or courts—to verify that the AI acted within its granted scope and in alignment with the principal’s objectives.
However, explainability doesn’t necessarily mean your AI model must disclose every micro-decision or offer a fully transparent “glass box” of its internal workings. Instead, it must produce enough traceable justification so that the principal can fulfill their legal and ethical duties of oversight. This typically involves:
1. Audit Trails: Logging key decisions, inputs, and outputs so there’s a verifiable paper trail of what the AI did.
2. Scope Verification: Demonstrating that the AI acted within clearly defined boundaries—especially important for “general” AI agents with broad mandates.
3. Regulatory Compliance: Certain industries (like finance or healthcare) already demand explanations for how decisions are made or actions are taken; this dovetails with existing rules.
In short, while the framework doesn’t demand a perfect “white box” breakdown of each neural network layer, it does require sufficient transparency to confirm the agent stayed within its authority. Think of it like employing a human manager: the boss might not see every thought process, but they (and regulators) do see the manager’s actions, rationales, and final outcomes. The same principle applies to AI—especially in high-stakes situations where accountability can’t be abdicated.
This makes a lot of sense, the analogy to human agents is helpful. It sounds like the newer "reasoning" models that explicitly explain steps might be on the path to adequate explainability?
Absolutely! The newer “reasoning” models that explicitly articulate their thought processes are a promising step toward more robust explainability. They can indeed help principals verify that an AI’s logic stays within its authorized scope, much as we do when we ask a human agent, “How did you arrive at this decision?”
That said, for heavily regulated industries—or any situation where the stakes demand airtight auditing—these emerging models may not be the whole story. Think of blockchain or other tamper-proof ledgers as complementary tools that can lock in the AI’s decision-making trail, ensuring it can’t be tampered with after the fact. In highly scrutinized environments, combining a reasoning-based model (providing human-friendly explanations) with a secure, verifiable record of every key input, output, and decision point is what truly meets the strictest compliance standards. It’s a layered approach—one that leverages both interpretable AI methods and immutable storage solutions—to guarantee the traceability and accountability that regulators (and the public) demand.
Wonderfully clear and lots to explore in centuries(!) worth of literature. Do this framework, especially for general agents, make explainability a condition of use?
Great question! Under the legal agency framework, any “general agent” (AI with broad authority) would indeed require a level of explainability that allows the principal—and, if necessary, external regulators or courts—to verify that the AI acted within its granted scope and in alignment with the principal’s objectives.
However, explainability doesn’t necessarily mean your AI model must disclose every micro-decision or offer a fully transparent “glass box” of its internal workings. Instead, it must produce enough traceable justification so that the principal can fulfill their legal and ethical duties of oversight. This typically involves:
1. Audit Trails: Logging key decisions, inputs, and outputs so there’s a verifiable paper trail of what the AI did.
2. Scope Verification: Demonstrating that the AI acted within clearly defined boundaries—especially important for “general” AI agents with broad mandates.
3. Regulatory Compliance: Certain industries (like finance or healthcare) already demand explanations for how decisions are made or actions are taken; this dovetails with existing rules.
In short, while the framework doesn’t demand a perfect “white box” breakdown of each neural network layer, it does require sufficient transparency to confirm the agent stayed within its authority. Think of it like employing a human manager: the boss might not see every thought process, but they (and regulators) do see the manager’s actions, rationales, and final outcomes. The same principle applies to AI—especially in high-stakes situations where accountability can’t be abdicated.