Unlike a human judge, whose mental process is hidden, an AI dispute resolution system can be designed to provide a full audit trail. It can be required to 'show its work,' explaining its step-by-step reasoning, potentially offering more accountability than the current system allows.

To trust an agentic AI, users need to see its work, just as a manager would with a new intern. Design patterns like "stream of thought" (showing the AI reasoning) or "planning mode" (presenting an action plan before executing) make the AI's logic legible and give users a chance to intervene, building crucial trust.

A primary use case emerging for the AI Arbitrator is as an 'early case evaluation' tool. Parties can upload evidence and arguments to get an objective assessment of their position's strength. This helps them decide whether to proceed, settle, or drop the case, saving significant time and legal fees.

While AI can inherit biases from training data, those datasets can be audited, benchmarked, and corrected. In contrast, uncovering and remedying the complex cognitive biases of a human judge is far more difficult and less systematic, making algorithmic fairness a potentially more solvable problem.

The AAA strategically launched its AI arbitrator for construction disputes. This industry already uses AI, values speed over confidentiality, and provided a rich library of 'documents-only' cases to train the system in a constrained, low-risk environment before expanding.

Contrary to fears of customer backlash, data from Bret Taylor's company Sierra shows that AI agents identifying themselves as AI—and even admitting they can make mistakes—builds trust. This transparency, combined with AI's patience and consistency, often results in customer satisfaction scores that are higher than those for previous human interactions.

While correcting AI outputs in batches is a powerful start, the next frontier is creating interactive AI pipelines. These advanced systems can recognize when they lack confidence, intelligently pause, and request human input in real-time. This transforms the human's role from a post-process reviewer to an active, on-demand collaborator.

Unlike consumer chatbots, AlphaSense's AI is designed for verification in high-stakes environments. The UI makes it easy to see the source documents for every claim in a generated summary. This focus on traceable citations is crucial for building the user confidence required for multi-billion dollar decisions.

The AI model is designed to ask for clarification when it's uncertain about a task, a practice Anthropic calls "reverse solicitation." This prevents the agent from making incorrect assumptions and potentially harmful actions, building user trust and ensuring better outcomes.