Can Your Clients Trust Your AI Tools?

As AI transforms how real estate professionals analyze markets, serve clients, and close deals, one question stands out: Can we trust AI to make decisions that are fair, transparent, and safe?

At a recent HumanX event, industry and policy leaders tackled this challenge directly, introducing the RAIL framework and exploring how responsible AI practices can guide innovation across sectors — including real estate and proptech.

For MLS executives, broker-owners, and technology vendors, the message was clear: Build trust into AI systems now — before clients, regulators, or competitors demand it.

Why Trust in AI Matters More Than Ever

Opening the event, HumanX’s Stefan Weitz put it bluntly: AI isn’t just powerful — it’s a force that moves faster and cuts deeper than any technology before it.

Unlike cars or electricity, which scaled only after damage and regulation, AI affects nearly everyone, instantly — and misuse may be too costly to fix after the fact.

In real estate, AI already influences property valuations, lead scoring, contract analysis, listing recommendations, and compliance workflows. If these systems are opaque or biased, they risk eroding trust — not just in the technology, but in the professionals who use it.

Introducing RAIL: A Framework for Responsible AI

To address this, HumanX introduced RAIL — the Responsible AI Lifecycle — a pioneering framework that helps companies build trust, fairness, and accountability into their AI systems from design through deployment.

RAIL’s Core Principles:

• Transparency: AI systems should be understandable. Agents, clients, and regulators need to know how decisions are made and what data is used.

• Fairness: AI must avoid bias and discrimination, especially in sensitive areas like housing, lending, and pricing.

• Accountability: Companies must take responsibility for AI outcomes and ensure oversight, appeals, and corrections are possible.

• Trustworthiness: Trust is earned through consistent, ethical performance and ongoing evaluation — not assumed.

The Lifecycle Approach:

The RAIL framework outlines practical steps across four key phases:

• Design: Define clear goals, set ethical guidelines, and engage diverse stakeholders.

• Development: Use high-quality, unbiased data; document processes; integrate safety checks.

• Deployment: Monitor real-world performance; enable feedback loops; ensure transparency for users.

• Governance: Establish policies for audits, risk management, and regulatory compliance.

For proptech firms and MLSs, RAIL is more than a framework — it’s a blueprint for aligning AI tools with business goals and ethical standards.

What RAIL Means for Brokers, MLSs, and Tech Vendors

Nuno Sebastião, CEO of Feedzai (a HumanX partner), emphasized a core belief: AI should serve people, not just profits. He proposed an AI “trust score” — similar to a FICO score — to assess model reliability.

In real estate, such a score could evaluate whether tools for home value estimates, lead generation, or financing recommendations are truly trustworthy.

Sebastião also challenged the notion that responsible AI slows innovation. In fact, companies like DeepSeek have achieved breakthroughs while operating under strict responsibility standards — proving innovation and accountability can (and must) coexist.

For real estate leaders, the call to action is clear:

• MLSs should audit AI features for bias, transparency, and accountability — especially tools affecting property search, pricing, and syndication.

• Brokers and team leaders should vet third-party AI tools (CRMs, CMA software, ad targeting) for compliance with emerging trust standards.

• Vendors should adopt RAIL-aligned practices and be ready to demonstrate fairness, transparency, and governance to clients.

AI Regulation Is Coming — Be Ready

Congressman Jay Obernolte, co-leader of the U.S. House AI Task Force and a rare AI expert in government, provided insight into the regulatory landscape:

• AI is already regulated sector by sector. Housing, lending, and data privacy are likely next for AI-specific scrutiny.

• The U.S. will favor incremental, practical regulation — unlike the EU AI Act — which means industry leadership is essential.

• If businesses don’t define responsible AI standards now, policymakers and courts will do it for them.

His message: Don’t wait. Adopt frameworks like RAIL now and build trust by design.

From Words to Actions: The Rise of Action AI

A key insight from HumanX: Most AI today focuses on text generation (chatbots, summaries), but 90% of human work is action-based.

Real estate is no exception — agents and brokers need tools that act, not just talk.

AI is evolving into Action AI — tools that don’t just suggest but act intelligently and automatically. In real estate, this means:

• AI-powered scheduling, automated document generation, and instant compliance alerts.

• AI agents that guide clients through transactions or manage follow-ups.

• Adaptive workflows that respond in real time to client behavior or market shifts.

To thrive in this landscape, MLSs and brokerages need platforms that support agentic AI — tools that act ethically, safely, and intelligently.

Final Thought: Trust is the Competitive Advantage

In real estate, trust is the foundation of every transaction. As AI adoption grows, clients and agents will ask: Can I trust the systems making these decisions?

RAIL offers a clear path forward — a responsible, transparent, and actionable approach to AI that builds confidence, reduces risk, and encourages long-term adoption.

Real estate professionals who lead with trust — by embracing frameworks like RAIL — won’t just stay ahead of regulation. They’ll stand out in a crowded, tech-driven market.