Following my session at Marketplace Risk Austin in February 2026, I'm proposing a talk built around the framework from my upcoming book, Awareness Equity (publishing September 10, 2026, just days before #MRNYC26). Every trust & safety and risk team is being asked to do more with AI — faster fraud detection, faster abuse response, faster scale. But productivity gains in AI tooling aren't translating into the business outcomes leadership expects. The reason isn't the technology. It's a structural information gap I call the Awareness Equity Gap: the right information exists somewhere in the organization, but it never reaches the person who needs it, in time, in a form they can act on. This session names the specific pattern behind why trust & safety teams — deeply skilled, deeply passionate — struggle to translate daily, hard-won operational knowledge into the kind of business case that earns infrastructure-level investment. It draws on 25 years of organizational research, conversations across this exact community, and the framework from my book. Attendees will leave with one practical question to ask inside their own organization — the same question I leave every audience with — that surfaces exactly where their highest-risk information gap is hiding.

This session will explore how standards can support a consistent, accountable, and rights-respecting approach to online safety. The goal is to talk about how safety standards can work across the AI and Internet stack while supporting human rights, and a more trustworthy digital environment. During the session, participants will examine where current safety standards are useful, where they need to be adapted for AI and different online services, and how standards can help preserve accountability without locking in overly rigid or one-size-fits-all approaches.

Take this time to visit the exhibitor space and connect with the solution providers and vendors on site. Explore the latest innovations in trust & safety, risk management, and legal compliance — all designed to help protect your platform, your users, and your business. Discover essential tools, build valuable connections, and stay ahead of emerging risks.

With over 1 billion reviews published on Google Maps annually, we invest heavily in protections to keep content trustworthy. In this session, we use negative review extortion as a case study in how we adapt to emerging threats. We detail the multi-layered approach we use to block abuse before it reaches users, from deploying specialized AI models and integrating community feedback, to introducing responsible friction to disrupt scammer economics.

Agentic AI refers to autonomous systems powered by Large Language Models (LLMs) that can perceive environmental states, reason through complex decisions, and execute actions to achieve business objectives with minimal human intervention. It can assist in building a scalable framework for autonomous decision-making in business applications. This paper proposes a framework for designing and implementing autonomous agents that solve real-world business problems. The extensive pretraining of LLMs enables agents to navigate vast problem spaces more effectively, facilitating accelerated problem-solving. The methodology adopts a modular architecture that divides the complex business problems into sub-tasks, each of which can be handled by sub-agents. Furthermore, the integration of learning and adaptation mechanisms allows these systems to modify their responses to environmental feedback, thus improving performance over time. Our findings indicate that while Agentic AI outperforms manual baseline and traditional rule-based approaches in terms of operational speed and adaptability, challenges related to LLM stochastic behavior in edge cases persist, necessitating human-AI collaboration. Ultimately, this architecture enables the deployment of autonomous decision-making agents capable of navigating the ever-evolving business requirements of modern enterprises.