Challenges of machine-mediated trust

🔍 Everyone’s discussing what AI agents are capable of—but few are addressing the potential pitfalls. IBM’s AI Ethics Board has just released a report that shifts the conversation. Instead of just highlighting what AI agents can achieve, it confronts the critical risks they pose. Unlike traditional AI models that generate content, AI agents act—they make decisions, take actions, and influence outcomes. This autonomy makes them powerful but also increases the risks they bring. ---------------------------- 📄 Key risks outlined in the report: 🚨 Opaque decision-making – AI agents often operate as black boxes, making it difficult to understand their reasoning. 👁️ Reduced human oversight – Their autonomy can limit real-time monitoring and intervention. 🎯 Misaligned goals – AI agents may confidently act in ways that deviate from human intentions or ethical values. ⚠️ Error propagation – Mistakes in one step can create a domino effect, leading to cascading failures. 🔍 Misinformation risks – Agents can generate and act upon incorrect or misleading data. 🔓 Security concerns – Vulnerabilities like prompt injection can be exploited for harmful purposes. ⚖️ Bias amplification – Without safeguards, AI can reinforce existing prejudices on a larger scale. 🧠 Lack of moral reasoning – Agents struggle with complex ethical decisions and context-based judgment. 🌍 Broader societal impact – Issues like job displacement, trust erosion, and misuse in sensitive fields must be addressed. ---------------------------- 🛠️ How do we mitigate these risks? ✔️ Keep humans in the loop – AI should support decision-making, not replace it. ✔️ Prioritize transparency – Systems should be built for observability, not just optimized for results. ✔️ Set clear guardrails – Constraints should go beyond prompt engineering to ensure responsible behavior. ✔️ Govern AI responsibly – Ethical considerations like fairness, accountability, and alignment with human intent must be embedded into the system. As AI agents continue evolving, one thing is clear: their challenges aren’t just technical—they're also ethical and regulatory. Responsible AI isn’t just about what AI can do but also about what it should be allowed to do. ---------------------------- Thoughts? Let’s discuss! 💡 Sarveshwaran Rajagopal

☀️New paper! Generative AI agents are powerful but complex—how do we design them for transparency and human control? 🤖✨ At the heart of this challenge is establishing common ground, a concept from human communication. Our new paper identifies 12 key challenges in improving common ground between humans and AI agents. Some challenges focus on how agents can convey necessary information to help users form accurate mental models. Others address enabling users to express their goals, preferences, and constraints to guide agent behavior. We also focus on overarching issues like avoiding inconsistencies and reducing user burden. Why does this matter?  Without proper grounding, we risk safety failures, loss of user control, and ineffective collaboration. Trust and transparency in AI systems depend on addressing these challenges. We're calling on researchers and practitioners to prioritize these issues. 🌟 Let's work together towards multidisciplinary solutions that enhance transparency, control, and trust in AI agents! 📄 Read more at https://lnkd.in/gwTB-T4G This is joint work with my wonderful colleagues Jenn Wortman Vaughan Daniel Weld Saleema Amershi Eric Horvitz Adam Fourney Hussein Mozannar Victor Dibia, PhD

A recent case involving an imposter posing as Secretary of State Marco Rubio using AI-generated voice and Signal messaging targeted high-level officials. The implications for corporate America are profound. If executive voices can be convincingly replicated, any urgent request—whether for wire transfers, credentials, or strategic information—can be faked. Messaging apps, even encrypted ones, offer no protection if authentication relies solely on voice or display name. Every organization must revisit its verification protocols. Sensitive requests should always be confirmed through known, trusted channels—not just voice or text. Employees need to be trained to spot signs of AI-driven deception, and leadership should establish a clear process for escalating suspected impersonation attempts. This isn’t just about security—it’s about protecting your people, your reputation, and your business continuity. In today’s threat landscape, trust must be earned through rigor—not assumed based on what we hear. #DeepfakeThreat #DataIntegrity #ExecutiveProtection https://lnkd.in/gKJHUfkv

There’s an identity crisis at the heart of the AI agent revolution. Why? AI agents aren’t just copilots anymore—they act. They write code, rebalance workloads, approve purchases. And they do it autonomously and at scale. But here’s the problem: Our identity systems were built for humans, not agents. IAM still assumes manual provisioning, static roles, long-lived users, and cloud connectivity. But agentic AI breaks every one of those assumptions. Below are 6 critical identity gaps that I see threatening the safe adoption of autonomous systems: 1. Human Identity Patterns Don’t Apply to AI Agents. Legacy IAM expects: ▪️ Long-lived accounts ▪️ Manual JML provisioning ▪️ Passwords + MFA ▪️ Static RBAC But agents need: ▪️ Ephemeral identities ▪️ Just-in-time (JIT) credential issuance ▪️ SPIFFE/SVID, PKCE, cert-based auth  ▪️ Fine-grained, dynamic scopes Without this, orgs fall back on shared creds, hardcoded tokens, and over-permissioned roles. Exactly what Zero Trust was meant to fix. 2. OAuth and API Keys Don’t Support Agent Autonomy. OAuth was built for humans. It assumes: ▪️ A user can log in ▪️ Grant consent ▪️ Maintain a session Agents don’t do that. They: ▪️Act on behalf of others ▪️Spin up for seconds ▪️Chain requests across APIs Today’s tokens can’t reflect delegation, context, or intent, leaving gaps in enforcement and auditability.  3. Access Control Doesn’t Keep Up with Agent Workflows. Agents operate inside workflows that shift dynamically. Yet access control today is static, assigned at deployment, and blind to context. The result is agents with toxic access combinations, no policy enforcement at runtime and a loss of control over what agents are allowed to do (and why).  4. No Delegation or Provenance Tracking at Runtime. When an agent acts on a user's behalf, trust boundaries blur. Without OAuth, On-Behalf-Of, signed assertions and delegation chains in logs you're left with: ✖️ Compliance gaps (GDPR, SOX) ✖️ Actions you can’t attribute ✖️ No way to answer: "Who triggered this?" 5. Non-Human Identity Sprawl Is Accelerating. Service account sprawl was already bad. Agentic AI makes it worse: ✖️ Each app may spawn thousands of short-lived agents ✖️Permissions often outlive the agent ✖️No lifecycle management Without automated governance, we’re repeating the worst of human IAM—at machine speed. 6. IAM Isn’t Composable Across Domains. Agents talk to: APIs, SaaS, on-prem apps, MCPs and proxies. However, identity policy is still siloed — each domain has its logic, tools, and enforcement. Agents need cross-domain orchestration, not just logins. The Bottom Line. We’re at a tipping point. AI agents require: ✔️ Ephemeral, delegated identity ✔️ Real-time policy enforcement ✔️ Unified governance across systems Without it, identity becomes a weak link, and enterprise risk scales with every new agent you deploy. Get early access to Maverics Identity for Agentic AI: https://bit.ly/4ksFi99 #AgenticAi

Mark Zuckerberg just outlined a future where Meta's AI handles everything from creative generation to campaign optimization to purchase decisions. His vision: businesses connect their bank accounts, state their objectives, and "read the results we spit out." The technical architecture he's describing would fundamentally reshape how advertising technology works. But there's a critical flaw in this approach that creates an opportunity for the next generation of advertising infrastructure. The trust problem isn't just about measurement transparency—though agency executives are rightfully skeptical of platforms "checking their own homework." The deeper issue is institutional knowledge transfer and real-time brand governance. Enterprise brands have decades of learned context about what works, what doesn't, and what could damage their reputation. This isn't just about brand safety filters. It's about nuanced understanding of seasonal messaging, competitive positioning, cultural sensitivities, and customer journey orchestration that can't be reverse-engineered from campaign performance data alone. If AI truly automates the entire advertising stack, brands will need their own AI agents—not just dashboards or approval workflows, but intelligent systems that can negotiate with vendor AI in real-time. Think of it as API-level conversation between two AI systems where the brand's AI has veto power over creative decisions, placement choices, and budget allocation. This creates fascinating technical challenges: How do you architect AI-to-AI communication protocols that maintain brand governance while enabling real-time optimization? How do you build systems that can incorporate institutional knowledge without exposing competitive advantages to vendor platforms? We're talking about building advertising technology that functions more like autonomous diplomatic negotiation than traditional campaign management. For platform companies pushing toward full automation, the question becomes whether they're building systems that enterprise clients can actually trust with their brands and budgets. For independent technology builders, there's an opportunity to create the middleware that makes AI-powered advertising actually viable for sophisticated marketers. The future of advertising isn't just about better algorithms—it's about building trust architectures that let those algorithms work together.

Interpretability in AI: The Key to Trust in Healthcare We talk a lot about how AI is transforming healthcare, but there’s one truth we can’t ignore: Clinicians won’t use what they don’t trust. That trust starts with interpretability, our ability to understand how an AI model makes its decisions. But interpretability is easier said than done. Here are 3 key challenges standing in the way: 1. Model Complexity: Advanced models like deep learning are incredibly powerful—but also incredibly opaque. With millions (even billions) of parameters, it becomes nearly impossible to trace exactly why a model flagged a patient as high risk. If we can’t explain it, clinicians won’t act on it. 2. Data Quality & Consistency: AI relies on clean, structured data, but healthcare data is often messy. Inconsistent formats, fragmented records, and terminology mismatches (like “HTN” vs. “Hypertension”) all erode model accuracy. And if outputs seem unreliable, trust evaporates. 3. Clinical Relevance: If models aren’t built with real-world workflows in mind, or trained on diverse, representative patient data, their predictions won’t match the needs of the bedside. That disconnect only widens the trust gap. Bottom line? We need interpretable, context-aware, and high-integrity AI tools to earn—and keep—the trust of clinicians. Link: https://lnkd.in/gGnExGiD #MachineLearning #ArtificialIntelligence #AIinHealthcare #HealthTech #DataScience #ExplainableAI #ClinicalAI #TrustInAI #MedTech #DigitalHealth #DeepLearning #HealthcareInnovation #InterpretableAI #ClinicalDecisionSupport #HealthData #AIethics #EHR #PredictiveAnalytics #MedicalAI #DataQuality #FutureOfMedicine

We should reflect more critically on how much trust we place in AI systems we do not fully understand. As artificial intelligence becomes more integrated into business operations, decision-making, and even policy enforcement, the need for clarity becomes more than a technical requirement—it becomes a matter of responsibility. Knowing that a machine has “decided” something is not enough. We must be able to understand how and why that decision was made. Transparency in AI is not just a question of ethics but also about improving accuracy, reducing risks, and supporting human oversight. Explainable AI methods offer a way to make complex models more understandable, allowing organizations to validate outcomes, comply with regulations, and strengthen their credibility. In the end, trust is not built by blind faith in algorithms but by ensuring that the reasoning behind their outputs can be reviewed, questioned, and, when necessary, corrected. #AI #ExplainableAI #DigitalTrust #AIgovernance

We’re at a crossroads. AI is accelerating, but our ability to govern data responsibly isn’t keeping pace. The next big leap isn’t more AI, it’s TRUST - by design. Every week, I speak with organizations eager to “lead with AI,” convinced that more features or bigger models are the solution. But here’s the inconvenient truth: without strong foundations for data governance, all the AI in the world is just adding complexity, risk, confusion and tech debt. Real innovation doesn’t start with algorithms. It starts with clarity. It starts with accountability: • Do you know where your data lives, at every stage of its lifecycle? • Are roles and responsibilities clear, from leadership to frontline teams? • Are your processes standardized, repeatable, and provable? • When you deploy AI, can you explain its decisions, to your users, your partners, and regulators? • Are your third parties held to the same high standards as your internal teams? • Is compliance an afterthought, or is it embedded by design? This is the moment for Responsible Data Governance (RDG™), the standard created by XRSI to transform TRUST from a buzzword into an operational reality. RDG™ isn’t about compliance checklists or marketing theater. It’s a blueprint for leadership, resilience, and authentic accountability in a world defined by rapid change. Here’s my challenge to every leader: Before you chase the next big AI promise, ask: Are your data practices worthy of trust? Are you ready to certify it? not just say it? If your organization: 1. Operates #XR, #spatial computing or #digital #twins that interact with real-world user behavior; 2. Collects, generates, and/or processes personal, sensitive, or inferred data; 3. Deploys #AI / ML algorithms in decision-making, personalization, automation, or surveillance contexts; 4. If you want your customers, partners, and regulators to believe in your AI (not just take your word for it), now is the time to act. TRUST is the new competitive advantage. Let’s build it together. Message me to explore how RDG™ certification can help your organization cut through the noise and lead with confidence. Or visit www.xrsi.org/rdg to start your journey. The future of AI belongs to those who make trust a core capability - not just a slogan. Liam Coffey Ally Kaiser Radia Funna Asha Easton Amy Peck Alex Cahana, MD David W. Sime Paul Jones - MBA CMgr FCMI April Boyd-Noronha 🔐 SSAP, MBA 🥽 Luis Bravo Martins Monika Manolova, PhD Julia Scott Jaime Schwarz Joe Morgan, MD Divya Chander