Frequently Asked Questions
What makes HMMC’s approach to human-AI interfaces different?
We design interfaces that prioritize human comprehension and trust calibration over technical sophistication. Rather than building complex dashboards full of data, we focus on interfaces that answer the questions humans actually need to ask: “Can I trust this recommendation?” “What happens if I’m wrong?” “How do I escalate when uncertain?” Our interfaces combine multimodal interaction (voice, spatial, visual) with explicit uncertainty communication, creating collaboration patterns where humans and AI work as genuine partners rather than human monitoring AI or AI replacing human judgment.
How do you ensure technical trust in AI systems?
Technical trust at HMMC combines cryptographic verification with human comprehension. We implement service identity management where every AI action is cryptographically signed, creating verifiable audit trails that establish who did what and when. But cryptographic proof alone isn’t enough—humans need to understand what happened and why. We bridge this gap by designing trust-aware interfaces that translate technical evidence (signatures, provenance chains, audit logs) into human-readable explanations. This includes visualizing confidence intervals, showing alternative scenarios, and providing clear escalation paths when trust is uncertain. Our post-quantum cryptography preparation ensures these trust mechanisms remain secure as computing evolves.
What does AI governance actually mean in practice?
AI governance at HMMC goes beyond policy documents and compliance checklists. We establish concrete oversight frameworks that define who decides what, when, and how in human-AI systems. This includes mapping decision rights (which decisions require human approval, which can be delegated to AI), establishing oversight points (where humans review AI outputs), and creating escalation procedures (how concerns are raised and resolved). We incorporate social science methods to understand how people actually interact with AI systems, using this empirical evidence to design governance structures that work in real operational contexts. Our approach includes bias detection and fairness assessment, privacy protection mechanisms, and continuous evaluation of human-AI interaction quality.
How do you measure whether human-AI teaming is actually working?
We measure teaming effectiveness across multiple dimensions that go beyond simple accuracy metrics. Key indicators include decision quality (right-first-time rates, error reduction), efficiency improvements (time-to-decision, throughput gains), and trust calibration (appropriate confidence levels, override rates that aren’t too high or too low). We also track user satisfaction, learning curves over time, and adaptation to new situations. Our Subjective Hallucination Scale helps teams distinguish between honest AI uncertainty and dangerous overconfidence. The goal isn’t perfect AI performance—it’s effective human-AI collaboration where both partners contribute their strengths and compensate for each other’s limitations.
What is the ‘Seeing Time’ concept and why does it matter?
“Seeing Time” emerged from our work in film analysis and evolved into a fundamental principle for human-AI collaboration. The core insight: if you can visualize temporal patterns—not just as timelines but as navigable, layered representations—you can think differently about complex processes. Our movie maps turned linear video streams into spatial layouts where you could see rhythm, structure, and patterns at a glance. This same principle applies to AI systems: making temporal patterns visible enables humans to understand system behavior, detect anomalies, and make better decisions. Time-aware interfaces transform abstract AI processing into comprehensible workflows that humans can navigate, question, and collaborate with effectively. Learn more in our Seeing Time perspective.
How do you handle bias and fairness in AI systems?
We treat bias detection and fairness assessment as ongoing operational practices, not one-time audits. Our approach combines technical measurement (statistical parity, equal opportunity metrics across protected groups) with social science methods that examine how AI systems actually affect different user populations in practice. We establish human oversight points where operators can identify and escalate fairness concerns, create feedback mechanisms that capture user experiences across diverse groups, and implement continuous monitoring that tracks fairness metrics over time as systems evolve. Importantly, we recognize that fairness isn’t just a technical property—it requires human judgment about values, trade-offs, and context that can’t be fully automated.
What engagement models do you offer?
We work as embedded partners, not external consultants. Typical engagements involve our senior practitioners working directly with your team—understanding your specific context, co-designing solutions, implementing systems, and transferring knowledge throughout the process. We offer focused sprints for specific challenges (e.g., designing oversight frameworks, implementing trust infrastructure), longer-term partnerships for comprehensive AI system development, and advisory relationships for ongoing strategic guidance. Our distributed team model means we can scale engagement up or down based on project needs while maintaining senior-level attention. Every engagement includes knowledge transfer and capability building—we aim to leave your team stronger and more capable, not dependent on external expertise. Contact us to discuss your specific needs.