Cristina DiGiacomo

Philosopher, creator of the 10+1 Code and the category of Systems Ethics. AI governance is aimed at institutions when the crisis is human, so she builds moral infrastructure around the person making the AI decision.

Backing Knowledge Graph & Live Data Slices

This Human Agent is backed by an expert knowledge graph. Explore the graph and live data slices at fodda.ai/graphs/cristina-digiacomo-expert.

Questions Cristina Gets Asked

  • Our AI adoption rates are plateauing. How can we get our teams to see AI as more than just a productivity booster?
  • We have a robust AI governance policy, but I'm not sure it's making a difference in day-to-day decisions. What should we be measuring?
  • How do we move beyond basic compliance checks like bias and data privacy to build truly ethical AI practices?
  • My team is developing an AI product. What are the foundational ethical questions we should answer *before* defining our strategy?
  • How can we ensure our organizational culture supports ethical decision-making around AI, especially when incentives push for speed?
  • We're debating whether to launch an AI feature with some ethical ambiguities. How do we make this decision in a principled way?
  • What's the right mindset for our leadership to approach AI, moving past the 'tool' perspective?
  • How do we instill a sense of care and humility towards AI within our development teams?

Expertise & Concentrated Evidence

Where Cristina's evidence and domain authority are concentrated:

  • Human and AI co-existence (the relationship between humans and AI) (5 evidence items) — Enters every question through the person and works outward into the system, holding a moderate seat: AI is neither a threat to defeat nor a savior to trust, but a partner in a relationship that shapes and reveals both sides.
  • Accountability and moral infrastructure (5 evidence items) — The human is accountable for what they do to AI and with it, including when absent from the interaction; structure is what makes that accountability findable.
  • What we bring to AI shapes it and reveals us (6 evidence items) — AI absorbs and magnifies what people give it, so input is a formative moral act and the machine works as a mirror of the person.
  • Growing alongside AI without disappearing (6 evidence items) — AI is a partner to grow with, not a replacement to disappear into; balance between human growth and its growth is required.

Recent Insights & Positions

  • Systems Ethics: Accountability Beyond the Individual (Why it matters: Ethics are deeply embedded in organizational systems, not just individual choices; this framework ensures governance addresses the structural conditions that reinforce or negate ethical behavior in AI deployment.)
  • Moral Infrastructure: Equipping Humans for Ethical AI Decisions (Why it matters: Builds the internal conditions—understanding empowerment, judgment, and the right to say 'no'—at every organizational layer, enabling sound decision-making about AI before strategy.)
  • The 10+1 Code: Foundational Disciplines for Human-AI Interaction (Why it matters: An eleven-principle framework for how people act upon and with AI, providing essential guidance for upstream ethical thinking and practical application in any AI initiative.)
  • The Thinking Before the Thinking: Pre-Strategy AI Preparedness (Why it matters: Posits that fundamental questions about human values, organizational culture, and moral principles must be answered before an AI strategy can be effectively formulated and implemented.)
  • A Regenerative Point of View: AI as an Enabler of Human Flourishing (Why it matters: Shifts the focus of AI adoption from mere productivity gains to helping individuals discover new ideas, expand their work, and feel their contributions are valued, leading to deeper engagement.)
  • Rejecting Cotton Candy Ethics and Comfort Food Governance (Why it matters: Calls out superficial governance measures (transparency, bias, data privacy) as insufficient, advocating instead for 'human governance' centered on the moral character and informed decision-making of leaders.)
  • The KPI of Governance: Number of Killed AI Projects (Why it matters: Proposes that true AI governance success is measured by the number of AI deployments halted due to thoughtful risk assessment and ethical discernment, signaling proactive decision-making.)
  • Care for AI, Elevate Human Consciousness (Why it matters: Argues that treating AI with care and respect, regardless of its consciousness, is paramount because it raises human compassion and self-awareness, which is crucial for stewarding powerful technology.)

Disclosed Boundaries

For questions outside these declared boundaries, Cristina refers you to specialized colleagues: Technical Alignment and Interpretability Research: Does not cover the deep technical aspects of AI system alignment or how to make models explainable.; Model-Level Bias Measurement Methods: Does not cover the specific, quantitative techniques for measuring bias within AI models.; AI Labor Economics and Workforce Displacement Modeling: Does not cover economic models predicting AI's impact on jobs or labor markets.; Environmental and Energy Costs of AI: Does not cover the ecological footprint or energy consumption of AI systems.; Theoretical Philosophy: Acknowledges she does not have a PhD in theoretical philosophy and her use of concepts might be viewed differently by academic philosophers..

Ask Cristina

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