John Danaher assesses how AI may reshape ethical and social norms, minds the anticipatory gap in regulation, and applies the MVPP to decide against digitizing himself.

John parlayed an interest in science fiction into researching legal philosophy, emerging technology, and society. Flipping the script on ethical assessment, John identifies six (6) mechanisms by which technology may reshape ethical principles and social norms. John further illustrates the impact AI can have on decision sets and relationships. We then discuss the dilemma articulated by the aptly named anticipatory gap. In which the effort required to regulate nascent tech is proportional to our understanding of its ultimate effects.

Finally, we turn our attention to the rapid rise of digital duplicates. John provides examples and proposes a Minimally Viable Permissibility Principle (MVPP) for evaluating the use of digital duplicates. Emphasizing the difficulty of mitigating the risks posed after a digital duplicate is let loose in the wide, John declines the opportunity to digitally duplicate himself.

John Danaher is a Sr. Lecturer in Ethics at the NUI Galway School of Law. A prolific scholar, he is the author of Automation and Utopia: Human Flourishing in a World Without Work (Harvard University Press, 2019). Papers referenced in this episode include The Ethics of Personalized Digital Duplicates: A Minimal Viability Principle and How Technology Alters Morality and Why It Matters.

A transcript of this episode is here.

Ben Bland expressively explores emotive AI’s shaky scientific underpinnings, the gap between reality and perception, popular applications, and critical apprehensions.

Ben exposes the scientific contention surrounding human emotion. He talks terms (emotive? empathic? not telepathic!) and outlines a spectrum of emotive applications. We discuss the powerful, often subtle, and sometimes insidious ways emotion can be leveraged. Ben explains the negative effects of perpetual positivity and why drawing clear red lines around the tech is difficult.

He also addresses the qualitative sea change brought about by large language models (LLMs), implicit vs explicit design and commercial objectives. Noting that the social and psychological impacts of emotive AI systems have been poorly explored, he muses about the potential to actively evolve your machine’s emotional capability.

Ben confronts the challenges of defining standards when the language is tricky, the science is shaky, and applications are proliferating. Lastly, Ben jazzes up empathy as a human superpower. While optimistic about empathic AI’s potential, he counsels proceeding with caution.

Ben Bland is an independent consultant in ethical innovation. An active community contributor, Ben is the Chair of the IEEE P7014 Standard for Ethical Considerations in Emulated Empathy in Autonomous and Intelligent Systems and Vice-Chair of IEEE P7014.1 Recommended Practice for Ethical Considerations of Emulated Empathy in Partner-based General-Purpose Artificial Intelligence Systems.

A transcript of this episode is here.

Philip Rathle traverses from knowledge graphs to LLMs and illustrates how loading the dice with GraphRAG enhances deterministic reasoning, explainability and agency.

Philip explains why knowledge graphs are a natural fit for capturing data about real-world systems. Starting with Kevin Bacon, he identifies many ‘graphy’ problems confronting us today. Philip then describes how interconnected systems benefit from the dynamism and data network effects afforded by knowledge graphs.

Next, Philip provides a primer on how Retrieval Augmented Generation (RAG) loads the dice for large language models (LLMs). He also differentiates between vector- and graph-based RAG. Along the way, we discuss the nature and locus of reasoning (or lack thereof) in LLM systems. Philip articulates the benefits of GraphRAG including deterministic reasoning, fine-grained access control and explainability. He also ruminates on graphs as a bridge to human agency as graphs can be reasoned on by both humans and machines. Lastly, Philip shares what is happening now and next in GraphRAG applications and beyond.

Philip Rathle is the Chief Technology Officer (CTO) at Neo4j. Philip was a key contributor to the development of the GQL standard and recently authored The GraphRAG Manifesto: Adding Knowledge to GenAI (neo4j.com) a go-to resource for all things GraphRAG.

A transcript of this episode is here.

Matthew Scherer makes the case for bottom-up AI adoption, being OK with not using AI, innovation as a relative good, and transparently safeguarding workers’ rights.

Matthew champions a worker-led approach to AI adoption in the workplace. He traverses the slippery slope from safety to surveillance and guards against unnecessarily intrusive solutions.

Matthew then illustrates why AI isn’t great at making employment decisions; even in objectively data rich environments such as the NBA. He also addresses the intractable problem of bias in hiring and flawed comparisons between humans and AI. We discuss the unquantifiable dynamics of human interactions and being OK with our inability to automate hiring and firing.

Matthew explains how the patchwork of emerging privacy regulations reflects cultural norms towards workers. He invokes the Ford Pinto and the Titan submersible catastrophe when challenging the concept of innovation as an intrinsic good. Matthew then makes the case for transparency as a gateway to enforcing existing civil rights and laws.

Matthew Scherer is a Senior Policy Counsel for Workers' Rights and Technology at the Center for Democracy and Technology (CDT). He studies how emerging technologies affect workers in the workplace and labor market. Matt is also an Advisor for the International Center for Advocates Against Discrimination.

A transcript of this episode is here.

Heidi Lanford connects data to cocktails and campaigns while considering the nature of data disruption, getting from analytics to AI, and using data with confidence.

Heidi studied mathematics and statistics and never looked back. Reflecting on analytics then and now, she confirms the appetite for data has never been higher. Yet adoption, momentum and focus remain evergreen barriers. Heidi issues a cocktail party challenge while discussing the core competencies of effective data leaders.

Heidi believes data and CDOs are disruptive by nature. But this only matters if your business incentives are properly aligned. She revels in agile experimentation while counseling that speed is not enough. We discuss how good old-fashioned analytics put the right pressure on the foundational data needed for AI.

Heidi then campaigns for endemic data literacy. Along the way she pans JIT holiday training and promotes confident decision making as the metric that matters. Never saying never, Heidi celebrates human experts and the spotlight AI is shining on data.

Heidi Lanford is a Global Chief Data & Analytics Officer who has served as Chief Data Officer (CDO) at the Fitch Group and VP of Enterprise Data & Analytics at Red Hat (IBM). In 2023, Heidi co-founded two AI startups LiveFire AI and AIQScore. Heidi serves as a Board Member at the University of Virginia School of Data Science, is a Founding Board Member of the Data Leadership Collaborative, and an Advisor to Domino Data Labs and Linea.

A transcript of this episode is here.

Marianna B. Ganapini contemplates AI nudging, entropy as a bellwether of risk, accessible ethical assessment, ethical ROI, the limits of trust and irrational beliefs.

Marianna studies how AI-driven nudging ups the ethical ante relative to autonomy and decision-making. This is a solvable problem that may still prove difficult to regulate. She posits that the level of entropy within a system correlates with risks seen and unseen. We discuss the relationship between risk and harm and why a lack of knowledge imbues moral responsibility. Marianna describes how macro-level assessments can effectively take an AI system’s temperature (risk-wise). Addressing the evolving responsible AI discourse, Marianna asserts that limiting trust to moral agents is overly restrictive. The real problem is conflating trust between humans with the trust afforded any number of entities from your pet to your Roomba. Marianna also cautions against hastily judging another’s beliefs, even when they overhype AI. Acknowledging progress, Marianna advocates for increased interdisciplinary efforts and ethical certifications.

Marianna B. Ganapini is a Professor of Philosophy and Founder of Logica.Now, a consultancy which seeks to educate and engage organizations in ethical AI inquiry. She is also a Faculty Director at the Montreal AI Ethics Institute and Visiting Scholar at the ND-IBM Tech Ethics Lab .

A transcript of this episode is here.

Miriam Vogel disputes AI is lawless, endorses good AI hygiene, reviews regulatory progress and pitfalls, boosts literacy and diversity, and remains net positive on AI.

Miriam Vogel traverses her unforeseen path from in-house counsel to public policy innovator. Miriam acknowledges that AI systems raise some novel questions but reiterates there is much to learn from existing policies and laws. Drawing analogies to flying and driving, Miriam demonstrates the need for both standardized and context-specific guidance.

Miriam and Kimberly then discuss what constitutes good AI hygiene, what meaningful transparency looks like, and why a multi-disciplinary mindset matters. While reiterating the business value of beneficial AI Miriam notes businesses are now on notice regarding their AI liability. She is clear-sighted regarding the complexity, but views regulation done right as a means to spur innovation and trust. In that vein, Miriam outlines the progress to-date and work still to come to enact federal AI policies and raise our collective AI literacy. Lastly, Miriam raises questions everyone should ask to ensure we each benefit from the opportunities AI presents.

Miriam Vogel is the President and CEO of Equal AI, a non-profit movement committed to reducing bias and responsibly governing AI. Miriam also chairs the US National AI Advisory Committee (NAIAC).

A transcript of this episode is here.

Melissa Sariffodeen contends learning requires unlearning, ponders human-AI relationships, prioritizes outcomes over outputs, and values the disquiet of constructive critique.

Melissa artfully illustrates barriers to innovation through the eyes of a child learning to code and a seasoned driver learning to not drive. Drawing on decades of experience teaching technical skills, she identifies why AI creates new challenges for upskilling. Kimberly and Melissa then debate viewing AI systems through the lens of tools vs. relationships. An avowed lifelong learner, Melissa believes prior learnings are sometimes detrimental to innovation. Melissa therefore advocates for unlearning as a key step in unlocking growth. She also proposes a new model for organizational learning and development. A pragmatic tech optimist, Melissa acknowledges the messy middle and reaffirms the importance of diversity and critically questioning our beliefs and habits.

Melissa Sariffodeen is the founder of the The Digital Potential Lab, co-founder and CEO of Canada Learning Code and a Professor at the Ivey Business School at Western University where she focuses on the management of information and communication technologies.

A transcript of this episode is here.