Responsible AI Summit Main Conference Day 2 - Tuesday 22 September

Explainability is often treated as a compliance afterthought; at Wiley, it is a system design requirement. At the scale of one of the world's largest scholarly publishers, that means AI outputs must be traceable, transparent about what models are trained on, clear on where human intervention sits in the workflow, and explicit about how updates and corrections are managed over time. In this session, Pascal Hetzscholdt, Director of AI Strategy and Content Integrity, provides a candid, technical look at how enabling students and researchers to prompt directly against curated, licensed content materially reduces hallucinations.

• Addressing cost pressures that drive unseen model changes.

• Securing meaningful quick wins while protecting long- term information integrity.

• Strengthening institutional situational awareness.

Pascal Hetzscholdt

Senior Director of AI Strategy and Content Integrity
Wiley

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Since 2019, Intesa Sanpaolo has been building an AI literacy programme designed to make AI accessible, practical, and responsible for employees. Structured around three pillars (Culture, Awareness, and Dissemination) the initiative combines role-based learning, expert-led discussions, and a voluntary AI community that now engages more than 7,000 employees through knowledge sharing, real-world use cases, and peer-to-peer collaboration. In this session, Federico Aguggini, Head of AI Transformation, Data Science & AI, will share the strategy, lessons learned, and measurable impact of scaling AI literacy across one of Europe's leading banking groups.

• Building AI literacy through a three-pillar strategic framework.

• Creating sustained engagement through a 7,000-employee AI community.

• Embedding responsible AI across employees, clients, and society.

Federico Aguggini

Head of AI Transformation – Data Science & Responsible AI
Intesa Sanpaolo

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AI literacy is the operating layer of responsible AI: shaping how teams interpret insights, exercise judgment, and maintain human oversight as AI becomes embedded in workflows. Moving beyond static adoption metrics, organisations must focus on cultural readiness and measurable capability shifts that influence governance and decision quality. This session explores practical approaches to literacy and culture so that AI deployment strengthens accountability and operational performance rather than simply increasing usage.

• Measuring literacy and decision impact beyond adoption metrics.

• Cultivating culture and shared understanding for responsible outcomes.

• Integrating literacy with operations to strengthen oversight and judgment.

James Fletcher

Responsible AI Lead
BBC

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Philippa Penfold

Responsible AI & Data Science Manager
Elsevier

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Ray Eitel-Porter

Senior Research Associate, The Intellectual Forum
Jesus College Cambridge

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Emily Campbell-Ratcliffe

Head of AI for Local Growth
Department for Science, Innovation, and Technology

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AI systems in high-stakes domains such as finance must remain reliable and trustworthy as new capabilities emerge at scale. In this session, Dr Stuart Burrell, Director of AI Research & Innovation, and Dr Maeve Madigan, Senior Research Scientist at Visa, share two research advances that help build safe and responsible AI systems at global payments scale. The first looks at research published at AISTATS 2025 and ICLR 2026 on detecting machine-generated text and what it takes to make such methods robust in real-world adversarial settings. Dr Madigan then turns to fairness, focusing on how bias can arise not only from individual models but as an emergent, collective property of complex multi-agent AI systems. Drawing on research published as a Spotlight paper at the 2026 AAAI Workshop on Agentic AI in Financial Services, she examines how these dynamics unfold, and what we can learn from large scale simulation studies in this area.

• Reliable detection of machine-generated text enhances safety and trust in many high-stakes domains.

• Bias can emerge as a collective property of complex multi-agent AI systems, beyond any single model.

• Principled safety and fairness research enables trustworthy AI at production scale.

Stuart Burrell

Director of AI Research and Innovation
VISA

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Every organisation says data is strategic, yet most still run on brittle pipelines, stale datasets, and underfunded infrastructure. As automation, real time AI systems, and agentic workflows explode, the gap between what models need and what data teams receive is widening fast. In this panel, speakers will unpack what “automated data” truly requires today, how to quantify its ROI, and how to build foundations that make Responsible AI actually possible.

Andy Chi

Senior Legal Counsel and Data & AI Governance Lead
SHEIN

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Detlef Nauck

Head of AI and Data Science Research
BT Group

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Mark Beckwith

Director of Data Governance & Architecture
Financial Times

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The EU AI Act mandates human oversight, yet in practice, this often becomes a junior staff becoming an “AI checker” rubber-stamping automated outputs. In this candid session, Dr David Crelley, Head of Responsible AI & Data at Admiral Group, challenges the compliance-driven interpretation of human-in-the-loop and examines why oversight designed for efficiency frequently undermines effectiveness and accountability. He will share how his team is rethinking oversight as proactive engagement, using judge LLMs to do the hard lifting, deliberately introducing friction, cultural ownership, and meaningful intervention points into AI-enabled processes. David offers practical insight into how to design oversight models that create genuine human engagement rather than passive validation.

• Moving from passive validation to active engagement.

• Using LLMs to do the basic checks.

• Designing friction to strengthen human judgement.

• Embedding cultural ownership into AI oversight.

David Crelley

Head of Responsible AI and Data
Admiral Group

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As AI systems become more conversational, expressive, and agentic, the push to humanise them is accelerating. In China, widespread deployment of highly human-like AI across daily life has already triggered regulatory action aimed at curbing over-anthropomorphism and protecting users from misplaced trust. Europe now has a choice: learn early or regulate late. In this session, Sarah Mathews, Group Responsible AI Manager at Adecco Group, explores what responsible design looks like before AI systems blur the line between tool and perceived actor, and how transparency, human-centricity, and governance must evolve as agentic capabilities scale.

• Anticipating regulatory risk from humanised AI systems.

• Designing transparency into increasingly agentic experiences.

• Protecting human agency as AI embeds into daily life.

Sarah Mathews

Group Responsible AI Manager
The Adecco Group

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As AI increasingly shapes core product, operational and strategic decisions, many organisations still treat ethics as a policy artefact or compliance exercise - often detached from the reality of day to day decision-making. Roos Brekelmans, Digital Innovation Accelerator at Vattenfall, explores the ethical, strategic and trust risks created by this gap. Rather than framing ethical AI as a control or governance problem, she reframes it as a practical business discipline - and a conversation skill. Drawing on hands-on innovation experience and academic foundations in technology ethics, this session moves beyond abstract principles to focus on concrete, repeatable practices that teams and leaders can embed directly into their innovation pipelines. Using AI responsibly is positioned not as a brake on progress, but as a lever for resilient, scalable, and strategically sound innovation.

• Embedding ethics into everyday and strategic business decisions.

• Using values to navigate real AI trade-offs under uncertainty.

• Linking responsible innovation directly to enterprise value and trust at scale.

Roos Brekelmans

Digital Innovation Accelerator
Vattenfall

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Generative AI is accelerating across insurance, bringing new capabilities and new risks to underwriting, claims, and customer operations. In this evolving environment, model risk functions must ensure that governance frameworks, validation processes, and lifecycle controls can adapt without losing rigour.

Nadeem Chaudhry

Head of AI Governance, Controls and Assurance
Aviva

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Roberto Martin-Reguera

Group Model Validation Director
Aviva

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Pandora uses AI to drive innovation, efficiency, and better decision making. But "trust" doesn't emerge by default, it has to be intentional. This session shares how Responsible AI becomes real through clear red lines, accountable ownership, and governance that works across teams and vendors. Using a retail chatbot example, Jennifer Cheung, AI Enablement & Responsible AI Data Scientist and Catarina Runa Miranda, Global Data & AI Enablement and Architecture Lead, examine how responsible AI controls are embedded across the lifecycle. They cover how they align with the EU AI Act in plain language, how they develop guardrails and risk envelopes, and why Responsible AI makes better tools (not just safer ones). 

Jennifer Cheung

AI Enablement & Responsible AI Data Scientist
Pandora

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As generative AI embeds across enterprise systems, bias becomes structural: shaping decisions, communications, and customer outcomes at scale. What begins as a model limitation can quickly become a systemic risk, reinforced by data provenance, prompt design, feedback loops, and organisational incentives. This panel takes a critical look at bias as a socio-technical challenge spanning the full AI lifecycle. Moving beyond surface-level fairness claims, panellists will examine how to measure structural and emergent bias in production, define meaningful accountability, and govern generative systems that continuously adapt and influence behaviour.

• Examining bias across the AI lifecycle.

• Measuring structural and emergent bias in production.

• Embedding accountability beyond technical fixes.

Olu Akinyede

Data Privacy, Data Governance and AI Ethics
Aviva

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Uthman Ali

Responsible AI Leader
Emirates

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Philippa Penfold

Responsible AI & Data Science Manager
Elsevier

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