Responsible AI Summit Main Conference Day 2 - Tuesday 22 September

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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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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Mahendra Muralidhar

Chief Technology Officer
VML

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Mark Beckwith

Director of Data Governance & Architecture
Financial Times

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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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As AI systems scale, the human labour powering them, data annotators, content moderators, and crowd workers, becomes operationally invisible. Yet laws such as the Corporate Sustainability Due Diligence Directive make oversight of supply chains a legal obligation, not a reputational choice. Ethical risk in AI no longer sits only within the model; it extends across global labour networks that train, filter, and sustain these systems.

This panel examines why auditing your AI supply chain matters, and what meaningful accountability looks like in practice. Speakers will highlight how standards-based evaluation, like Fairwork AI, can expose hidden labour risks, benchmark working conditions, and create enforceable transparency. The discussion moves beyond awareness to explore due diligence, procurement leverage, and how organisations can align AI deployment with fair work principles under emerging regulatory pressure.

• Exposing hidden labour across AI supply chains.

• Using certification frameworks to benchmark labour standards.

• Embedding due diligence into AI procurement decisions.

Mark Graham

Professor of Internet Geography, Director of Fairwork
University of Oxford, Fairwork

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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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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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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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Clara Higuera Cabañes

Responsible AI Program Lead
BBVA

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Uthman Ali

Associate Fellow
University of Oxford

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