Data Science in Practice is a series of 4 events that unites professionals from diverse domains and sectors who work with data science, providing a platform to share insights, discuss challenges, and explore innovative solutions.

The events emphasises the journey to a solution rather than the solution itself, as well as the mistakes along the way rather than the successes. Designed to foster collaboration and knowledge exchange, Data Science in Practice extends beyond the physical events. It encourages participants to engage in informal discussions and smaller group interactions between events, promoting continuous engagement and a robust network.

Event 4 – GenAI and Ethics

Generative AI (GenAI) and AI in general are significantly transforming how individuals and companies operate. The productivity gains from automating tasks and decision-making are becoming commonplace, as AI enhances efficiency by taking over repetitive tasks, allowing employees to focus on more complex work and increasing job satisfaction. However, the risks of unchecked AI adoption are increasingly clear, including issues like hallucinations in LLMs, opaque decisions from high-risk models, and inadequate governance frameworks leading to ethical challenges. Alarmingly, some agentic AI systems are already exhibiting self-preservation and deceptive behaviors as their capabilities expand.

As a result, AI and GenAI are now seen as technologies that must be regulated, as highlighted by the EU AI Act, which imposes obligations on high-risk AI and GenAI systems. This presentation will provide a high-level overview of the EU AI Act and other relevant legislation affecting the pharmaceutical industry’s AI landscape. It will also detail how companies like Novo Nordisk is adapting to these requirements by implementing safety guardrails, quality and risk management systems, and effective AI governance frameworks to ensure responsible AI use that benefits patients and society. Finally, it will showcase how the Responsible AI team operates at the intersection of data science, engineering, and compliance to ensure best practices and adherence to regulations.

Agenda (subject to change)
13.00 Welcome & Introduction
13.15 Icebreaker
13.30 Presentation
14.15 Discussion & Q&A
14.30 Break
14.45 Facilitated group discussions
1. How to explain AI and GenAI model decision-making to create transparency for non-technical stakeholders?
2. How can the importance and relevance of bias in algorithms be determined, and how can its impacts be mitigated?
3. What are good practices to manage AI models through their entire lifecycle (exploration, development, production and retirement)?
15.30 Facilitated networking
16.00 End

Register here: https://forms.gle/Bpn5dE6F3XzQb9vw7