Equity, Diversity, and Inclusion for AI and Data Science 🌐

Join us for an insightful evening at the London Bioinformatics Meetup, dedicated to unravelling challenges in Equity, Diversity, and Inclusion (EDI) within the realms of AI, Data Science, and Genomics.

🗓 Date: Thursday, 29th February
🕖 Time: 19:00 - 20:00
📍 Location: C2.12 - Large Lecture, New Cavendish Campus, University of Westminster

AGENDA
19:00 - Welcome and Introduction
Start the evening with a brief overview of the event's purpose and goals, setting the stage for an evening of learning and discussion.

19:05 - Dr Manuel Corpas: "The Missing Ancestry Problem: Quantifying Biases in Global Genome Data and Analyses"
Dr Corpas, a renowned bioinformatician and lecturer at the University of Westminster, will explore the systemic underrepresentation in global genomic datasets. Discover the impact of these biases on personalised medicine and healthcare outcomes.

19:20 - Prof Nitasha Kaul: "Social Justice and AI"
Delve into the ethical dimensions of AI with Nitasha Kaul, as she discusses the intersection of AI, equity, and social justice. Understand the societal implications of AI-driven decisions and the importance of conscientious AI development.

19:35 - Prof Louise Thomas: "Deciphering the UK Biobank: A Journey in Data Analysis"
Gain insights from Louise Thomas's experiences in analysing the UK Biobank. Learn about the challenges and triumphs in handling large-scale datasets, and the critical role of diversity in genomic research.

19:50 - Open Discussion and Q&A
Engage with our speakers in a lively Q&A session. Share your thoughts, ask questions, and contribute to a meaningful dialogue on ensuring inclusivity and equity in AI and genomics research.

20:00 - Closing Remarks and Networking
Conclude the evening with a networking session, where you can connect with fellow attendees, exchange ideas, and foster collaborations.

DISCUSSION POINTS

• Systemic Underrepresentation: Explore the extent and implications of data underrepresentation in genomics.
• Diversity in Datasets: Evaluate the alignment of dataset representation with global population diversity.
• Workforce Diversity: Assess the impact of diversity within research teams as a measure of inherent biases and inequities.

Join us for a night of groundbreaking insights and spirited discussions, as we strive to ensure that the benefits of AI and Data Science are universally applicable and inclusive.

RSVP
Secure your spot for an enlightening evening that promises to bridge the gap between technology and humanity.

We look forward to seeing you there! 🌐🧬🖥️