How can my organisation use AI responsibly?

Our second webinar in a series of three on how AI can support the third sector’s data use. Our speakers talk about real-world use cases of generative AI in the third sector and discuss the risks associated with genAI, including hallucinations, bias, and environmental costs.

Resources mentioned in the webinar

This list will not be updated, but we may share more in future articles.

Responsibility guidelines

• Principles for responsible, trustworthy and privacy-protective generative AI technologies 

• How to use AI and personal data appropriately and lawfully 

• How do we ensure lawfulness in AI?

• Humans in the Loop: The Design of Interactive AI Systems | Stanford HAI

• Responsible AI Adoption in Philanthropy

Generative AI Learning

• SCVO AI Guides

• AI Checklist for Leaders

• NTEN AI Hub 

• AI4C Working Group

• Generative AI with Large Language Models | Coursera 

• Generative AI for Everyone: Basics and Applications for All

• Explained: Generative AI Environmental Impact

LLMs

• Dummies Guide to Understanding LLM 

• Mastering LLM Agent: The Tester's Essential Guide 

• Presentation on using LLMs for text analysis/evaluation tasks

Use examples

• HuggingChat

• KnowBot

• How AI can help fact checkers – Full Fact 

Evaluating risk

• real-world examples of AI hallucinations

• The Wildlife Trust’s AI risk assessment spreadsheet

• DataKind Global Generative AI Playbook

• CAST AI Resources

• MySociety AI Framework

Planning

• A four step approach to collaborative data projects | by Eddie Copeland

• The MERL Tech Initiative

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Your speakers

Michelle Lee

Michelle is a Director in the Deployment team at WovenLight, a category-defining private equity firm powered by cutting-edge AI and expertise in real-world deployment. She currently leads the Private Equity Roundtable on Responsible AI, co-developing policies and guidelines for the industry around safe and scalable usage of AI. Michelle has completed a PhD at Cambridge in Computer Science with a focus on mitigating unintended biases in AI systems.

Billy Zhao

Billy is currently a machine learning engineer at Trainline, after spending several years as a senior data scientist with multinational education company Education First, exploring and deploying data science and machine learning technologies. He is also co-founder of Reportable, a service in the UK for East and Southeast Asian communities to increase reporting and awareness of hate crime and ensure support is available for those in need. He has been a volunteer with DataKind UK for many years.

Dulcie Vousden

Dulcie is DataKind UK’s Head of Data Science, where she has spent the last five years supporting third sector organisations to use their data more effectively and advocating for the ethical and responsible use of data in the sector. Prior to DataKind UK, she completed a PhD in medical biophysics and has over 15 years of experience designing and analysing data-focussed projects.

Thank you to the Insight Infrastructure programme for their support. Developed by the Joseph Rowntree Foundation, it aims to democratise access to high-quality quantitative and qualitative data and evidence through open collaboration and innovation, to help tackle injustice and inequality in the UK.