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Toju Duke

Responsible AI Advisor, Author, Founder, Ex-Google

About

Gender: Female
Nationality:
Languages: English
Travels from: United Kingdom

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Biography Highlights

  • Author of “Building Responsible AI Algorithms,” offering a framework and practical tips for ethical model development Founder of Diverse AI, supporting underrepresented groups in AI Ex-Program Manager on Responsible AI at Google, specializing in large-scale models and Responsible AI processes Former UK lead for “Women in AI,” promoting global gender diversity in AI

Biography

Meet Toju Duke

With over 18 years experience spanning across Advertising, Retail, Not-For Profit and Tech, Toju is a popular speaker, author, thought leader and advisor on Responsible AI. Toju worked at Google for 10 years where she spent the last couple of years as a Programme Manager on Responsible AI leading various Responsible AI programmes across Google’s product and research teams with a primary focus on large-scale models and Responsible AI processes. Prior to her time on Google’s research organisation, Toju was the EMEA product lead for Google Travel and worked as a specialist across Google Travel and Shopping.

Toju is also the founder of Diverse AI, a community interest organisation with a mission to support and champion underrepresented groups to build a diverse and inclusive AI future. Her passion to improve Artificial Intelligence and representation in the field, led to the birth of Diverse AI which is focused on 3 main activities: Education, Research and Events targeted at people who identify with underrepresented groups and either want to up skill, join the field or have a sense of belonging and community with like-minded people. Toju was previously the UK lead for “Women in AI”, a non-profit global organisation with a mission to drive gender diversity in AI.

Toju’s dedication and commitment to the development and deployment of Responsible AI practices led to the writing of her book “Building Responsible AI Algorithms”. The book introduces a Responsible AI framework and guides readers through processes to apply at each stage of the machine learning (ML) life cycle, from problem definition to deployment, in order to reduce and mitigate the risks and harms found in AI technologies. The book provides practical tips and guidance on how to develop models that are fair, transparent, safe, secure, robust, and ethical.

Videos

Topics

Understanding the concept of Responsible AI and how it applies to AI technologies.

Available: Virtually

A walk-through of the various ways AI impacts humanity both positively and negatively.

Available: Virtually

Insights into Responsible Generative AI: Insights into generative AI, its pros and cons, and how to develop it using a Responsible AI framework.

Available: Virtually

Understanding AI, its history, timeline and how it applies to the education sector with an emphasis on ethical and responsible AI principles.

Available: Virtually

AI’s timeline, history and the AI arms race; AI for good, areas where it’s failed and how to mitigate its risks with a Responsible AI framework.

Available: Virtually

A glimpse into AI in the customer experience landscape and how to ensure AI applications remain customer centric.

Available: Virtually

AI’s uses, risks and mitigations, with a purview in the financial sector.

Available: Virtually

Books

Building Responsible AI Algorithms: A Framework for Transparency, Fairness, Safety, Privacy, and Robustness

This book introduces a Responsible AI framework and guides you through processes to apply at each stage of the machine learning (ML) life cycle, from problem definition to deployment, to reduce and mitigate the risks and harms found in artificial intelligence (AI) technologies. AI offers the ability to solve many problems today if implemented correctly and responsibly. This book helps you avoid negative impacts – that in some cases have caused loss of life – and develop models that are fair, transparent, safe, secure, and robust. The approach in this book raises your awareness of the missteps that can lead to negative outcomes in AI technologies and provides a Responsible AI framework to deliver responsible and ethical results in ML. It begins with an examination of the foundational elements of responsibility, principles, and data. Next comes guidance on implementation addressing issues such as fairness, transparency, safety, privacy, and robustness. The book helps you think responsibly while building AI and ML models and guides you through practical steps aimed at delivering responsible ML models, datasets, and products for your end users and customers. What You Will Learn: ● Build AI/ML models using Responsible AI frameworks and processes ● Document information on your datasets and improve data quality ● Measure fairness metrics in ML models ● Identify harms and risks per task and run safety evaluations on ML models ● Create transparent AI/ML models ● Develop Responsible AI principles and organizational guidelines Who This Book Is For: AI and ML practitioners looking for guidance on building models that are fair, transparent, and ethical; those seeking awareness of the missteps that can lead to unintentional bias and harm from their AI algorithms; policy makers planning to craft laws, policies, and regulations that promote fairness and equity in automated algorithms

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Building Responsible AI Algorithms: A Framework for Transparency, Fairness, Safety, Privacy, and Robustness

This book introduces a Responsible AI framework and guides you through processes to apply at each stage of the machine learning (ML) life cycle, from problem definition to deployment, to reduce and mitigate the risks and harms found in artificial intelligence (AI) technologies. AI offers the ability to solve many problems today if implemented correctly and responsibly. This book helps you avoid negative impacts – that in some cases have caused loss of life – and develop models that are fair, transparent, safe, secure, and robust. The approach in this book raises your awareness of the missteps that can lead to negative outcomes in AI technologies and provides a Responsible AI framework to deliver responsible and ethical results in ML. It begins with an examination of the foundational elements of responsibility, principles, and data. Next comes guidance on implementation addressing issues such as fairness, transparency, safety, privacy, and robustness. The book helps you think responsibly while building AI and ML models and guides you through practical steps aimed at delivering responsible ML models, datasets, and products for your end users and customers. What You Will Learn: ● Build AI/ML models using Responsible AI frameworks and processes ● Document information on your datasets and improve data quality ● Measure fairness metrics in ML models ● Identify harms and risks per task and run safety evaluations on ML models ● Create transparent AI/ML models ● Develop Responsible AI principles and organizational guidelines Who This Book Is For: AI and ML practitioners looking for guidance on building models that are fair, transparent, and ethical; those seeking awareness of the missteps that can lead to unintentional bias and harm from their AI algorithms; policy makers planning to craft laws, policies, and regulations that promote fairness and equity in automated algorithms

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