Human-centered AI Podcast

In today’s episode we are joined by Linda McIver, Executive Director at the Australian Data Science Education Institute and author of Raising Heretics: teaching kids to change the world.In the book she explores how data science education can change the world and she proposes a new approach to education that empowers children to solve real problems, challenge their own results, and shake up the status quo on the basis of evidence and data. In todays episode we talk about Linda’s upcoming book and explore the current education system, data literacy, the ways in which were misunderstanding science and how we can change our education system and raise heretics.

We talk about human-rights centered design, and Caroline’s multi-year feminist data set project. In specific, we discuss one of her latest initiatives called technically responsible knowledge, a tool and advocacy initiative spotlighting unjust labor in the machine learning pipeline.

We talk about building machine learning products and the different practices it involves. We dive into the data science development lifecycle, agile development practices, interdisciplinary team work and practices such as ML Ops, test driven development and pair programming.

We discuss Niya's research on uncovering the unintended consequences of AI. We also talk about her recently published paper on designing with and for artificial intelligence, and finally, about adaptive organisations in the context of artificial intelligence.

We talk about Sherif’s company, Carbon Re, and how they’re helping organisations to increase efficiency and reduce emissions. We also talk about the ‘Tech for Climate’ space in general and explore the role that technology can play in the climate challenges we are facing.

We talk about the work Cansu does at AI Ethics Lab, and in particular their latest initiative, “Dynamics of AI Principles”. We also discuss the role of AI ethics principles, the difference between core principles and instrumental principles, and how organisations can go about operationalising AI ethics.

We dive into the work B does at the Partnership, particularly around the topics of responsible sourcing and the AI and Shared Prosperity Initiative. Within this conversation, we explore big questions such as 'How can you steer technology in a direction that increases economic prosperity for the many rather than the few?'

We talk about Future Ethics, and how we can use methods from disciplines such as speculative design and future studies to stimulate moral imagination. We also discuss the role of ethical guidelines, bringing those into practice, and how you can start having more conversations within your team or organisation around ethics.

We talk about bias, which has been a central topic in Ricardo’s research for the last 12 years. We discuss the many different types of biases that are out there, zoom in on bias on the web and bias in recommender systems. Beyond that, Ricardo shares valuable advice about what we can all do to become more aware of our own biases.

We talk about how Nadina applies emerging technologies to improve the health of urban ecosystems, through the “Internet of Nature”. We also discuss Green City Watch, an open-source geoAI initiative, which Nadina co-founded.

Ciaran shares the story of how Heineken started and scaled advanced analytics and AI. He also talks about Heineken's value driven approach and mentality.

We discuss Sudha's recent book, AIX: Designing Artificial Intelligence and what it means to design for AI. We also discuss the role of AI in the current global landscape and her advice on how to pivot your career to AI.

We dive into best practices for launching successful AI initiatives, from choosing a use case, getting buy in from the business, designing the technical architecture, and scaling.

We discuss AI trends in the financial industry and how ABN AMRO applies artificial intelligence to create business value. Touching on the broadening of skillsets in AI, the necessary end-to-end approach and the importance of ethics and explainability in developing responsible AI.