ΑΙhub.org
 

The Machine Ethics Podcast: Rights, trust and ethical choice with Ricardo Baeza-Yates


by
26 September 2022



share this:

Ricardo Baeza-Yates
Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology’s impact on society.

Rights, trust and ethical choice

This episode we talk with Ricardo Baeza-Yates about responsible AI, the importance of AI governance, questioning people’s intent to create AGI, robot rights and brain / neural rights, the evolution of intelligence, ethical risk assessment, machine ethics, making ethical choices on behalf of your users, binary notions of trust, stupid uses of AI and more…

Listen to the episode here:

Ricardo Baeza-Yates is Director of Research at the Institute for Experiential AI of Northeastern University. He is also a part-time Professor at Universitat Pompeu Fabra in Barcelona and Universidad de Chile in Santiago. Before, he was the CTO of NTENT, a semantic search technology company based in California and prior to these roles, he was VP of Research at Yahoo Labs, based in Barcelona, Spain, and later in Sunnyvale, California, from 2006 to 2016. He is co-author of the best-seller Modern Information Retrieval textbook, which won the ASIST 2012 Book of the Year award. From 2002 to 2004 he was elected to the Board of Governors of the IEEE Computer Society and between 2012 and 2016 was elected to the ACM Council.

Since 2010 he has been a founding member of the Chilean Academy of Engineering. In 2009 he was named ACM Fellow and in 2011 IEEE Fellow, among other awards and distinctions. He obtained a PhD in CS from the University of Waterloo, Canada, and his areas of expertise are web search and data mining, information retrieval, bias and ethics on AI, data science and algorithms in general.


About The Machine Ethics podcast

This podcast was created, and is run by, Ben Byford and collaborators. Over the last few years the podcast has grown into a place of discussion and dissemination of important ideas, not only in AI but in tech ethics generally.

The goal is to promote debate concerning technology and society, and to foster the production of technology (and in particular: decision making algorithms) that promote human ideals.

Ben Byford is a AI ethics consultant, code, design and data science teacher, freelance games designer with over 10 years of design and coding experience building websites, apps, and games. In 2015 he began talking on AI ethics and started the Machine Ethics podcast. Since then, Ben has talked with academics, developers, doctors, novelists and designers about AI, automation and society.

Join in the conversation with us by getting in touch via email here or following us on Twitter and Instagram.




The Machine Ethics Podcast




            AIhub is supported by:



Related posts :



How AI is opening the playbook on sports analytics

  18 Sep 2025
Waterloo researchers create simulated soccer datasets to unlock insights once reserved for pro teams.

Discrete flow matching framework for graph generation

and   17 Sep 2025
Read about work presented at ICML 2025 that disentangles sampling from training.

We risk a deluge of AI-written ‘science’ pushing corporate interests – here’s what to do about it

  16 Sep 2025
A single individual using AI can produce multiple papers that appear valid in a matter of hours.

Deploying agentic AI: what worked, what broke, and what we learned

  15 Sep 2025
AI scientist and researcher Francis Osei investigates what happens when Agentic AI systems are used in real projects, where trust and reproducibility are not optional.

Memory traces in reinforcement learning

  12 Sep 2025
Onno writes about work presented at ICML 2025, introducing an alternative memory framework.

Apertus: a fully open, transparent, multilingual language model

  11 Sep 2025
EPFL, ETH Zurich and the Swiss National Supercomputing Centre (CSCS) released Apertus today, Switzerland’s first large-scale, open, multilingual language model.

Interview with Yezi Liu: Trustworthy and efficient machine learning

  10 Sep 2025
Read the latest interview in our series featuring the AAAI/SIGAI Doctoral Consortium participants.

Advanced AI models are not always better than simple ones

  09 Sep 2025
Researchers have developed Systema, a new tool to evaluate how well AI models work when predicting the effects of genetic perturbations.



 

AIhub is supported by:






 












©2025.05 - Association for the Understanding of Artificial Intelligence