ΑΙhub.org
 

The Machine Ethics podcast: responsible AI research with Madhulika Srikumar


by
14 September 2021



share this:

Madhulika Srikumar
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.

AI regulation

This time we’re talking AI research with Madhulika Srikumar of Partnership on AI. We chat about managing the risks of AI research, how the AI community should think about the consequences of their research, documenting best practises for AI, OpenAI’s GTP2 research disclosure example, considering unintended consequences & negative downstream outcomes, considering what your research may actually contribute, promoting scientific openness, proportional ethical reflection, research social impact assessments and more…

Listen to the episode here:

Madhulika Srikumar is a program lead at the Safety-Critical AI initiative at Partnership on AI, a multistakeholder non-profit shaping the future of responsible AI. Core areas of her current focus include community engagement on responsible publication norms in AI research and diversity and inclusion in AI teams. Madhu is a lawyer by training and completed her graduate studies (LL.M) at Harvard Law School.

Managing the Risks of AI Research: Six Recommendations for Responsible Publication.


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.



tags: ,


The Machine Ethics Podcast

            AUAI is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

Image Empire – a new short film from Alan Warburton

  29 May 2026
An animated fairytale about the fusion of the real and the virtual within contemporary AI models.
monthly digest

AIhub monthly digest: May 2026 – AI for science, the lottery ticket hypothesis, and world models

  28 May 2026
Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.

You probably wouldn’t notice if an AI chatbot slipped ads into its responses

  27 May 2026
Research suggests AI chatbots could easily be used for covert advertising to manipulate their human users.

The Good Robot podcast: the future of data centres and digital sovereignty with Friederike von Franqué

  26 May 2026
Can cloud infrastructure be owned and governed by the people, and not just Big Tech?
coffee corner

AIhub coffee corner: World models

  22 May 2026
The AIhub coffee corner captures the musings of AI experts over a short conversation.

Why the world’s banks are so worried about Anthropic’s latest AI model

  21 May 2026
The finance world’s concern rests on the impressive cyber capabilities of a product called Mythos.

Embracing empiricism – from the lottery hypothesis to creating real-world impact: an interview with Jonathan Frankle

  20 May 2026
Jonathan Frankle discusses empiricism, making an impact, and the legacy of his lottery ticket hypothesis.

A faster way to estimate AI power consumption

  19 May 2026
The “EnergAIzer” method generates reliable results in seconds, enabling data center operators to efficiently allocate resources and reduce wasted energy.



AUAI is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.02 - Association for the Understanding of Artificial Intelligence