ΑΙhub.org
 

Stanford HAI 2021 fall conference: four radical proposals for a better society


by
11 November 2021



share this:
Stanford HAI conference logo

This year’s Stanford HAI virtual fall conference took place on 9-10 November. It comprised a discussion of four policy proposals that respond to the issues and opportunities created by artificial intelligence. The premise is that each policy proposal poses a challenge to the status quo. These proposals were presented to panels of experts who debated the merits and issues surrounding each policy.

The event was recorded and you can watch both days’ sessions on YouTube. Day one covered proposals 1 and 2, and day two focussed on proposals 3 and 4.

Day one

Proposal 1: Middleware could give consumers choices over what they see online

Middleware is software that rides on top of an existing internet or social media platform such as Google, Facebook or Twitter and can modify the presentation of underlying data. This proposal suggests outsourcing content moderation to a layer of competitive middleware companies that would offer users the ability to tailor their search and social media feeds to suit their personal preferences.

Taking part in this discussion were:
Francis Fukuyama (Freeman Spogli Institute for International Studies)
Ashish Goel (Stanford University)
Kate Starbird (University of Washington)
Katrina Ligett (Hebrew University)
Renee DiResta (Stanford Internet Observatory)

Read more here.

Proposal 2: Universal Basic Income to offset job losses due to automation

The proposal is to give every American adult $1,000 a month to avert an economic crisis.

Taking part in this discussion were:
Andrew Yang (Venture for America)
Darrick Hamilton (The New School Milano)
Mark Duggan (Stanford Institute for Economic Policy Research)
Juliana Bidadanure (Stanford University)

Read more here.

Day two

Proposal 3: Data cooperatives could give us more power over our data

To address the power imbalance between data producers and corporations that profit from our data, scholars propose creating data cooperatives to act as fiduciary intermediaries.

Taking part in this discussion were:
Divya Siddarth (Microsoft)
Pamela Samuelson (UC Berkeley)
Sandy Pentland (MIT)
Jennifer King (Stanford University)

Read more here.

Proposal 4: Third-party auditor access for AI accountability

A proposal for legal protections and regulatory involvement to support organizations that uncover algorithmic harm.

Taking part in this discussion were:
Deborah Raji (UC Berkeley)
DJ Patil (Devoted Health)
Cathy O’Neil (Columbia University)
Fiona Scott Morton (Yale University)

Read more here.

These four proposals were chosen following a public consultation last spring. The organisers received nearly 100 suggestions. In addition to the four discussed at the event, there are six more that the team at Stanford HAI would like to highlight. You can find out more about these here.




Lucy Smith is Senior Managing Editor for AIhub.
Lucy Smith is Senior Managing Editor for AIhub.

            AUAI is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

The Machine Ethics podcast: organoid computing with Dr Ewelina Kurtys

In this episode, Ben chats to Ewelina about the uses of organoids and energy saving computing, differences between biological neurons and digital neural networks, and much more.

#AAAI2026 invited talk: Yolanda Gil on improving workflows with AI

  28 Apr 2026
Former AAAI president on using AI to help communities of scientists better streamline their research.

Maryna Viazovska’s proofs of sphere packing formalized with AI

  27 Apr 2026
Formalization achieved through a collaboration between mathematicians and artificial intelligence tools.

Interview with Deepika Vemuri: interpretability and concept-based learning

  24 Apr 2026
Find out more about Deepika's research bridging the gap between data-driven models and symbolic learning.

As a ‘book scientist’ I work with microscopes, imaging technologies and AI to preserve ancient texts

  23 Apr 2026
Using an array of technologies to recover, understand and preserve many valuable ancient texts.

Sony AI table tennis robot outplays elite human players

  22 Apr 2026
New robot and AI system has beaten professional and elite table tennis players.

Causal models for decision systems: an interview with Matteo Ceriscioli

  21 Apr 2026
How can we integrate causal knowledge into agents or decision systems to make them more reliable?

A model for defect identification in materials

  20 Apr 2026
A new model measures defects that can be leveraged to improve materials’ mechanical strength, heat transfer, and energy-conversion efficiency.



AUAI is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.02 - Association for the Understanding of Artificial Intelligence