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 , Managing Editor for AIhub.
Lucy Smith , Managing Editor for AIhub.




            AIhub is supported by:


Related posts :



Are emergent abilities of large language models a mirage? – Interview with Brando Miranda

We hear about work that won a NeurIPS 2023 outstanding paper award.
25 April 2024, by

We built an AI tool to help set priorities for conservation in Madagascar: what we found

Daniele Silvestro has developed a tool that can help identify conservation and restoration priorities.
24 April 2024, by

Interview with Mike Lee: Communicating AI decision-making through demonstrations

We hear from AAAI/SIGAI Doctoral Consortium participant Mike Lee about his research on explainable AI.
23 April 2024, by

Machine learning viability modelling of vertical-axis wind turbines

Researchers have used a genetic learning algorithm to identify optimal pitch profiles for the turbine blades.
22 April 2024, by

The Machine Ethics podcast: What is AI? Volume 3

This is a bonus episode looking back over answers to our question: What is AI?
19 April 2024, by

DataLike: Interview with Tẹjúmádé Àfọ̀njá

"I place an emphasis on wellness and meticulously plan my schedule to ensure I can make meaningful contributions to what's important to me."




AIhub is supported by:






©2024 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association