Reid Hoffman On The Importance Of Systematic Risk Identification While Scaling

By Amit Chowdhry ● Nov 10, 2019
  • LinkedIn co-founder and Greylock partner Reid Hoffman recently went on stage at the Stanford Institute for Human-Centered Artificial Intelligence. Here’s what he said.

LinkedIn co-founder and partner at Greylock Reid Hoffman recently appeared on stage at the Stanford Institute for Human-Centered Artificial Intelligence (HAI) with former US Chief Data Scientist and DJ Patil, which was moderated by Davies Family Senior Fellow at the Hoover Institution Amy Zegart. Patil had also served as the head of data products, chief scientist, and chief security officer at LinkedIn between 2008 and 2011.

According to VentureBeat, HAI started earlier this year with support from Hoffman, IBM Research director Dario Gil, Microsoft Research director Eric Horvitz, and Amazon Web Services CEO Andy Jassy. Hoffman is on the board for HAI and also backed the Ethics and Governance of Artificial Intelligence Fund at MIT and Harvard University in January 2017.

During the panel, Hoffman and Patil discussed the implications around data privacy and the necessities around integrating technology in policy-making. Hoffman referred to this as bridging “the suits and the hoodies.” Hoffman pointed out that the misuse of technology may lead to harm as startups expand rapidly as part of a process called “blitz-scaling.” And Hoffman explained that this challenge should be managed with systematic risk identification.

“As you move your organization … one of the things you can do is add in some threads and say, ‘Let’s try to identify what the serious risks would be,’ and ‘Let’s try to identify what the things we wished to be fixed in advance versus afterward,” said Hoffman at the event via The Stanford Daily.

Hoffman explained that technology ethics is not about “having a 0% chance of bad outcomes.” But it is all about balancing risks for avoiding worse results. This was the response Hoffman gave when asked about the use of artificial intelligence applications in the healthcare industry.

Patil — who is often credited for coining the term “data science” — had worked with the science and technology policy team under former President Obama between 2015 and 2017. And Patil said that the proper data use should enable people to make better decisions.

Patil suggested that people working in tech may also need a broad education in humanities to be prepared for judgment in complex ethical questions such as private data sharing.

“If you don’t have ethics and the liberal arts as part of the undergraduate core curriculum, you’re at a disadvantage for dealing with these ethical aspects of the challenge,” Patil noted.

Patil believes that the US should also increase its investment in technological education for meeting the demands of tomorrow’s competitive workplace.

“When we say that China’s accelerating, we’re also saying that we’re decelerating our investment (in technology),” stated Patil via The Stanford Daily. “Why aren’t we not investing more aggressively? Why aren’t we not taking the leadership role that we know can happen because of our leading institutions? We’re not investing as we need to as a country.”

While the US and China compete economically, Hoffman and Patil agreed that collaboration and a data-sharing framework would help prevent risks around “species challenges” such as climate change. With better international frameworks for sharing data across regional lines, it would work better for human problems.