Contributions to this post were provided by CCC Council member Odest Chadwicke Jenkins. Computing Community Consortium (CCC) Council member Odest Chadwicke Jenkins (University of Michigan) was recently interviewed by the New York Times about his thoughts on the AI field’s apparent failure to make systems that are accurate for everyone. Many of today’s AI systems have biases against people of color and the broader diversity beyond the white, male, affluent and able-bodied developers of most computer and robot systems. We need to be sure that when autonomous robots make their decisions, the designer’s flaws and judgements are not “baked in.” Robotics researchers in our community are committed to ending the […]
Computing Community Consortium Blog
The goal of the Computing Community Consortium (CCC) is to catalyze the computing research community to debate longer range, more audacious research challenges; to build consensus around research visions; to evolve the most promising visions toward clearly defined initiatives; and to work with the funding organizations to move challenges and visions toward funding initiatives. The purpose of this blog is to provide a more immediate, online mechanism for dissemination of visioning concepts and community discussion/debate about them.
Posts Tagged ‘NYT’
CCC Council Member Chad Jenkins in NYT Article: Can We Make Our Robots Less Biased Than We Are?
December 7th, 2020 / in Announcements, CCC, robotics, workshop reports / by Helen WrightAI Research: Times They Are A-Changin’ (or They Should Be)
October 2nd, 2019 / in AI, Announcements, policy, research horizons, Research News / by Helen WrightThe following blog was written by Computing Community Consortium (CCC) Vice-Chair Liz Bradley from University of Colorado Boulder and CCC Chair Mark D. Hill from the University of Wisconsin Madison. Times in Artificial Intelligence are or should be changing. See Bob Dylan’s 1964 lyrics below. Last week the New York Times published an article titled “A.I. Researchers See Danger of Haves and Have-Nots.” Modern AI research, which demands enormous computational resources, large data sets, and significant human expertise, is becoming increasingly difficult for anyone outside the large tech companies like Google, Microsoft, Amazon and Facebook. This includes university labs—which, as the article points out, have traditionally been a wellspring of […]
NYT: Stanford Team Aims at Alexa and Siri With a Privacy-Minded Alternative
June 27th, 2019 / in AI, Announcements, CCC, research horizons, Research News / by Helen WrightContributions to this post were provided by Monica Lam and Jen King from Stanford University. The New York Times recently published an article titled Stanford Team Aims at Alexa and Siri With a Privacy-Minded Alternative. Professor Monica Lam and her students, Giovanni Campagna, Silei Xu, Michael Fischer, and Mehrad Moradshahi, have developed a virtual assistant called Almond that can avoid surrendering personal information to a centralized service and encourage open competition among companies. She is joined by Stanford computer science researchers Michael Bernstein, Dan Boneh, Jen King, James Landay, Chris Manning, and David Mazières, Chris Re in a newly funded NSF research grant to expand the capabilities and privacy protection […]
CCC Responds to New York Times Article- Society Needs Computer Science (and Math and Social Sciences) Now More Than Ever
November 28th, 2017 / in Announcements, CCC, research horizons, Research News / by Helen WrightThe following blog post was drafted by CCC Chair Beth Mynatt, CCC Exec Member Ben Zorn, and CCC Council Members Elizabeth Bradley, Sampath Kannan, and Cynthia Dwork. Beth Mynatt, CCC Chair, recently submitted the following letter to the Editor of the New York Times: In her November 14th Op-Ed, Cathy O’Neil makes the case that technology is impacting people’s lives at an accelerating pace and that computer scientists have been “asleep at the wheel” in dealing with emerging challenges. Computing research advances have had sweeping societal effects, but not without problems (e.g. racial bias in facial recognition). Careful design is critical to heading off “unintended consequences” resulting from one-sided research […]