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Policymakers and the public often find algorithms to be complex, opaque and intimidating—and it can be tempting to pretend that hard moral questions have simple technological answers. But that approach leaves technical experts holding the moral microphone, and it stops people who lack technical expertise from making their voices heard.
Clinical Mind AI is a Stanford-developed, international collaborative platform designed to strengthen clinical reasoning education through AI-simulated patient encounters across health professions.
Clinical Mind AI is a Stanford-developed, international collaborative platform designed to strengthen clinical reasoning education through AI-simulated patient encounters across health professions.
Our November session is Scaling Training Across GPUs and Nodes with speaker Zoe Ryan. This session focuses on efficient strategies for using multiple GPUs and nodes. We will discuss how to deploy strategies of data parallelism and model parallelism to scale to multiple GPUs through PyTorch, enabling faster training times and better model performance.
Location: CoDa W401 & Zoom
Refreshments and snacks provided
Please follow the link to register
Our November session is Scaling Training Across GPUs and Nodes with speaker Zoe Ryan. This session focuses on efficient strategies for using multiple GPUs and nodes. We will discuss how to deploy strategies of data parallelism and model parallelism to scale to multiple GPUs through PyTorch, enabling faster training times and better model performance.
Location: CoDa W401 & Zoom
Refreshments and snacks provided
Please follow the link to register
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Today, policymakers and scholars are seeking better ways to share the moral decision-making within high stakes software — exploring ideas like public participation, transparency, forecasting, and algorithmic audits. But there are few real examples of those techniques in use.
In Voices in the Code, scholar David G. Robinson tells the story of how one community built a life-and-death algorithm in an inclusive, accountable way. Between 2004 and 2014, a diverse group of patients, surgeons, clinicians, data scientists, public officials and advocates collaborated and compromised to build a new transplant matching algorithm – a system to offer donated kidneys to particular patients from the U.S. national waiting list.Drawing on interviews with key stakeholders, unpublished archives, and a wide scholarly literature, Robinson shows how this new Kidney Allocation System emerged and evolved over time, as participants gradually built a shared understanding both of what was possible, and of what would be fair. Robinson finds much to criticize, but also much to admire, in this story. It ultimately illustrates both the promise and the limits of participation, transparency, forecasting and auditing of high stakes software. The book’s final chapter draws out lessons for the broader struggle to build technology in a democratic and accountable way.
