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Voices in the Code: A Story About People, Their Values, and the Algorithm They Made

Présenté par

Stanford Institute for Human-Centered Artificial Intelligence (HAI)

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Activités de formation

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. Today, policymakers and scholars are seeking better ways to share the moral decisionmaking 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donated kidneys to particular patients from the U.S. national waiting list.Drawing on interviews with key stakeholders, unpublished archives, and a wide scholarly literature, Robinson shows how this new Kidney Allocation System emerged and evolved over time, as participants gradually built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.

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Projet en vedette

Le cancer de la prostate est le deuxième cancer le plus fréquent et la cinquième cause de décès par cancer chez les hommes. Pour améliorer les résultats de santé des patients, le traitement doit être personnalisé en se basant sur un pronostic précis. Il existe déjà des nomogrammes permettant d’identifier les patients à faible risque de récidive sur la base d’informations cliniques préopératoires, mais ces outils n’utilisent pas les images médicales des patients.

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