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Managing Risk in Machine Learning Models

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Contenuto fornito da O'Reilly Radar. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da O'Reilly Radar o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.
In this episode of the Data Show, I spoke with Andrew Burt, chief privacy officer at Immuta, and Steven Touw, co-founder and CTO of Immuta. Burt recently co-authored an upcoming white paper on managing risk in machine learning models, and I wanted to sit down with them to discuss some of the proposals they put forward to organizations that are deploying machine learning. Some high-profile examples of models gone awry have raised awareness among companies for the need for better risk management tools and processes. There is now a growing interest in ethics among data scientists, specifically in tools for monitoring bias in machine learning models. In a previous post, I listed some of the key considerations organization should keep in mind as they move models to production, but the upcoming report co-authored by Burt goes far beyond and recommends lines of defense, including a description of key roles that are needed.
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443 episodi

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Managing Risk in Machine Learning Models

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Manage episode 209840169 series 1427720
Contenuto fornito da O'Reilly Radar. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da O'Reilly Radar o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.
In this episode of the Data Show, I spoke with Andrew Burt, chief privacy officer at Immuta, and Steven Touw, co-founder and CTO of Immuta. Burt recently co-authored an upcoming white paper on managing risk in machine learning models, and I wanted to sit down with them to discuss some of the proposals they put forward to organizations that are deploying machine learning. Some high-profile examples of models gone awry have raised awareness among companies for the need for better risk management tools and processes. There is now a growing interest in ethics among data scientists, specifically in tools for monitoring bias in machine learning models. In a previous post, I listed some of the key considerations organization should keep in mind as they move models to production, but the upcoming report co-authored by Burt goes far beyond and recommends lines of defense, including a description of key roles that are needed.
  continue reading

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