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Predicting Mental Illness Through AI with Stevie Chancellor

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Manage episode 303952432 series 2664415
Innhold levert av Radical AI. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Radical AI eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.

How is AI used to predict mental illness? What are the benefits and challenges to its use?

In this episode we interview Stevie Chancellor about AI, mental health, and the benefits and challenges of machine learning systems that are used to predict mental illness.

Stevie Chancellor is an Assistant Professor in the Department of Computer Science & Engineering at the University of Minnesota - Twin Cities. Her research combines human-computer interaction and machine learning approaches to build and critically evaluate machine learning systems for pressing social issues, focusing on high-risk health behaviors in online communities.

Full show notes for this episode can be found at Radicalai.org.

If you enjoy this episode please make sure to subscribe, submit a rating and review, and connect with us on twitter at twitter.com/radicalaipod

  continue reading

91 episoder

Artwork
iconDel
 
Manage episode 303952432 series 2664415
Innhold levert av Radical AI. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Radical AI eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.

How is AI used to predict mental illness? What are the benefits and challenges to its use?

In this episode we interview Stevie Chancellor about AI, mental health, and the benefits and challenges of machine learning systems that are used to predict mental illness.

Stevie Chancellor is an Assistant Professor in the Department of Computer Science & Engineering at the University of Minnesota - Twin Cities. Her research combines human-computer interaction and machine learning approaches to build and critically evaluate machine learning systems for pressing social issues, focusing on high-risk health behaviors in online communities.

Full show notes for this episode can be found at Radicalai.org.

If you enjoy this episode please make sure to subscribe, submit a rating and review, and connect with us on twitter at twitter.com/radicalaipod

  continue reading

91 episoder

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