Hands on inquiry into algorithmic bias and machine learning interpretability /

"Presented by Fatih Akici, Manager, Risk Analytics and Data Science at Populus Financial Group. As intelligent systems deepen their footprints in our daily lives, algorithmic bias becomes a more prominent problem in today's world. The position of executives and data science leaders to this...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Formato: Electrónico Video
Idioma:Inglés
Publicado: [Austin, Texas] : Data Science Salon, 2020.
Temas:
Computer algorithms.
Machine learning > Social aspects.
Research > Statistical methods.
Algorithms
Algorithmes.
Apprentissage automatique > Aspect social.
Recherche > Méthodes statistiques.
algorithms.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Descripción
Sumario:"Presented by Fatih Akici, Manager, Risk Analytics and Data Science at Populus Financial Group. As intelligent systems deepen their footprints in our daily lives, algorithmic bias becomes a more prominent problem in today's world. The position of executives and data science leaders to this issue is generally reactive, in that, companies solely respond to the requirements coming from regulatory agencies. In this presentation, I am going to argue why the leaders should be proactive in identifying biases and how they will benefit from fixing them. I will demonstrate my point on an applied example."--Resource description page
Notas:Title from resource description page (Safari, viewed October 6, 2020).
Place of publication from title screen.
Descripción Física:1 online resource (1 streaming video file (34 min., 35 sec.))

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