Online-Ressource | |
Verfasst von: | Salon, Data [VerfasserIn] |
Titel: | Hands on Inquiry into Algorithmic Bias and Machine Learning Interpretability |
Institutionen: | Safari, an O'Reilly Media Company. [MitwirkendeR] |
Verf.angabe: | Salon, Data |
Ausgabe: | 1st edition |
Verlagsort: | [Erscheinungsort nicht ermittelbar] |
Verlag: | Data Science Salon |
Jahr: | 2020 |
Umfang: | 1 online resource (1 video file, approximately 35 min.) |
Fussnoten: | Online resource; Title from title screen (viewed March 24, 2020) |
Abstract: | 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. |
ComputerInfo: | Mode of access: World Wide Web. |
URL: | Aggregator: https://learning.oreilly.com/library/view/-/000015ZPNEGQBV2/?ar |
Datenträger: | Online-Ressource |
Sprache: | eng |
Sach-SW: | Electronic videos ; local |
K10plus-PPN: | 1702677680 |
Lokale URL UB: | Zum Volltext |
Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg | |
Bestellen/Vormerken für Benutzer des Klinikums Mannheim Eigene Kennung erforderlich | |
Bibliothek/Idn: | UW / m3691333214 |
Lokale URL Inst.: | Zum Volltext |