Hands on inquiry into algorithmic bias and machine learning interpretability
(eVideo)
Contributors
Published
[Austin, Texas] : Data Science Salon, 2020.
Format
eVideo
Physical Desc
1 online resource (1 streaming video file (34 min., 35 sec.))
Status
Description
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Subjects
Other Subjects
More Details
Language
English
Notes
General Note
Title from resource description page (Safari, viewed October 6, 2020).
General Note
Place of publication from title screen.
Participants/Performers
Presenter, Fatih Akici.
Description
"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
Citations
APA Citation, 7th Edition (style guide)
Akici, F. (2020). Hands on inquiry into algorithmic bias and machine learning interpretability . Data Science Salon.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Akici, Fatih. 2020. Hands On Inquiry Into Algorithmic Bias and Machine Learning Interpretability. Data Science Salon.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Akici, Fatih. Hands On Inquiry Into Algorithmic Bias and Machine Learning Interpretability Data Science Salon, 2020.
MLA Citation, 9th Edition (style guide)Akici, Fatih. Hands On Inquiry Into Algorithmic Bias and Machine Learning Interpretability Data Science Salon, 2020.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.
Staff View
Grouped Work ID
90dfe65b-9b27-729e-fe6c-c29403d6512a-eng
Grouping Information
| Grouped Work ID | 90dfe65b-9b27-729e-fe6c-c29403d6512a-eng |
|---|---|
| Full title | hands on inquiry into algorithmic bias and machine learning interpretability |
| Author | data science salon |
| Grouping Category | movie |
| Last Update | 2024-09-06 16:31:08PM |
| Last Indexed | 2024-09-21 03:57:28AM |
Book Cover Information
| Image Source | default |
|---|---|
| First Loaded | Sep 16, 2024 |
| Last Used | Sep 16, 2024 |
Marc Record
| First Detected | Jul 29, 2024 04:05:45 PM |
|---|---|
| Last File Modification Time | Sep 06, 2024 04:41:33 PM |
MARC Record
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| 500 | |a Title from resource description page (Safari, viewed October 6, 2020). | ||
| 500 | |a Place of publication from title screen. | ||
| 511 | 0 | |a Presenter, Fatih Akici. | |
| 520 | |a "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 | ||
| 650 | 0 | |a Computer algorithms.|0 http://id.loc.gov/authorities/subjects/sh91000149 | |
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