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| Format: | Elektronisch E-Book |
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[Erscheinungsort nicht ermittelbar]
O'Reilly Media, Inc.
2020
|
| Links: | https://learning.oreilly.com/library/view/-/9781492090878/?ar |
| Zusammenfassung: | Like other powerful technologies, AI and machine learning present significant opportunities. To reap the full benefits of ML, organizations must also mitigate the considerable risks it presents. This report outlines a set of actionable best practices for people, processes, and technology that can enable organizations to innovate with ML in a responsible manner. Authors Patrick Hall, Navdeep Gill, and Ben Cox focus on the technical issues of ML as well as human-centered issues such as security, fairness, and privacy. The goal is to promote human safety in ML practices so that in the near future, there will be no need to differentiate between the general practice and the responsible practice of ML. This report explores: People: Humans in the Loop --Why an organization's ML culture is an important aspect of responsible ML practice Processes: Taming the Wild West of Machine Learning Workflows --Suggestions for changing or updating your processes to govern ML assets Technology: Engineering ML for Human Trust and Understanding --Tools that can help organizations build human trust and understanding into their ML systems Actionable Responsible ML Guidance --Core considerations for companies that want to drive value from ML. |
| Beschreibung: | Title from content provider |
| Umfang: | 1 online resource |
| ISBN: | 9781492090861 1492090867 |
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| spelling | Cox, Benjamin VerfasserIn aut Responsible Machine Learning Benjamin Cox [Erscheinungsort nicht ermittelbar] O'Reilly Media, Inc. 2020 1 online resource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Title from content provider Like other powerful technologies, AI and machine learning present significant opportunities. To reap the full benefits of ML, organizations must also mitigate the considerable risks it presents. This report outlines a set of actionable best practices for people, processes, and technology that can enable organizations to innovate with ML in a responsible manner. Authors Patrick Hall, Navdeep Gill, and Ben Cox focus on the technical issues of ML as well as human-centered issues such as security, fairness, and privacy. The goal is to promote human safety in ML practices so that in the near future, there will be no need to differentiate between the general practice and the responsible practice of ML. This report explores: People: Humans in the Loop --Why an organization's ML culture is an important aspect of responsible ML practice Processes: Taming the Wild West of Machine Learning Workflows --Suggestions for changing or updating your processes to govern ML assets Technology: Engineering ML for Human Trust and Understanding --Tools that can help organizations build human trust and understanding into their ML systems Actionable Responsible ML Guidance --Core considerations for companies that want to drive value from ML. Gill, Navdeep MitwirkendeR ctb Hall, Patrick MitwirkendeR ctb |
| spellingShingle | Cox, Benjamin Responsible Machine Learning |
| title | Responsible Machine Learning |
| title_auth | Responsible Machine Learning |
| title_exact_search | Responsible Machine Learning |
| title_full | Responsible Machine Learning Benjamin Cox |
| title_fullStr | Responsible Machine Learning Benjamin Cox |
| title_full_unstemmed | Responsible Machine Learning Benjamin Cox |
| title_short | Responsible Machine Learning |
| title_sort | responsible machine learning |
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