Challenges in Data Preservation for AI and ML Systems

Emma L. Tonkin; Gregory J. L. Tourte

Challenges in Data Preservation for AI and ML Systems

Emma L. Tonkin, Gregory J. L. Tourte

Research output: Contribution to conference › Conference Paper › peer-review

Abstract

The management and preservation of machine learning (ML) and artificial intelligence (AI) data is increasingly a concern for research institutions, as well as for institutions and industry organisations making use of this type of data and method. This paper summarises key issues in this area, presenting the case that there are significant benefits to the industry in developing best practices and joint standards in this area, and identifying the benefits of this approach, as well as highlighting risks and a current paucity of best practice in the area.

Original language	English
Publication status	Accepted/In press - 1 Jul 2024
Event	8th International Workshop on Annotation of useR Data for UbiquitOUs Systems - Wiesbaden, Germany Duration: 24 Sept 2024 → … https://arduous.eu/

Workshop

Workshop	8th International Workshop on Annotation of useR Data for UbiquitOUs Systems
Abbreviated title	ARDUOUS 2024
Country/Territory	Germany
City	Wiesbaden
Period	24/09/24 → …
Internet address	https://arduous.eu/

Keywords

data preservation
data management
artificial intelligence
machine learning
best practices

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AB - The management and preservation of machine learning (ML) and artificial intelligence (AI) data is increasingly a concern for research institutions, as well as for institutions and industry organisations making use of this type of data and method. This paper summarises key issues in this area, presenting the case that there are significant benefits to the industry in developing best practices and joint standards in this area, and identifying the benefits of this approach, as well as highlighting risks and a current paucity of best practice in the area.

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KW - data management

KW - artificial intelligence

KW - machine learning

KW - best practices

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ER -

Challenges in Data Preservation for AI and ML Systems

Abstract

Workshop

Keywords

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