Extracting enhanced artificial intelligence model metadata from software repositories

Tsay, Jason; Braz, Alan; Hirzel, Martin; Shinnar, Avraham; Mummert, Todd

doi:10.1007/s10664-022-10206-6

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…In addition to the required data, further information about the data, referred to as metadata, is collected in catalogs to facilitate the organization and usage of the data. Metadata can also hold information about access rights, data ownership, data controllers, third parties, usage purpose, retention, or other relevant information about the maintenance of data and the AI system relevant to privacy concerns [44]. Therefore, this phase should address the requirements for both data and metadata.…”

Section: B Data Collectionmentioning

confidence: 99%

A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle

et al. 2023

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Over the decades, Artificial Intelligence (AI) and machine learning has become a transformative solution in many sectors, services, and technology platforms in a wide range of applications, such as in smart healthcare, financial, political, and surveillance systems. In such applications, a large amount of data is generated about diverse aspects of our life. Although utilizing AI in real-world applications provides numerous opportunities for societies and industries, it raises concerns regarding data privacy. Data used in an AI system are cleaned, integrated, and processed throughout the AI life cycle. Each of these stages can introduce unique threats to individual's privacy and have an impact on ethical processing and protection of data. In this paper, we examine privacy risks in different phases of the AI life cycle and review the existing privacy-enhancing solutions. We introduce four different categories of privacy risk, including (i) risk of identification, (ii) risk of making an inaccurate decision, (iii) risk of non-transparency in AI systems, and (iv) risk of non-compliance with privacy regulations and best practices. We then examined the potential privacy risks in each AI life cycle phase, evaluated concerns, and reviewed privacy-enhancing technologies, requirements, and process solutions to countermeasure these risks. We also reviewed some of the existing privacy protection policies and the need for compliance with available privacy regulations in AI-based systems. The main contribution of this survey is examining privacy challenges and solutions, including technology, process, and privacy legislation in the entire AI life cycle. In each phase of the AI life cycle, open challenges have been identified.

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Section: B Data Collectionmentioning

confidence: 99%

A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle

et al. 2023

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“…Natural language processing is part of current metadata extraction tools (Méndez et al, 2021) and has proven successful in a wide variety of domains such as e‐commerce (Paik et al, 2001), educational resources (Yilmazel et al, 2004), bio‐medical documents (Caufield et al, 2018; Valdez et al, 2016), legislative texts (Sleimi et al, 2018; Spinosa et al, 2009), software repositories (Tsay et al, 2020), and even archeological digital archives (Felicetti et al, 2018). These works show how to effectively integrate information extraction modules into processing pipelines to catalog large document libraries.…”

Section: Introductionmentioning

confidence: 99%

Addressing structural hurdles for metadata extraction from environmental impact statements

Laparra,

Binford‐Walsh,

Emerson

et al. 2023

Asso for Info Science & Tech

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Natural language processing techniques can be used to analyze the linguistic content of a document to extract missing pieces of metadata. However, accurate metadata extraction may not depend solely on the linguistics, but also on structural problems such as extremely large documents, unordered multi-file documents, and inconsistency in manually labeled metadata. In this work, we start from two standard machine learning solutions to extract pieces of metadata from Environmental Impact Statements, environmental policy documents that are regularly produced under the US National Environmental Policy Act of 1969. We present a series of experiments where we evaluate how these standard approaches are affected by different issues derived from real-world data.We find that metadata extraction can be strongly influenced by nonlinguistic factors such as document length and volume ordering and that the standard machine learning solutions often do not scale well to long documents. We demonstrate how such solutions can be better adapted to these scenarios, and conclude with suggestions for other NLP practitioners cataloging large document collections.

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PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software

Jiang,

Yasmin,

Jones

et al. 2024

Proceedings of the 21st International Conference on Mining Software Repositories

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Extracting enhanced artificial intelligence model metadata from software repositories

Cited by 3 publications

References 37 publications

A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle

A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle

Addressing structural hurdles for metadata extraction from environmental impact statements

PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software

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