De-identification policy and risk distribution framework for securing personal information

Joo, Moon Ho; Yoon, Sang Pil; Kwon, Hun Yeong; Lim, Jong In

doi:10.3233/ip-170057

Cited by 5 publications

(1 citation statement)

References 1 publication

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“…Another example of PbD principles is privacy as default. In farm technologies, de-identification at the source can enable default privacy and address identifiability concerns before storing or sharing farm data (Joo et al, 2018). This approach separates personal information from records stored in the data tables and stores them in other locations (Raji et al, 2020).…”

Section: Recommendations For Agricultural Technology Providersmentioning

confidence: 99%

Recommendations for ethical and responsible use of artificial intelligence in digital agriculture

Dara

Fard

Kaur

2022

Front. Artif. Intell.

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Artificial intelligence (AI) applications are an integral and emerging component of digital agriculture. AI can help ensure sustainable production in agriculture by enhancing agricultural operations and decision-making. Recommendations about soil condition and pesticides or automatic devices for milking and apple picking are examples of AI applications in digital agriculture. Although AI offers many benefits in farming, AI systems may raise ethical issues and risks that should be assessed and proactively managed. Poor design and configuration of intelligent systems may impose harm and unintended consequences on digital agriculture. Invasion of farmers' privacy, damaging animal welfare due to robotic technologies, and lack of accountability for issues resulting from the use of AI tools are only some examples of ethical challenges in digital agriculture. This paper examines the ethical challenges of the use of AI in agriculture in six categories including fairness, transparency, accountability, sustainability, privacy, and robustness. This study further provides recommendations for agriculture technology providers (ATPs) and policymakers on how to proactively mitigate ethical issues that may arise from the use of AI in farming. These recommendations cover a wide range of ethical considerations, such as addressing farmers' privacy concerns, ensuring reliable AI performance, enhancing sustainability in AI systems, and reducing AI bias.

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Section: Recommendations For Agricultural Technology Providersmentioning

confidence: 99%

Recommendations for ethical and responsible use of artificial intelligence in digital agriculture

Dara

Fard

Kaur

2022

Front. Artif. Intell.

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Is big data used by cities? Understanding the nature and antecedents of big data use by municipalities

Ali

Titah

2021

Government Information Quarterly

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The Study of Privacy Protection of Scientific Data Sharing Based on Data Life Cycle

Song

Chen

2021

J. Phys.: Conf. Ser.

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Scientific data sharing has become an important activity to promote modern research findings in which helps to reduce costs and save time for data collection, but it also brings certain privacy issues when using scientific data. First, based the network and literature survey method to described the basic conceptual of scientific data sharing, and discussed the issues of privacy violation in scientific data sharing during the data lifecycle. Second, tried to propose a privacy protection model and framework to prevent privacy violation. Finally, provided some suggestions for the current privacy protection in scientific data sharing from different perspectives. The study contributes to the scientific data sharing by shedding light on how to protect privacy of scientific data sharing through the privacy protection model and framework.

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De-identification policy and risk distribution framework for securing personal information

Cited by 5 publications

References 1 publication

Recommendations for ethical and responsible use of artificial intelligence in digital agriculture

Recommendations for ethical and responsible use of artificial intelligence in digital agriculture

Is big data used by cities? Understanding the nature and antecedents of big data use by municipalities

The Study of Privacy Protection of Scientific Data Sharing Based on Data Life Cycle

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