Big Data Privacy and Ethical Challenges

Lacroix, Paulette

doi:10.1007/978-3-030-06109-8_9

Cited by 55 publications

(6 citation statements)

References 2 publications

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“…This involves engaging with core problems of metaethics and ensuring that AI systems are designed with careful consideration of their moral implications. Lacroix (2019) explores the challenges posed by big data privacy and ethics in the context of AI. The study discusses how big data analytics, underpinned by AI and machine learning, challenge compliance with information privacy principles.…”

Section: Addressing Data and Ethical Challenges In Ai Modelingmentioning

confidence: 99%

Ai-Driven Environmental Health Disease Modeling: A Review of Techniques and Their Impact on Public Health in the Usa and African Contexts

Nzubechukwu Chukwudum Ohalete,

Oluwatoyin Ayo-Farai,

Tolulope O Olorunsogo

et al. 2024

imsrj

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This scholarly paper embarks on an exploratory journey into the realm of AI-driven environmental health disease modeling, with a keen focus on its implications in the diverse healthcare landscapes of the USA and Africa. The study's background delves into the historical evolution of disease modeling techniques, emphasizing the revolutionary role of AI in modern public health strategies. It meticulously examines the comparative effectiveness of AI models in these distinct regions, addressing the challenges and opportunities inherent in AI-driven health models. Aiming to unravel the multifaceted impact of AI in disease prediction and public health policy, the paper navigates through various thematic corridors. It critically analyzes the significance of data sources and quality, ethical considerations in AI health modeling, and the integration of AI models into public health policies. The scope of the paper encompasses a comprehensive review of AI's efficacy in predicting environmental diseases, its role in enhancing disease surveillance systems, and the geographic and socioeconomic variations affecting model accuracy. The main findings reveal that AI models, while effective in disease prediction and surveillance, encounter challenges related to data integrity and ethical complexities. The study concludes that the integration of AI in healthcare necessitates a balanced approach, advocating for policies that support the development of context-specific AI models and address ethical concerns. Recommendations include fostering interdisciplinary collaboration and continuous evaluation of AI models to align them with evolving healthcare needs and ethical standards. This paper serves as a beacon for understanding AI's transformative potential in environmental health disease modeling, offering insights that are crucial for shaping future public health strategies and interventions. Keywords: AI in Healthcare, Disease Modeling, Public Health Policy, Data Quality, Ethical Considerations, Geographic Variations.

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Section: Addressing Data and Ethical Challenges In Ai Modelingmentioning

confidence: 99%

Ai-Driven Environmental Health Disease Modeling: A Review of Techniques and Their Impact on Public Health in the Usa and African Contexts

Nzubechukwu Chukwudum Ohalete,

Oluwatoyin Ayo-Farai,

Tolulope O Olorunsogo

et al. 2024

imsrj

View full text Add to dashboard Cite

show abstract

“…Big data thus offers a great opportunity for doctors, epidemiologists, and specialists in health policy to make evidence-driven decisions that will eventually enhance patient care [34,35]. "Big data is not only a modern reality for the biomedical scientist but an imperative that needs to be fully grasped and used in the search for new knowledge" [36][37][38].…”

Section: Big Data In the Healthcare Systemmentioning

confidence: 99%

“…Data in the healthcare sector are usually huge and not easy to handle [38][39][40]. This results from the enormous way by which data grows in the health care sector, the rate at which data are been produced, and the variety of various data in the healthcare system [41,42].…”

Section: Big Data In the Healthcare Systemmentioning

confidence: 99%

Prediction of Malaria Fever Using Long-Short-Term Memory and Big Data

Awotunde

Jimoh

Oladipo

et al. 2021

Communications in Computer and Information Science

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Malaria has been identified to be one of the most common diseases with a great public health problem globally and it is caused by mosquitos' parasites. This prevails in developing nations where healthcare facilities are not enough for the patients. The technological advancement in medicine has resulted in the collection of huge volumes of data from various sources in different formats. A reliable and early parasite-based diagnosis, identification of symptoms, disease monitoring, and prescription are crucial to decreasing malaria occurrence in Nigeria. Hence, the use of deep and machine learning models is essentials to reduce the effect of malaria-endemic and for better predictive models. Therefore, this paper proposes a framework to predict malaria-endemic in Nigeria. To predict the malaria-endemic well, both environmental and clinical data were used using Kwara State as a case study. The study used a deep learning algorithm as a classifier for the proposed system. Three locations were selected from Irepodun Local Government Areas of Kwara State with 34 months periodic pattern. Each location reacted differently based on environmental factors. The findings indicate that both factors are significant in malaria prediction and transmission. The LSTM algorithm provides an efficient method for detecting situations of widespread malaria.

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“…Philosophically, predictive analytics prompts profound inquiries into the nature of determinism, the role of human agency, and the ethical considerations entwined with leveraging vast troves of data for predictive insights (Kitchin, 2014). As algorithms gain unprecedented predictive power, ethical dilemmas emerge, questioning the boundaries between foresight and individual autonomy (Lacroix, 2019, Amann et al, 2023. The overview explores these philosophical dimensions, addressing the ethical implications of data-driven decision-making, the challenges of safeguarding privacy, and the imperative to grapple with biases inherent in predictive models.…”

Section: Introductionmentioning

confidence: 99%

Advancements in predictive analytics: A philosophical and practical overview

Oluwatoyin Ajoke Farayola,

Ejuma Martha Adaga,

Zainab Efe Egieya

et al. 2024

World J. Adv. Res. Rev.

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This review would summarize recent advancements in predictive analytics within the financial industry, exploring both the technological developments and their philosophical implications. It would evaluate the balance between data-driven decision-making and ethical considerations. Predictive analytics has witnessed remarkable advancements, blending philosophical considerations with practical applications to redefine decision-making processes across various industries. This abstract provides an overview of the philosophical underpinnings and practical implications of the evolving landscape of predictive analytics. Philosophically, predictive analytics raises profound questions about determinism, human agency, and the ethical implications of data-driven decision-making. The abstract explores the tension between the predictive power of algorithms and the need to preserve individual autonomy, delving into the ethical considerations surrounding privacy, bias, and accountability. Practically, the overview navigates the cutting-edge tools and techniques that drive predictive analytics. From machine learning algorithms to big data analytics, the abstract examines how these technologies empower organizations to make data-driven predictions, optimize processes, and gain actionable insights. Real-world applications in business, healthcare, finance, and other domains underscore the transformative impact of predictive analytics on operational efficiency and strategic decision-making. As predictive analytics continues to shape the future of information processing, this abstract encapsulates the dual nature of its evolution – a philosophical exploration of its ethical dimensions and a practical examination of its applications, offering a holistic understanding of the field's implications for individuals, organizations, and society at large.

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Big Data Privacy and Ethical Challenges

Cited by 55 publications

References 2 publications

Ai-Driven Environmental Health Disease Modeling: A Review of Techniques and Their Impact on Public Health in the Usa and African Contexts

Ai-Driven Environmental Health Disease Modeling: A Review of Techniques and Their Impact on Public Health in the Usa and African Contexts

Prediction of Malaria Fever Using Long-Short-Term Memory and Big Data

Advancements in predictive analytics: A philosophical and practical overview

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