Informed consent and algorithmic discrimination – is giving away your data the new vulnerable?

Behrendt, Hauke; Loh, Wulf

doi:10.1080/00346764.2022.2027506

Cited by 3 publications

(1 citation statement)

References 53 publications

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“…Issues such as gender, race, or socioeconomic bias can emerge, leading to unfair treatment and reinforcing systemic inequalities. Addressing algorithmic bias requires thorough scrutiny of training datasets, continuous monitoring of model outputs, and efforts to enhance transparency and accountability in algorithmic decision-making processes [73]- [75]. As big data analytics increasingly influences critical decisions in various domains, mitigating bias becomes paramount to ensure fair, equitable, and ethical outcomes.…”

Section: Algorithmic Bias and Discriminationmentioning

confidence: 99%

Tackling security and privacy challenges in the realm of big data analytics

Janet Ngesa

2023

World J. Adv. Res. Rev.

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As organizations increasingly harness the power of big data analytics to derive insights and drive decision-making, the paramount concerns of security and privacy have come to the forefront. This paper presents a comprehensive framework for addressing the multifaceted challenges of big data security and privacy. Drawing on a synthesis of cutting-edge technologies, encryption methods, and access control mechanisms, our approach aims to fortify the entire big data lifecycle. The paper delves into innovative strategies for secure data storage, transmission, and processing, ensuring that sensitive information is shielded from unauthorized access or malicious attacks. Additionally, the framework incorporates robust privacy-preserving techniques, including anonymization and differential privacy, to uphold individual confidentiality. Through a meticulous analysis of current trends, emerging threats, and regulatory landscapes, this paper not only provides theoretical insights but also practical guidelines for organizations seeking to navigate the intricate landscape of big data while safeguarding the integrity, security, and privacy of the vast datasets at their disposal.

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Section: Algorithmic Bias and Discriminationmentioning

confidence: 99%

Tackling security and privacy challenges in the realm of big data analytics

Janet Ngesa

2023

World J. Adv. Res. Rev.

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Was ist digitale Teilhabe? Anmerkungen zu den Gefahren digitaler Spaltung in einer zunehmend vernetzten Welt

Behrendt

2024

Digitalitätsforschung / Digitality Research

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Privacy frontiers in customers’ relations with banks

Piotrowski¹

2023

EBR

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The widespread use of digital technologies in banking allows banks to obtain and analyse huge amounts of data from different communication channels. While this phenomenon is conducive to improving the quality of services it also increases the risk of privacy breaches. The aim of this study is to identify what factors determine consumer acceptance of banks’ use of public access personal data found on social media accounts. The results indicate the importance of the financial incentive and consumers’ assessment of banks’ information activities regarding the processing of personal data. Determinants relating to the technological sophistication of respondents were also found to be significant, with a particular focus on the ethical evaluation of decisions made by Artificial Intelligence algorithms. The results of the work may be used by banks in practice to adapt the area of personal data management to the requirements of e-privacy and Trustworthy Artificial Intelligence.

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Informed consent and algorithmic discrimination – is giving away your data the new vulnerable?

Cited by 3 publications

References 53 publications

Tackling security and privacy challenges in the realm of big data analytics

Tackling security and privacy challenges in the realm of big data analytics

Was ist digitale Teilhabe? Anmerkungen zu den Gefahren digitaler Spaltung in einer zunehmend vernetzten Welt

Privacy frontiers in customers’ relations with banks

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