Developing an Ethical Framework for Responsible Artificial Intelligence (AI) and Machine Learning (ML) Applications in Cryptocurrency Trading: A Consequentialism Ethics Analysis

Alibašić, Haris

doi:10.3390/fintech2030024

Cited by 5 publications

(1 citation statement)

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“…This perspective calls for a broader understanding of ethical AI, one that encompasses the operational contexts in which AI systems operate. Alibašić (2023) proposes an ethical framework for the responsible application of AI and Machine Learning (ML) in cryptocurrency trading, employing a consequentialist ethics analysis. This framework emphasizes the outcomes of AI and ML deployment in the sector and its effects on stakeholders, advocating for regulatory methods that address the unique characteristics of digital assets.…”

Section: Examining Regulatory and Ethical Considerations Of Aimentioning

confidence: 99%

The influence of AI on financial reporting quality: A critical review and analysis

Lawrence Damilare Oyeniyi,

Chinonye Esther Ugochukwu,

Noluthando Zamanjomane Mhlongo

2024

World J. Adv. Res. Rev.

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In an era where the confluence of Artificial Intelligence (AI) and financial reporting is reshaping the contours of financial analysis and accountability, this paper ventures into the heart of this transformation. With a purposeful gaze, it examines the burgeoning role of AI in enhancing the quality, accuracy, and timeliness of financial reporting. The study, anchored in a qualitative research methodology, meticulously explores the integration of AI technologies within the financial reporting landscape, aiming to illuminate the pathways through which AI can augment traditional reporting practices. Through the lens of this inquiry, the paper traverses the evolution of financial reporting, the foundational principles of AI, and the symbiotic relationship between AI applications and financial analytics, culminating in a nuanced understanding of AI's potential to revolutionize financial reporting. The main findings reveal that AI significantly enhances reporting accuracy, analytical depth, and efficiency, while also presenting challenges related to ethical considerations, regulatory compliance, and the potential for biases. These insights pave the way for a set of carefully articulated recommendations, advocating for the standardization of AI systems in financial reporting, the development of comprehensive regulatory frameworks, and the promotion of AI literacy among financial professionals. Conclusively, the paper posits that the strategic integration of AI into financial reporting is not merely an option but a necessity for the advancement of the field, urging stakeholders to embrace this technological evolution with a balanced approach that harmonizes innovation with ethical and regulatory imperatives. This scholarly endeavor not only contributes to the academic discourse on AI in financial reporting but also serves as a beacon for practitioners navigating the complexities of this digital transformation.

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Section: Examining Regulatory and Ethical Considerations Of Aimentioning

confidence: 99%