The integration of AIML in cryptocurrency trading raises ethical concerns, demanding a thorough examination of accountable approaches. This research focuses on evaluating the outcomes of AI and ML in cryptocurrency trading, emphasizing the need for innovative regulatory strategies. Utilizing critical case studies like Sam Bankman-Fried (SBF) and FTX and reviewing relevant literature, it explores the ethical dimensions of AI and ML in this domain. The study advocates for regulatory frameworks that accommodate digital assets' unique features while aligning with existing legal structures. It proposes a structural framework for AI and ML-driven trading, juxtaposing it with alternative ethical theories. From an outcome-oriented perspective, the research emphasizes balancing the potential of AI and ML with ethical considerations, ensuring market integrity, safeguarding investor interests, and promoting overall welfare in the cryptocurrency trading landscape.
TopIntroduction
The surge in the use of artificial intelligence (AI) and machine learning (ML) in cryptocurrency trading has raised significant ethical concerns related to transparency, accountability, and responsible trading practices (OECD, 2021). This study aims to address these issues by adopting a consequentialist perspective in analyzing the application of AI and ML in cryptocurrency trading. The investigation is substantiated by an in-depth literature review and a case study involving Sam Bankman-Fried (SBF) and FTX, intending to provide a well-founded exploration and fill existing gaps in understanding the ethical implications of AI in this domain (Cao, 2020; Cao et al., 2021; Hamayel & Owda, 2021; Kumar et al., 2023).
Cryptocurrencies, with their decentralized nature and lack of clear legal frameworks, pose regulatory challenges distinct from well-regulated traditional financial markets. While AI in finance offers advantages such as faster decision-making, it also introduces risks like manipulation and insider trading. The inherent opacity of AI models further complicates matters, necessitating scrutiny and regulatory measures to ensure ethical and responsible applications of AI in cryptocurrency trading. The study underscores the importance of adopting a nuanced ethical approach and utilizes the SBF and FTX cases to highlight crucial ethical considerations (Miura et al., 2019; Patel et al., 2020).
Fama's Efficient Market Hypothesis (EMH), asserting that financial markets are “informationally efficient,” is challenged by the integration of AI and ML in cryptocurrency trading. The extreme volatility of cryptocurrencies attracts global investors, and AI and ML algorithms, capable of processing vast data and predicting price movements, question the assumption of informational efficiency. The lack of transparency in AI-driven systems adds complexity, potentially distorting information asymmetry in the market (Sadman et al., 2022; Sebastião & Godinho, 2021).
The consequentialist perspective and ethical responsibility in the financial sector's adoption of AI and ML demand thorough exploration, considering both potential benefits and risks. While these technologies enhance efficiency, they also introduce financial and non-financial risks that impact institutions and consumers. Understanding AI and ML models is crucial for effective policymaking, leading the paper to advocate for a reinforced ethical focus and prudential oversight in the financial sector (Makarov & Schoar, 2022; Narain & Moretti, 2022).
Drawing inspiration from Mill's works, the study formulates a consequentialist framework that evaluates the morality of AI's role in cryptocurrency trading based on its outcomes. Consequentialism dictates the design of AI algorithms and trading strategies to enhance market efficiency and stability while mitigating adverse outcomes. The framework takes into account various stakeholders, ensuring ethical and responsible AI application (Bahordia et al., 2023).
The research methods involve a systematic literature review and a case study approach. The literature review, including 105 articles and books, employed a comprehensive search strategy using keywords related to AI, cryptocurrency trading, ethics, and consequentialism. Data extraction and analysis adhered to established guidelines. The case study focused on the charges against SBF, exploring the intersection of effective altruism and AI ethics. While the research methods aim for rigor and transparency, acknowledging limitations such as the potential omission of relevant articles and sample size constraints, future research could overcome these by expanding the literature review, conducting expert interviews, and analyzing multiple case studies. Additionally, quantitative methods like surveys or experiments could further explore the ethical implications of AI in trading (Bin Sarhan & Altwaijry, 2023; Boukherouaa et al., 2021).