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Citation: Alibaši´c, H. Developing an
Ethical Framework for Responsible
Artificial Intelligence (AI) and
Machine Learning (ML) Applications
in Cryptocurrency Trading: A
Consequentialism Ethics Analysis.
FinTech 2023,2, 430–443.
https://doi.org/10.3390/
fintech2030024
Academic Editor: David Roubaud
Received: 29 May 2023
Revised: 18 June 2023
Accepted: 28 June 2023
Published: 3 July 2023
Copyright: © 2023 by the author.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Developing an Ethical Framework for Responsible Artificial
Intelligence (AI) and Machine Learning (ML) Applications in
Cryptocurrency Trading: A Consequentialism Ethics Analysis
Haris Alibaši´c
Public Administration Program, University of West Florida, 11000 University Pkwy, Pensacola, FL 32514, USA;
halibasic@uwf.edu
Abstract:
The rise in artificial intelligence (AI) and machine learning (ML) in cryptocurrency trading
has precipitated complex ethical considerations, demanding a thorough exploration of responsible
regulatory approaches. This research expands upon this need by employing a consequentialist
theoretical framework, emphasizing the outcomes of AI and ML’s deployment within the sector and
its effects on stakeholders. Drawing on critical case studies, such as SBF and FTX, and conducting an
extensive review of relevant literature, this study explores the ethical implications of AI and ML in
the context of cryptocurrency trading. It investigates the necessity for novel regulatory methods that
address the unique characteristics of digital assets alongside existing legalities, such as those about
fraud and insider trading. The author proposes a typology framework for AI and ML trading by
comparing consequentialism to other ethical theories applicable to AI and ML use in cryptocurrency
trading. By applying a consequentialist lens, this study underscores the significance of balancing AI
and ML’s transformative potential with ethical considerations to ensure market integrity, investor
protection, and overall well-being in cryptocurrency trading.
Keywords:
artificial intelligence; cryptocurrency trading; consequentialism ethics; regulatory
approaches; case studies; market integrity; investor protection
1. Introduction
Artificial intelligence (AI) and machine learning (ML) have experienced burgeoning
usage in cryptocurrency trading, raising critical ethical questions around transparency,
accountability, and responsible trading [
1
–
3
]. This study bridges the gap in current un-
derstanding by providing a consequentialist perspective on AI and ML application in
cryptocurrency trading, further substantiated through a comprehensive literature review
and an analysis of a case study involving Sam Bankman-Fried (SBF) and FTX to deepen the
discussion and provide a grounded perspective. This approach offers an opportunity to
incorporate the technical aspects of AI and ML into cryptocurrency trading [4–9].
Cryptocurrencies present unique regulatory challenges due to their decentralized na-
ture and lack of legal clarity. Unlike traditional financial markets, which are well-regulated
and have apparent legal oversight, cryptocurrencies operate in a “gray” legal area [
10
,
11
].
While AI brings potential benefits to the finance sector, including faster, more efficient
decision-making and cost reduction, it also introduces the potential for manipulation and
insider trading [
12
,
13
]. Furthermore, the opaque nature of AI models amplifies the risks,
challenging existing financial supervision and governance frameworks, potentially leading
to financial instability and biased, unfair, or discriminatory results [
14
–
22
]. Friedman,
1970 [
21
] The pressing need for scrutiny underlines the study’s primary objective: to exam-
ine the ethical implications of AI in cryptocurrency trading through a consequentialist lens
and propose regulatory approaches that promote ethical and responsible AI applications in
this sector. The rapid integration of AI and ML technologies demands a nuanced ethical
FinTech 2023,2, 430–443. https://doi.org/10.3390/fintech2030024 https://www.mdpi.com/journal/fintech
FinTech 2023,2431
approach. Utilizing the case of SBF and FTX, this paper seeks to expose the necessary
ethical considerations.
Additionally, Fama’s (1970) seminal work on the Efficient Market Hypothesis (EMH)
posits that financial markets are “informationally efficient”, that is, at any given time,
prices fully reflect all available information [
23
]. The EMH suggests that it is impossible
to consistently achieve returns over average market returns once the investment cost
is considered. The EMH has been a guiding principle for much of modern financial
market architecture.
However, the advent of AI and ML in trading, particularly in cryptocurrencies, poses
new questions for this long-standing paradigm. Cryptocurrencies are decentralized digital
assets that operate on blockchain, essentially a distributed ledger enforced by a disparate
network of computers. They are known for their extreme volatility, which has attracted
many investors and traders globally [24].
AI and ML algorithms have been leveraged to predict price movements in the volatile
cryptocurrency markets. They can process and analyze vast amounts of data at speeds far
beyond human capability, potentially gaining insights and making predictions that human
traders would not be able to generate. The increased data processing and analytical capacity
supply certain market participants with an informational advantage, challenging the
assumption of informational efficiency [
25
]. Moreover, the transparency and accountability
of AI- and ML-driven trading systems are significant concerns. AI algorithms are complex
and often function as a ‘black box’, with their decision-making processes challenging to
interpret. The lack of transparency could further distort the information asymmetry in the
market, contrary to the EMH principles [26].
The consequentialist perspective and ethical responsibility in the financial sector’s
burgeoning AI and ML adoption must be sufficiently explored. AI and ML, making
significant strides in recent years, have found extensive applications in the financial domain,
yielding potential benefits like enhanced efficiency and cost reduction. However, the
introduction of AI and ML in this realm also portends the creation or amplification of
various financial and non-financial risks, including pro-cyclicality, systemic risk, biased
consumer outcomes, and data management and use issues. These factors may negatively
impact financial institutions and consumers.
Comprehending the workings of AI and ML models that generate results could
challenge the technology-neutral approach to policymaking and the existing frameworks
for financial supervision and internal governance. This paper reviews the unique risks AI
and ML pose to the financial system’s integrity, ethics, and stability and suggests potential
regulatory approaches. Given that the full scope of AI and ML’s benefits and limitations
in finance is not entirely understood, the paper argues for a strengthened ethical focus on
prudential oversight.
Policymakers and practitioners will find the research relevant in understanding AI
and ML’s possible financial risks and challenges, reinforcing their regulatory framework to
endorse the ethical and responsible use of AI and ML in the financial sector. This study aims
to construct a typology of ethical frameworks applicable to AI and ML in cryptocurrency
trading, emphasizing consequentialism.
The paper posits a comprehensive consequentialist framework for AI and ML ap-
plication in cryptocurrency trading, spotlighting the maximization of societal welfare,
circumvention of stakeholder harm, and fostering transparency and accountability. The
framework is the bedrock for guidelines to steer the development and implementation of
AI and ML in cryptocurrency trading, a critical element in maintaining public trust and
reaping AI and ML benefits in cryptocurrency.
Drawing from Mill’s (1863) works, the study proposes a consequentialist framework
to explore AI’s role in cryptocurrency trading [
27
,
28
]. This ethical principle focuses on
evaluating an action’s morality based on its outcomes, underscoring the ultimate goal of
cryptocurrency trading: maximizing societal welfare [
29
]. Thus, the decision to employ AI
FinTech 2023,2432
in trading should consider its potential to fulfil this goal while avoiding societal harm or
negative governance [30,31].
Consequentialism mandates the design of AI and ML algorithms and trading strategies
to bolster market efficiency and stability while curtailing adverse outcomes like market
manipulation, insider trading, or fraud. A balanced approach should consider various
stakeholders, including traders, investors, regulators, and the public, to ensure AI’s ethical
and responsible application.
In contrast, other ethical theories, such as deontological ethics, emphasize adhering
to moral rules and duties, irrespective of the outcomes, while virtue ethics underlines
the cultivation of desirable character traits like honesty, empathy, and courage (Aristotle,
Nicomachean Ethics) [
32
–
34
]. Deontological ethics in cryptocurrency trading demand the
design of AI and ML algorithms and trading strategies to comply with ethical and legal
standards, incorporating provisions against market manipulation, insider trading, and
fraud. AI and ML algorithms and trading strategies should be transparent, auditable, and
accountable, prioritizing all stakeholders’ interests.
The virtue ethics approach stresses the importance of character and integrity in ethical
decision-making. In cryptocurrency trading, this implies designing AI algorithms and
trading strategies to foster responsible and ethical behavior, promoting character traits such
as honesty, transparency, and accountability.
The proposed framework has three components:
- Maximizing societal welfare;
- Avoiding harm to stakeholders;
- Promoting transparency and accountability.
The first component underscores the importance of maximizing societal welfare in
cryptocurrency trading. It entails enhancing AI and ML’s benefits in trading and mitigating
their possible adverse outcomes. AI and ML’s algorithms should be designed to optimize
for various social welfare objectives, such as market efficiency, stability, and fairness.
The second component focuses on avoiding harm to stakeholders, including individual
traders, investors, and the larger community. Harm occurs through different channels, such
as market manipulation, insider trading, and fraud, facilitated by AI and ML in trading.
Thus, evaluating the potential for harm when contemplating AI use in cryptocurrency
trading and implementing protective measures to minimize risk is crucial.
The third component highlights the promotion of transparency and accountability,
which is crucial for maintaining public trust in the cryptocurrency industry and ensuring
that the benefits of AI in trading are realized. Measures, including open-source algorithms,
regular audits, and regulatory oversight, ensure that the use of AI in trading is transparent
and auditable to prevent unethical behavior.
To operationalize the framework, a set of guidelines has been developed to guide the
development and implementation of AI in cryptocurrency trading. The guidelines outline
best practices for AI use in trading and frame the evaluation of AI’s ethical implications in
trading decisions. They also include measures promoting transparency and accountability,
such as regular audits and reporting requirements.
In summary, the proposed framework for ethical considerations in AI applications in
cryptocurrency trading is grounded in consequentialism, emphasizing maximizing societal
welfare, avoiding harm to stakeholders, and promoting transparency and accountabil-
ity. The consequentialist framework provides a valuable tool for evaluating the ethical
implications of AI in cryptocurrency trading. It guides the industry in developing and
implementing responsible AI applications and ensures that we avoid the pitfalls of negative
governance [31].
2. Materials and Methods
This section describes the procedures and techniques used in the study to achieve the
development of an ethical framework for utilizing AI in cryptocurrency trading. A sys-
tematic literature review was conducted to identify relevant sources on AI, cryptocurrency
FinTech 2023,2433
trading, and ethical frameworks. A total of one hundred articles, research documents and
books were included in the review, covering a range of topics related to AI in finance, ethics,
and ethical considerations. To begin with, a thorough literature review was conducted
to identify various ethical frameworks that could be applied to AI and cryptocurrency
trading. This involved searching various databases such as Google Scholar, Scopus, and
Web of Science using specific keywords such as “ethics”, “consequentialism”, “AI”, and
“cryptocurrency trading”. To ensure transparency and replicability of the study, all methods
and protocols used were appropriately cited [35].
The literature review was conducted using a comprehensive search strategy, including
keywords such as “AI”, “cryptocurrency”, “trading”, “ethics”, and “consequentialism”.
Articles were selected based on relevance to the research question, and exclusion criteria
included non-English language publications and articles that were not peer-reviewed. Data
extraction was carried out using a standardized form, which included information on the
author, year of publication, research question, methodology, findings, and limitations. The
data extracted from the literature review were analyzed using a qualitative content analysis
approach, which involved the identification of themes and patterns in the data.
The themes that emerged from the analysis were used to develop a typology of ethical
frameworks, and then compared to various consequentialist theories. In addition to the
literature review, a case study approach was used to explore the ethical considerations of
using AI in cryptocurrency trading. A case study was selected based on its relevance to the
research question and data availability. The case study involved an analysis of the charges
against Sam Bankman-Fried, who was accused of engaging in insider trading using AI.
The case study was analyzed using the typology of ethical frameworks developed from the
literature review and expert interviews.
The methods used in this research paper were designed to be rigorous and transparent,
allowing for replication and the building of future research on this topic. The systematic
literature review followed established guidelines for conducting a comprehensive literature
review. However, there are limitations to this research paper that should be acknowledged.
While comprehensive, the sample size of the literature review may have included only
some relevant articles on the topic.
To address these limitations, future research can expand the sample size of the lit-
erature review, conduct expert interviews, and analyze multiple case studies to offer a
comprehensive understanding of the ethical considerations related to the use of AI in
cryptocurrency trading. Furthermore, future research could also use quantitative methods,
such as surveys or experiments, to further explore the ethical implications of AI in trading.
Overall, the methods used in this research paper have a strong foundation for develop-
ing a typology of ethical frameworks comparing various theories that could be applied to
AI in cryptocurrency trading, emphasizing consequentialism. The research paper outcomes
have relevant implications for policymakers, regulators, and practitioners in the financial
industry, highlighting the need for responsible and ethical use of AI in trading to ensure
market integrity and protect investors.
A systematic literature review was conducted to identify relevant sources on AI,
cryptocurrency trading, and ethical frameworks. A total of one hundred and five articles
and books were included in the review, covering a range of topics related to AI in finance
and ethical considerations. To begin with, a thorough literature review was conducted
to identify various ethical frameworks that could be applied to AI and cryptocurrency
trading [
32
–
34
,
36
–
43
].This involved searching various databases such as Google Scholar,
Scopus, and Web of Science using specific keywords such as “ethics”, “consequentialism”,
“AI”, “ML”, and “cryptocurrency trading”.
The literature review was conducted using a comprehensive search strategy, includ-
ing keywords such as “AI”, “cryptocurrency”, “trading”, “ethics”, and “consequential-
ism”
[44–55]
. Articles were selected based on relevance to the research question, and
exclusion criteria included non-English language publications and articles that were not
peer-reviewed. Data extraction was carried out using a standardized form, which included
FinTech 2023,2434
information on the author, year of publication, research question, methodology, findings,
and limitations.
The data extracted from the literature review were analyzed using a qualitative con-
tent analysis approach, which involved the identification of themes and patterns in the
data. The themes that emerged from the analysis were used to develop a typology of
ethical frameworks, and then compared to various consequentialist theories [
33
,
36
,
56
–
63
].
The methods used in this research paper were designed to be rigorous and transparent,
allowing for replication and the building of future research on this topic. The systematic
literature review followed established guidelines for conducting a comprehensive literature
review [
64
–
66
]. The findings outline the need for responsible and ethical use of AI and ML
in trading to ensure market integrity and protect investors.
In addition to the literature review, a case study approach was used to investigate
the ethical implications of utilizing AI and ML in cryptocurrency trading. A case study
was selected based on its pertinence to the research question and data availability [
67
].
The selected case involved the analysis of charges against Sam Bankman-Fried, who was
accused of engaging in insider trading using AI and ML in trading. The case study was
analyzed using the ethical frameworks developed from the literature review centering on
consequentialist theory [44,52,56–63].
The case involving Sam Bankman-Fried, commonly known as SBF, and his cryptocur-
rency derivatives exchange, FTX, offers a nuanced look into the ethical implications of
cryptocurrency trading. Bankman-Fried was accused of utilizing diversionary tactics to
engage in insider trading and defraud FTX customers, exploiting confidential, non-public
information for financial gain [
24
,
68
–
82
]. The allegations were centered around the de-
ployment by FTX and access to trading information ahead of the general market, thereby
providing an unfair advantage to the firm [79–81].
These charges led to a series of legal and regulatory challenges. The Securities and
Exchange Commission (SEC) launched an investigation into the practices of FTX under
Bankman-Fried’s leadership [
78
–
82
]. The consequences of the charges against Bankman-
Fried and FTX were significant. They faced potential legal penalties, which included
fines and trading restrictions imposed by regulatory bodies [
75
]. The charges also led to
reputational damage, causing distrust among the platform’s investors and traders. The
case of SBF and FTX underscored the importance of a robust ethical framework to guide
cryptocurrency trading. It further demonstrated the necessity of regulations to monitor the
use of AI and ML and uphold market integrity, thus preventing any misuse of advanced
technologies that could provide an unfair advantage and destabilize the market.
Interestingly, Sam Bankman-Fried, the founder of the cryptocurrency trading platform
FTX argued for the philosophy of effective altruism. However, his actions showed the
opposite outcomes, as philosophy emphasizes using evidence and reasoning to determine
the most effective ways to benefit others [
70
,
71
,
83
]. Effective altruism implies that individ-
uals are morally obligated to use their resources to help others as effectively as possible.
The intersection of effective altruism and AI and ML ethics is especially pertinent, given
the potential for AI and ML to disrupt traditional economic structures and create novel
challenges and opportunities. The benefits of using AI and ML in trading, such as improved
market efficiency and the potential for greater returns, could be used to generate wealth
that, in the hands of effective altruists, be directed toward highly impactful causes [70,71].
Limitations to this portion of research include the potential for relevant articles to have
been missed in the literature review due to the chosen search parameters and databases.
Future research could address these limitations by expanding the sample size of the litera-
ture review, conducting expert interviews, and analyzing multiple case studies to provide
a comprehensive understanding of the ethical considerations related to the use of AI and
ML in cryptocurrency trading.
Moreover, future studies could integrate quantitative methods, such as surveys or
experiments, to further explore the ethical implications of AI and ML in cryptocurrency
trading. Incorporating consequentialist theory into this study is a foundation for future
FinTech 2023,2435
research exploring ethics in AI and ML and cryptocurrency trading. In conclusion, the
methods utilized in this study, which combine a literature review and a case study approach,
provide a strong basis for developing an ethical framework for applying AI and ML in
cryptocurrency trading. The study’s findings are significant for policymakers, regulators,
and practitioners within the financial industry and underscore the importance of the ethical
use of AI and ML in trading to maintain market integrity and safeguard investors [41,56].
3. Results
The research incorporated a two-cycle coding strategy using NVivo for analysis of
the data from the literature review. The analytical process developed a typology of ethical
frameworks applicable to AI and ML’s role in cryptocurrency trading, with consequen-
tialism as the key focus, as outlined in Table 1. This typology encapsulates key ethical
frameworks such as utilitarianism, egoism, hedonistic egoism, ethical altruism, rule conse-
quentialism, and act consequentialism. Each framework is described by its fundamental
principles, its application in cryptocurrency trading, and concrete examples illustrating AI
and ML’s potential application within each framework.
Table 1. A typology of ethical frameworks with a focus on consequentialism [56–63,84–104].
Ethical
Framework Key Principles Application in Cryptocurrency
Trading Example in Cryptocurrency Trading
Utilitarianism
Maximize overall happiness
and minimize
overall suffering.
Assess whether AI in cryptocurrency
trading would create more happiness
or suffering.
A cryptocurrency exchange uses AI to
prevent market manipulation, resulting
in more trust in the market and increased
happiness among traders.
Egoism Maximize self-interest.
Assess whether AI in cryptocurrency
trading would benefit the self-interest
of the individual or group.
A cryptocurrency exchange uses AI to
make more accurate trades, resulting in
higher profits for the exchange and
its investors.
Hedonistic
Egoism
Maximize pleasure and
minimize pain for oneself.
Assess whether AI in cryptocurrency
trading would create more pleasure or
pain for oneself.
Cryptocurrency trader uses AI to make
more profitable trades, resulting in more
pleasure for themselves.
Ethical
Altruism
Maximize the overall
well-being of others.
Assess whether AI in cryptocurrency
trading would create more overall
well-being for others.
A cryptocurrency exchange uses AI to
prevent market manipulation, resulting
in increased trust and overall well-being
for traders.
Rule Conse-
quentialism
Follow rules that maximize
overall happiness and
minimize overall suffering.
Assess whether AI in cryptocurrency
trading would follow rules that lead to
more overall happiness and less
overall suffering.
A cryptocurrency exchange uses AI to
follow strict regulations, resulting in
increased trust in the market and overall
happiness for traders.
Act Conse-
quentialism
Make decisions that
maximize overall happiness
and minimize overall
suffering in each situation.
Assess whether AI in cryptocurrency
trading would make decisions that lead
to more overall happiness and less
overall suffering in each situation.
A cryptocurrency trader uses AI to make
decisions about trades, resulting in more
overall happiness and less overall
suffering for traders.
Table 1summarizes the typology of ethical frameworks, including their key principles,
applications in cryptocurrency trading, and corresponding examples. The references cited
in the table underpin the development of the typology and its underlying concepts. The
research outcomes present a comprehensive framework for analyzing ethical considerations
linked to AI’s use in cryptocurrency trading. The typology of ethical frameworks guides
decision-making and policy formulation in the financial sector, promoting responsible and
ethical practices.
Utilitarianism, as an exemplar of consequentialist frameworks, seeks to maximize hap-
piness and mitigate overall suffering [
27
,
28
,
33
]. In cryptocurrency trading, utilitarianism’s
application examines whether AI and ML use would produce more happiness or suffering.
For instance, an AI/ML-enabled cryptocurrency exchange preventing market manipulation
could augment market trust, increasing happiness among traders and stakeholders [60].
FinTech 2023,2436
Egoism, another consequentialist framework, prioritizes self-interest maximization [
86
].
(In cryptocurrency trading, an egoistic application evaluates whether AI would promote
the self-interest of individuals or groups. An illustration of this might be an AI and ML
executing more accurate trades, leading to amplified profits for the exchange and investors,
thus aligning with egoism’s principles.
Hedonistic egoism, a branch of egoism, promotes maximizing one’s pleasure and mini-
mizing pain [
34
]. In cryptocurrency trading, the application of hedonistic egoism considers
whether AI’s implementation would yield more pleasure or pain for individuals. If AI
enables traders to execute more lucrative trades, it could enhance personal pleasure but
adversely impact society.
Ethical altruism, another consequentialist perspective, stresses maximizing the overall
well-being of others [
41
]. In cryptocurrency trading, the application of ethical altruism
evaluates whether AI’s use would improve the well-being of others. An example would be
an AI and ML preventing market manipulation, which could enhance trust and contribute
to stakeholders’ overall well-being. However, this approach also raises significant ethical
considerations. Using AI and ML in trading can exacerbate information asymmetry and
distort market efficiency [
25
,
26
]. Even if the generated wealth is used for altruistic purposes,
the means by which it is generated may be ethically dubious. Reliance on the goodwill of
individuals for the redistribution of wealth may not be a sustainable or reliable model for
addressing systemic issues.
Rule consequentialism advocates adherence to rules that enhance happiness and alleviate
overall suffering [
104
]. In cryptocurrency trading, rule consequentialism evaluates whether
AI aligns with rules that foster more happiness and less suffering. A cryptocurrency
exchange employing AI to adhere to regulations strictly could amplify market trust.
Lastly, act consequentialism, another consequentialist framework, involves decision-
making that maximizes overall happiness and minimizes overall suffering in each specific
situation [
84
]. In cryptocurrency trading, act consequentialism’s application involves
assessing whether AI and ML’s use would result in more happiness and less suffering in
each scenario. An example might be a cryptocurrency trader utilizing AI to make informed
trading decisions, thus contributing to overall trading satisfaction.
Table 2summarizes four ethical outcomes of artificial intelligence (AI) and machine
learning (ML) in finance, each with its potential benefits, detriments, and alignment with
consequentialist theories. Regarding market efficiency, AI and ML can enhance market
dynamics through quick data processing and accurate predictions, but there’s a risk of
market manipulation and financial instability if misused. If these technologies result
in a fairer, more efficient market, they align with consequentialist theories. AI and ML
could improve risk identification and generate real-time alerts for risk management, but
they could also cause complacency and lack of human oversight [
105
,
106
]. If effectively
managing risks, they align with consequentialist theories.
Table 2. Analysis of different ethical outcomes and alignments with consequentialist theories.
Ethical
Outcomes Potential Benefits Potential Detriments Alignment with Consequentialist
Theories
Market
Efficiency
AI and ML can enhance market
efficiency by quickly processing
large amounts of data and
making accurate predictions.
AI and ML could contribute to
market manipulation and
financial instability if misused.
If the use of AI and ML leads to a more
efficient and fair market, it will align with
consequentialist theories, prioritizing the
greatest good for the greatest number.
Risk Man-
agement
AI and ML can improve risk
management by identifying
potential risks and generating
real-time alerts.
Over-reliance on AI and ML
could lead to complacency and a
lack of human oversight,
potentially exacerbating risks.
If the use of AI and ML effectively
manages risks and prevents harm, it
would align with
consequentialist theories.
Access to
Financial
Markets
AI and ML can democratize
access to financial markets by
providing sophisticated trading
tools to the general public.
If not properly regulated, AI and
ML could be used to exploit less
knowledgeable investors, leading
to unfair outcomes.
If the use of AI and ML broadens access
to financial markets and promotes
financial inclusion, it would align with
consequentialist theories.
FinTech 2023,2437
Table 2. Cont.
Ethical
Outcomes Potential Benefits Potential Detriments Alignment with Consequentialist
Theories
Regulatory
Compli-
ance
AI and ML can help firms comply
with regulatory requirements by
automating compliance tasks.
If used unethically, AI and ML
could be used to evade regulatory
scrutiny and engage in
illegal activities.
If the use of AI and ML promotes
regulatory compliance and protects
investors, it would align with
consequentialist theories.
Regarding access to financial markets, AI and ML could democratize access by pro-
viding advanced trading tools to the public. Still, there’s a risk of exploitation of less
knowledgeable investors if not adequately regulated. If AI and ML broaden market access,
they align with consequentialist theories. Finally, AI and ML could automate compliance
tasks in regulatory compliance but also be used to evade regulatory scrutiny if used un-
ethically. If promoting regulatory compliance and protect investors, they again align with
consequentialist theories.
In the FTX case, the potential detriments of using AI and ML in cryptocurrency trading
have materialized. The company’s rapid rise and subsequent fall highlight the risks that
exist without proper oversight and regulatory compliance. The outcome does not align
with ethical considerations from a consequentialist perspective, resulting in harm and
unfair outcomes for many stakeholders. However, it is important to note that the ethical
implications of AI and ML are not inherent to these technologies but rather depend on how
they are used. With proper management and regulation, AI and ML have the potential to
bring significant benefits to the cryptocurrency market and its participants.
4. Discussion
The research provides crucial insights into the application of consequentialism in the
context of AI and ML in cryptocurrency trading, with a particular focus on the FTX case.
Consequentialism, as an ethical framework, emphasizes the outcomes or consequences
of actions. This perspective is particularly relevant in cryptocurrency trading, where AI
and ML technologies can significantly influence market efficiency, transparency, and the
well-being of traders. The FTX case is a prime example of the potential consequences
of AI and ML applications in cryptocurrency trading. FTX, a cryptocurrency exchange,
experienced a rapid rise and subsequent fall, highlighting the risks of over-reliance on
these technologies without proper human oversight and regulatory compliance. This case
underscores the importance of considering the ethical implications of AI and ML from a
consequentialist perspective.
Utilitarianism
, a consequentialist ethical framework, emphasizes maximizing happi-
ness and minimizing overall suffering. In the context of the FTX case, if AI and ML could
have prevented market manipulation, increased trust, and led to greater happiness among
stakeholders, this would align with the principles of utilitarianism.
Egoism
focuses on maximizing self-interest. In the context of the FTX case, if AI and
ML technologies could have enabled more accurate trades and higher profits for traders or
exchanges, this would align with the principles of egoism.
Hedonistic
egoism seeks to maximize pleasure and minimize pain for oneself. If using
AI and ML in cryptocurrency trading leads to more profitable trades and greater pleasure
for traders, this aligns with the principles of hedonistic egoism.
Ethical altruism
focuses on maximizing the overall well-being of others. If AI and ML
could have been used in the FTX case to prevent market manipulation and ensure fair and
transparent trading practices, this would align with the principles of ethical altruism.
Rule consequentialism
emphasizes following rules that maximize overall happiness
and minimize overall suffering. In the context of the FTX case, if AI and ML technologies
were designed to follow regulations and ensure market integrity strictly, this would align
with the principles of rule consequentialism.
FinTech 2023,2438
Act consequentialism
involves making decisions that maximize happiness and mini-
mize overall suffering in each situation. If AI and ML could have assisted traders in the FTX
case in making informed decisions that led to more happiness and less overall suffering,
this would align with the principles of act consequentialism.
The research offers a comprehensive typology of how consequentialist perspectives can
be applied to AI and ML in cryptocurrency trading. However, it is essential to acknowledge
the limitations of this research. The typology of ethical frameworks presented here is not
exhaustive, and other ethical theories and perspectives are relevant in the context of AI in
cryptocurrency trading. Additionally, applying ethical frameworks is subjective and varies
based on individual values and interpretations.
Further research would expand on this study by exploring the implications of other
ethical frameworks, such as deontology or virtue ethics, in AI and cryptocurrency trad-
ing. Additionally, quantitative research methods, such as surveys or experiments, can be
employed to gather empirical data and validate the findings of this study. Fama’s market
inefficiency theory about the FTX case and the use of AI and ML in cryptocurrency trading
could also be explored.
The FTX case also provides an opportunity to examine the potential consequences of
AI and ML applications in cryptocurrency trading from the perspective of Fama’s market
efficiency theory, with three forms of market efficiency: weak, semi-strong, and strong [
23
].
In the context of the FTX case, if AI and ML technologies could have enhanced the weak
form of market efficiency by quickly processing historical price and volume information, it
would align with the principles of consequentialism [23].
However, the FTX case also highlights the potential risks associated with semi-strong
and potent forms of market efficiency. If AI and ML technologies were used to process
publicly available information (semi-strong form) or even private information (strong
form) to gain an unfair advantage in the market, it would not align with the principles
of consequentialism. Such practices could lead to market manipulation and financial
instability, as seen in the FTX case [78–80].
Moreover, the FTX case underscores the importance of regulatory oversight in using AI
and ML in cryptocurrency trading. Regulatory bodies, such as the Securities and Exchange
Commission (SEC), ensure market integrity and protect investors. In the FTX case, the
SEC charged Samuel Bankman-Fried with defrauding investors, highlighting the potential
ethical issues associated with using AI and ML in cryptocurrency trading [
78
–
80
]. AI and
ML can be employed to commit and identify fraud and insider trading, and the dual-edged
nature of AI and ML use can be both a risk and a remedy in cryptocurrency trading.
Emphasizing the importance of ethical considerations in shaping AI and ML applications
using correct ethical regulation would allow for appropriate utilization and, rather than
restriction of AI and ML use, promotion of its responsible and beneficial use.
From a consequentialist perspective, the ethical implications of using AI and ML in
cryptocurrency trading should be evaluated if these technologies could be ethically justified
to lead to more beneficial outcomes. However, they could be seen as ethically problematic,
resulting in harm or unfair outcomes. The FTX case is a stark reminder of the potential
consequences of unregulated markets in cryptocurrency trading [70,71].
In conclusion, this research provides valuable insights into the application of conse-
quentialism in the context of AI and ML in cryptocurrency trading, with a particular focus
on the FTX case. It underscores the importance of considering the ethical implications of
these technologies and the need for proper management and regulation. Further research
is needed to explore other ethical frameworks and gather empirical data to validate the
findings of this study [24].
5. Conclusions
This study contributes to developing an ethical framework for applying AI and ML in
cryptocurrency trading, with a particular emphasis on consequentialism. The typology of
ethical frameworks presented herein offers a comprehensive review of how consequential-
FinTech 2023,2439
ist perspectives can inform decision-making processes within the financial industry. The
findings underscore the importance of considering AI and ML technologies’ potential con-
sequences and impacts in cryptocurrency trading. By leveraging these ethical frameworks,
stakeholders such as policymakers, regulators, and practitioners can navigate complex ethi-
cal dilemmas and make informed decisions that uphold market integrity, protect investors,
and promote well-being.
It is critical to recognize that ethical considerations in cryptocurrency trading extend
beyond consequentialism, necessitating exploring other ethical approaches and perspec-
tives. Future research should delve into alternative ethical frameworks, utilize quantitative
research methodologies, and address the legal and regulatory aspects of AI in cryptocur-
rency trading. The responsible and ethical deployment of AI and ML technologies in
cryptocurrency trading is paramount for maintaining trust, fairness, and transparency
within financial markets. By integrating ethical considerations into the development and
implementation of AI systems, the financial industry can maximize the potential of these
technologies while mitigating the associated risks and safeguarding the interests of mar-
ket participants.
In essence, this research lays the groundwork for further exploration and discourse on
the ethical implications of AI and ML in cryptocurrency trading. By continually examining
and refining ethical frameworks, researchers can contribute to developing guidelines and
best practices that promote ethical decision-making and responsible innovation within the
financial industry. New regulatory approaches are necessary for trading and AI and ML
in cryptocurrency. Digital assets present unique challenges, such as their decentralization,
anonymity, and the speed and volume of transactions, which necessitate novel regulatory
and ethical perspectives. While existing laws address aspects like fraud and insider trading,
the unique dynamics of cryptocurrencies and the implementation of AI technology in
trading call for more specific and context-sensitive regulatory approaches.
The FTX case underscores the necessity for robust regulatory frameworks to ensure
that the benefits of AI and ML in trading are not disproportionately concentrated and
are instead utilized for the broader social good. Effective altruism presents a compelling
model for the role of individual actors in mitigating the potential negative impacts of AI
and ML in trading. It highlights the importance of regulatory and ethical frameworks in
ensuring these technologies are employed to benefit society as a whole. While AI- and
ML-driven trading have the potential to optimize trading strategies and enhance returns,
they introduce new variables and risks into the financial market ecosystem. This challenges
traditional understandings of market efficiency and necessitates the incorporation of ethical
considerations into applying AI and ML in financial trading. The advent of AI and ML
underscores the need for a revised understanding of market efficiency, considering the
evolving technological landscape of trading.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The author declares no conflict of interest.
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