# Why Talking about ethics is not enough: a proposal for Fintech’s AI ethics

Cristina Godoy Bernardo de Oliveira
Faculdade de Direito de Ribeirão Preto
University of São Paulo
Ribeirão Preto, SP, BRAZIL
<EMAIL_ADDRESS>
Evandro Eduardo Seron Ruiz
Department of Computing and Mathematics
FFCLRP – University of São Paulo
Ribeirão Preto, SP, BRAZIL
<EMAIL_ADDRESS>
On a sabbatical leave sponsored by the Institute of Advanced Studies of the
University of São Paulo.

###### Abstract

As the potential applications of Artificial Intelligence (AI) in the financial
sector increases, ethical issues become gradually latent. The distrust of
individuals, social groups and governments about the risks arising from
Fintech’s activities is growing. Due to this scenario, the preparation of
recommendations and Ethics Guidelines is increasing and the risks of being
chosen the principles and ethical values most appropriate to companies is
high. Thus, this exploratory research aims to analyze the benefits of the
application of the stakeholder theory and the idea of Social License to build
an environment of trust and for the realization of ethical principles by
Fintech. The formation of a Fintech association for the creation of a Social
License will allow early-stage Fintech to participate from the beginning of
its activities in the elaboration of a dynamic ethical code and with the
participation of stakeholders.

_K_ eywords Artificial Intelligence and Ethics $\cdot$ AI and Society $\cdot$
Social License and Fintech

##  1 Introduction

As the potential applications of Artificial Intelligence (AI) in the financial
sector increases, ethical issues become gradually latent. The distrust of
individuals, social groups, and governments about the risks arising from
Fintech’s activities is growing. Due to this scenario, the preparation of
recommendations and Ethics Guidelines is increasing, and the risks of being
chosen the principles and ethical values most appropriate to companies is
high. Thus, this exploratory research aims to analyze the benefits of applying
the stakeholder theory and the idea of Social License to build an environment
of trust and for the realization of ethical principles by Fintech. The
formation of a Fintech association for the creation of a Social License will
allow early-stage Fintech to participate from the beginning of its activities
in elaborating a dynamic ethical code and with the participation of
stakeholders.

The financial sector is one of the fields that most seeks technological
solutions and applications of Artificial Intelligence in its services. In this
scenario, Fintech (Financial technology) quickly presents solutions in more
than 19 business areas of the financial sector. Although many Fintech swells,
few could climb or remain in this competitive market, mainly due to legal
regulations that are costly to early-stage Fintech. In addition, Fintech are
new companies that do not yet have a strong brand, so consumer distrust is
high. Thus, it is necessary to think about solutions so that Fintech could
survive and create answers that help the financial sector and society.

Hence, our article suggests that the creation of an association formed by
Fintech from several countries allows stakeholders’ participation for the
elaboration, monitoring, and correction of ethical principles followed by
Fintech is a way to create an environment of trust between companies and
consumers. Also, a Social License is a way to increase Fintech’s credibility
and strengthen these companies’ dialogue with the government to solve legal
problems more quickly.

Finally, the word ethics must have meaning and be applied within the data-
driven industry. Artificial Intelligence (AI) presents several solutions and
facilities to human problems, and it is necessary to increase the dialogue
between society and technology companies to create a trustworthy environment.

##  2 Artificial Intelligence

Creating and applying non-biological intelligent systems has been a long
pursued task as summoned by Lee Spector [1]. Spector also recalls that
artificial intelligence (AI) was not really founded at the famous Dartmouth
College conference [2]. The rumors of artificial beings endowed with
intelligence since the 19th century when William Paley argued “that
intelligent designers are necessary for producing complex adaptive systems”
[3]. Spector also remembers that Charles Darwin refuted this idea around 1859,
showing that complex and adaptive systems, nowadays called intelligent
systems, can arise naturally from a process of selection.

Nowadays, abstract models of cognition under artificial agents and the
software instantiations of such models are what has been called ‘Cognitive
Architectures’ [4]. These cognitive architectures aim to produce artificial
systems that exhibit intelligent behavior analog to human in every detail,
including the ability to take unsupervised decisions ensuring the safety of
their behavior, and more than that, they should also be explicitly ethical
[5].

The current evolution of AI systems has brought in an extensive discourse on
AI ethics. Hagendorff [6] brings an updated analysis and comparison of 22
guidelines, namely normative principles and recommendations aimed to deprive
AI systems of any disturbing new AI technologies. Earlier, Jobim and co-
authors [7] analyzed whether a global agreement about the major ethical
questions was emerging based on some AI guidelines. Their study revealed a
global convergence around five ethical principles, which are: 1) Transparency;
2) Justice and fairness; 3) Non-maleficence; 4) Responsibility, and; 5)
Privacy. These principles will be discussed directly or indirectly in this
paper, but before that, we shall recall the main tasks we can find AI methods
being applied. Together, principles and applications might bring a clearer
picture of the eventual distress employed by AI techniques.

What we humans may think about AI, the intelligence exhibited by machines, is
not the same as the natural intelligence demonstrated by animals and humans as
it involves emotions and consciousness. The AI of today is manifested by what
we call ‘Machine Learning’ (ML) methods. Machine Learning is a technology
where computers can learn from examples of data associations. An ML task
results from an evaluation, sometimes a prediction or inference, of many
sampled data. Machine learning tasks rely on learning from patterns in data
and not by explicitly programming a computer. This initial amount of data
presented to the ML algorithms is usually called ‘training data.’ It is used
to build a working model. This model drives the prediction or the inference
engine. Building a model from training data is what we call ‘supervised
learning’, and although primarily used by ML approaches, it is not the only
one.

Applications for ML methods vary as much as human creativity might take it,
but the majority of the ML methods can be resumed as the following tasks:

  * •

Binary classification, which is the task of classifying the elements of a set
into two distinct groups. Quality control is a well known binary
classification test;

  * •

Multiclass classification is the task of classifying the elements into three
or more classes. Language identification systems are a suitable example of
multiclass classification

  * •

Regression is a statistical technique for estimating the relationships among
variables. Predictions from historic data most often are based on regression
algorithms;

  * •

Clustering is a technique used to group multi-dimensional data into closely
related groups. The task of finding similar users on social networks based on
the contents of their posts is an example of clustering;

  * •

Ranking may also be a classification task as it involves scaling and rating
systems. It is a task generally applied to information retrieval, e.g.,
ranking the web links retrieved by a search algorithm looking at web pages. A
recommendation may also be thought of as a ranking system, and finally;

  * •

Forecasting, as a process for making predictions of future data based on
previous or present samples.

This previous compilation of ML tasks is here to invite the reader to foresee
that some ethical issues in artificial intelligence might be the work of one
or a group of these algorithms. It might be now clear that the expected
predictability of these methods as they rely on their ‘knowledge’ and trained
data is positively related to the loss of predictable physical work jobs such
as soldering and packing in an assembly line. Racism and AI bias can be
understood as an improper balance of the data samples. Bias is a part of life.
Bias origin accompanies a diversity of data that each group is represented by.
This same data is the hallmark that naturally divides them by the algorithms.

Finally, the existing discrimination in society could not be eliminated
through corrections of algorithms. The data used to reflect an image of a
particular society’s past, and the change depends on state intervention and
public policies. On the other hand, the construction of a trustworthy AI
depends on companies’ initiative to avoid the perpetuation of discrimination.
In this way, our research will focus on the case of financial technology
(FinTech) that use AI for prusesling and granting credit, directly impacting
people’s lives. Furthermore, we will propose a model of association between
Fintech and the issuing of a certification so that companies’ performance for
the implementation of ethical principles is carried out in a more efficient
way.

###  2.1 AI and Fintech

AIDS is an useful concept for this article brought by Longbing Cao [8] that
means:

> AIDS broadly refers to both classic AI areas including logic, planning,
> knowledge representation, modeling, autonomous systems, multiagent systems,
> expert system (ES), decision support system (DSS), simulation, pattern
> recognition, image processing, and natural language processing (NLP) and
> modern AI and DS areas such as representation learning, machine learning,
> optimization, statistical modeling, mathematical modeling, data analytics,
> knowledge discovery, complexity science, computational intelligence, event
> and behavior analysis, social media/network analysis, and more recently deep
> learning and cognitive computing. In contrast, we broadly refer finance to
> areas including capital market, trading, banking, insurance, leading/loan,
> investment, wealth management, risk management, marketing, compliance and
> regulation, payment, contract, auditing, accounting, financial
> infrastructure, blockchain, financial operations, financial services,
> financial security, and financial ethics.

In order to comprehend the expression ‘Smart Fintech’, Longbing Cao [8] stated
that Fintech is a keystone of “synthesizing, innovating and transforming
financial services, economy, technology, media, communication, and society
broadly driven by AIDS techniques”. Besides, AIDS techniques represent the
combination of the classic AI areas, such as logic, planning, autonomous
systems, expert system (ES), support system (DSS,) natural language processing
(NLP), among others, and also, modern AI and DS areas (representing learning,
machine learning, optimization, data analytics, etc.). Therefore, EcoFin
(economics and finance) and the new generation of FinTech are based on AIDS
techniques, and the result of this is called smart FinTech.

Longbing Cao also identified 19 different Fintech business areas, and our
research focuses on economic-financial services, such as banking, insurance,
lending, financing, and crowdfunding services. Within this business area,
there are several challenges to be faced by smart FinTech. Our article will
seek solutions concerning “human and social complexities: such as AIDS
techniques for modeling and managing the diversity and inconsistency of
participant’s cognitive, emotional and technical capabilities and performance,
and for enabling effective communications, cooperation and collaboration
within a department and between stakeholders(…)” [8].

FinTech represents a relevant strategy for banks and start-ups to adapt to the
changes that technology is generating in society [9] (p. 1411). Thus, it is
not restricted to e-banking and consumer digitalization, but the strengthening
of relations between users and banking services. Thus, AI represents an
opportunity for modifying the financial industry and increasing revenue [10].

For Fintech, data has the following functions: providing data to its customers
and providing solutions to the Financial Service Institutions (FSIs) to
improve their services and to create new products and services [11].
Consequently, rapid data analysis and proper information management are
crucial for developing new services for FSIs. Therefore, data analysis and
artificial intelligence are fundamental in this scenario.

The financial sector has always sought innovations to improve its services and
create new opportunities, but, currently, there is a focus on the use of new
technologies to provide solutions and the creation of new financial services.
Thus, Fintech uses big data analytics, artificial intelligence (robot
advisors, credit score, credit risk, etc.), and blockchain technology to meet
the high demand for new products, solutions, and services [12].

Fintech comes up with new services and solutions for FSIs in an environment of
excessive legal regulation, causing the emergence of ‘Regtech’ (Regulatory
Technology) firms. These Regtech provide legal solutions that can help banks
and other intermediaries so that they do not violate the legislation and could
manage risks more effectively [13]. Also, Fintech faces two other challenges:
security problems and lack of confidence in the data used. Thus, building a
relationship of trust between users and Fintech is essential for activities to
be expanded and new services to be offered [14] (p. 61).

Fintech are suitable for our analysis proposed in this article because they
have some difficulties that can be overcome by employing a Social License,
such as: they have difficulty scaling the business due to lack of trust from
its users, lack of brand awareness, “an established distribution
infrastructure, capital, and regulatory compliance expertise that,
historically, are the strengths of incumbent firms” [15] (p. 10).

Besides, Fintech are based on the experience of consumers of their services,
so they create an environment where there is trust through “principles of
personalization, quick response (speed), relevance, transparency, and seamless
delivery” [15] (p. 14). Thus, conducting tests simulating in real-time and in
a realistic manner its operations is essential for the development of its
platform, and it is only possible through collaborations with other companies
in a favorable regulatory environment [14]. As a result, testing with live
simulations increases consumer confidence, and automated discriminatory
decisions could be corrected, for example.

On the other hand, it is difficult to identify the limits of this
collaborative relationship with other companies, as the early-stage Fintech is
also a competitor [16] (p. 07). As 95% of Fintech fail to scale the business
due to the absence of strategic collaboration [14], it is noted that this is
the favorable scenario for the development of a Fintech association aimed at
conducting ML tests, consumer awareness regarding the use of your data by
Fintech, participation of all stakeholders in the development of ethical
principles and values, etc. Thus, collaboration is necessary for Fintech’s
survival, and our proposal could contribute to building scalable business.

In the end, tests related to Fintech are changing because regulatory sandbox
initiatives have emerged in several countries, allowing the development of a
safe environment so that early-stage Fintech could carry out their tests
without the need to obtain a full license that could impede due to legal costs
required to obtain it [17]. Consequently, regulatory sandboxes help develop a
system of “startup-friendly, cross-sectoral and global ecosystem” [14]. Thus,
regulatory sandbox and data analytics will provide a trustworthy legal and
economic environment through data processing transparency.

###  2.2 Apple Card Case and Gender Discrimination

In the United States, there is a US Equal Credit Opportunity Act (ECOA) that
prohibits collecting and using gender data for credit rating assessment. As a
result, Fintech does not use gender data for ML training in the US, which
causes discriminatory decisions like Kelley and Ovchinnikov [18] seek to
demonstrate.

Furthermore, David Heinemeier Hansson, a software developer, demonstrated that
Apple Card [19] was a ‘sexist program’ because his credit line was 20 times
higher than his wife. After this case, the debate about a discriminatory
algorithm increased, and research was developed to face gender discrimination
in ML training.

Consequently, Kelley and Ovchinnikov [18] studied the reduction of
discrimination based on gender for the granting of credit by Fintech. Thus,
three types of anti-discrimination legislation were analyzed:

Level 1

Legislation that authorizes data collection and the use of gender for training
machine learning (ML) (Example: Singapore);

Level 2

Legislation that authorizes data collection regarding gender, but does not
authorize use for ML training (Example: European Union (EU));

Level 3

Legislation that does not authorize the collection and the use of gender data
for ML training (Example: United States (US)).

Comparing statistical and other ML models, Kelley and Ovchinnikov [18] (p. 06)
stated that the withdrawal of gender data interferes in various stages of the
ML modeling process, which are not captured by the omitted variable bias (OVB)
such as feature engineering (the use of gender data creates new features),
algorithm selection (outperformance of a random forest model, e.g.), feature
selection (e.g., the use of SHAP values and SHAP interaction values
demonstrated that “gender-reliant features are on average 19 times more
important for women compared to men” [18] (p. 07), and also hyperparameter
selection. They concluded that the gender data and engineered features impact
the set of hyperparameters, which impacts the model predictions.

Consequently, Kelley and Ovchinnikov [18] carried out some studies to analyze
how it is possible to reduce discrimination by gender according to existing
legislation and presented four approaches:

  1. 1.

Down-sampling the training data to rebalance gender: is possible in the Level
1 and 2 of anti-discriminatory legislations and “results in -9.43%
discrimination, - 174 bps predictive quality, -0.06% avg. profitability” [18]
(p. 08);

  2. 2.

Gender-aware hyperparameter turning: is possible in the Level 1 and 2 of legal
regulation and the results are “-24.56%, -280 bps predictive quality, -0.11%
average profitability” [18] (p. 07);

  3. 3.

Up-sampling the training data to rebalance gender: is feasible in the Level 1
and 2 of anti-discriminatory laws and results are “-2.55% discrimination, no
significant change to predictive quality and - 0.04% average profitability”
[18] (p. 08);

  4. 4.

Probabilistic gender proxy modeling: is possible to be applied it in Level 3
but the US legislation prohibits. The results are “-62.08% discrimination,
+0.01% average profitability and no significant change to predictive quality”
[18] (p. 08).

The research by Kelley and Ovchinnikov [18] demonstrates that legislation can
impact discrimination for the granting of credit to Fintech since excluding
gender data does not correct discrimination by gender but intensifies it. In
this way, Aristotle’s statement [20] (p. 160) is extremely current to face the
challenges arising from the use of artificial intelligence in the financial
field:

> Thus injustice, as we say, is both an excess and a deficiency, in that it
> chooses both an excess and a deficiency–in one’s own affairs choosing excess
> of what is, as a general rule, advantageous, and deficiency of what is
> disadvantageous; in the affairs of others making a similarly
> disproportionate asymmetry, though in which way the proportion is violated
> will depend upon circumstances…

Hence, we observed that legislation could impact ML applications’ results, and
it is necessary to update it when the occurrence of discrimination is detected
due to legal restrictions. Suppose Fintech organizes itself in an association
to apply the ethical principles and values of society. In that case, it is
possible to maintain constant contact with legislators to avoid what occurred
in the Apple Card case, for example. Thus, we will present below the Social
License for Fintech to help realize ethical decisions by Fintech.

##  3 Discussion

Are only the AI systems that may have ethical issues? Have we assumed that
previously to AI, all computer systems and algorithms were fair and ethical?
Let us take a simple but very effective ML approach called Naïve Bayes, here
called NB. NB is a family of ‘probabilistic classifiers’ based on the Bayes’
theorem. The 18th-century British mathematician Thomas Bayes created a formula
for determining a conditional probability used to measure the likelihood of an
outcome occurring based on previous events. What is ‘intelligence’ there for
just calculating probabilities? How difficult is it to count events? How could
an 18th-century formula be avoided from use during the last 30 or 40 years?
Person profiling might be an application of the Bayes theorem. For instance,
knowing that someone is a quiet person and prefers to spend his/her free time
reading the newspaper, one might guess whether he/she is a librarian or a
salesperson. Computer codes are pervasive. They are everywhere. AI systems can
be programmed from various methods, from sophisticated ones to the more simple
if-them-else commands; however, unlikely this latter approach might be. Are
the consumers trained to recognize ethical flaws in these systems? Are the
programmers trained for challenges like this one?

###  3.1 Codes of digital ethics: risks and challenges

The term ‘ethics’ could lose its meaning if we do not connect it with action.
Ethics guides us to decide better in a context based on available information.
Thus, we need to analyze ethical values and principles thinking about how to
act to apply them. Therefore, we will talk about the risks of talking and
writing about ethics when we ignore practicing ethical values and principles.

Between 2017 and 2018, more than 70 recommendations on ethics of AI were
published [21]. Luciano Floridi [22] highlighted five unethical risks due to
the increase of recommendations related to ethics in artificial intelligence
applications:

  1. 1.

Ethics shopping: it is a practice used to justify itself concerning the
decisions, choices, and processes adopted by a company. In this way, the most
appropriate principles for your objectives are chosen as a ‘menu’ of
principles and values. In this way, instead of companies looking for
behavioral changes, alterations, and corrections of algorithms and changes in
data processing, they aim at the selection of principles that legitimize their
conduct;

  2. 2.

Ethics blue washing: it is about adopting cosmetic measures to appear the
existence of ethical concerns by technology companies. Thus, there is a focus
on using advertising and marketing to cover the company in the appearance of
being concerned with ethical issues. However, in the field of action, nothing
is done, as the implementation of effective measures to correct processes or
decisions can generate the expense of many resources;

  3. 3.

Ethics lobbing: refers to the exploration of self-regulation through codes of
ethics to delay or prevent the drafting of relevant legislation, to justify a
limitation of compliance, and to weaken enforcement. Companies do not see what
the benefits of acting ethically regarding their lobbying policy [23] and
prefer to “avoid good and necessary legislation (or its enforcement) about
design, development, and deployment of digital processes, products, services,
or other solutions” [22];

  4. 4.

Ethics dumping: refers to the export of unethical research activities to
countries where they do not have strong legislation that prohibits the conduct
of these practices and the importation of the results from this research to
the country of origin of the company that conducted it. The term ‘ethics
dumping’ was introduced by the Science with and for Society Unity of the
European Commission in 2013. Finally, there are two reasons for the export of
unethical practices: intentional exploitation from high-income to low or
middle-income countries and exploitation due to lack of knowledge and ethical
concerns [24] (p. 02); and

  5. 5.

Ethics shirking: it is about carrying out few activities aimed at the
application of ethical principles and values when the return is low, and the
cost of responsibility is transferred to third parties. The term shirking
comes from the financial market and means “the tendency to do less work when
the return is smaller” [25].

Due to these five risks arising from the excess of codes of ethics, Floridi
[22] states that there are some strategies to face these malpractices:

  * •

For ethics shopping concerns, a clear ethical guide could be an effective
initiative (e.g., Ethics Guidelines for Trustworthy AI – EU) [26], widely
disseminated and accepted by many companies so that malpractice is easily
detected;

  * •

For ethics blue washing and dumping, a good strategy could be strengthening
transparency and education because this initiative could help identify
companies that do not adopt ethical practices. Besides, a certification system
for digital products and services can help implement this strategy;

  * •

For ethics lobbying, a great strategy is to enact clear legislation and make
it effective. Furthermore, the exposition of companies that practice lobbying
to delay the approval of laws and the differentiation between these companies
from those that have efficient self-regulation is essential;

  * •

For ethics shirking, the strategy consists of finding the malpractice source
and reallocating the distribution of responsibilities.

Although strategies to address these malpractices are pointed out, there is a
lack of a model capable of facing digital companies’ ethical problems. Thus,
we will present an association model with its roots in social license and open
software initiatives to implement strategies aimed at combating unethical
conduct.

###  3.2 Social license for data-driven industry

Accounting provides a relevant mechanism for social control [27] (p. 107). The
consumers have the ‘right to know’ [28] and the structure based on awards and
sanctions are part of the accountability process. To monitor social
transformations and develop a dynamic code of ethics that reflects the
principles and values of society, it is necessary to call for the
participation of stakeholders. However, the accountability process is often
too expensive for early-stage Fintech, so it is necessary to analyze
alternatives to implementing the idea of a Social License for Fintech.

Based on the assumption that artificial intelligence must have the following
qualities: Ability, Benevolence, and Integrity, there are authors, like Aitken
and co-authors [29] (p. 03) who argue that Fintech should seek a Social
License (SL) to create engagement among your users and so that they can
participate in the data processing.

The idea of Social License comes from the 90s [30], and its application
occurred, mainly concerning environmental protection and responsibility for
preserving forests. Furthermore, there are some examples of Social License use
in the health industry [29] (p. 03) that created a trustworthy relation
between science and society.

Although the importance of the engagement between the community and Fintech,
there are some challenges [29] (p. 11)), such as avoiding power asymmetry, not
directing society’s response, transparency about the risks that exist about
the use of individuals’ personal data, etc. Thus, the authors present the
benefits of a Social License; however, they do not indicate how to apply it.

Consequently, the article by Aitken [29] is relevant, as it provides an
alternative for the population to interact with the Fintechs for the dynamic
development of principles and values to be followed to build a reliable
relationship. On the other hand, it doesn’t show how to implement this
proposal to elaborate a Social License in the field of artificial intelligence
employed by Fintech.

The idea of Social License is embedded in the concept of Corporate Social
Responsibility (CSR), which consists of “a voluntary approach that a business
enterprise takes to meet or exceed stakeholder expectations by integrating
social, ethical, and environmental concerns with the usual measures of
revenue, profit, and legal obligation.” [31]. As global companies are becoming
less and less interested in the voluntary adoption of CSR [32], there is a
constant political incentive for companies to recognize the need for ethical
globalization and the adoption of global responsibility for their activities,
such as: World Economic Forums (stated politicians that corporate leader must
focus on sustainability), the Global Reporting Initiative, Mary Robinson’s
Realizing Rights, The Ethical Globalization Initiative, etc.

Unlike the legal license, the social license is provided by stakeholders in a
continuous process of reformulation. The legal license is carried out by a
public authority at a given time, and it is often expensive to obtain it and
the legal analysis does not follow the market transformations [33] (p.500). In
addition, the sense of stakeholder can be obtained through the concept
developed by the founder of stakeholder theory, Freeman [34] classifies
stakeholder into two groups: the group that can affect the company’s
activities and the group that will be affected by the company’s activities.
Thus, for the application of stakeholder theory in the data-driven industry,
individuals and stakeholder groups should be divided into two categories:

  * •

Stakeholders that affect the company’s activities: traditional banks,
financial institutions, government, regulatory agencies, consumers, etc.;

  * •

Stakeholders who are affected by the company’s activities: family members of
consumers, individuals who are denied credit, holders of personal data used
for ML training, relatives of those who have denied credit, startups that
depend on credit, etc.

Stakeholders have a major impact on the company’s activities as they form
groups and organizations with connections to political actors [35]. Due to
these coalitions, it is possible to obtain political support for the
development of the company’s project [36]; however, it is possible to occur
government pressures that affect legal licenses [33], for example.
Consequently, the idea of the social license is misconstrued, as it is no
longer based on the participation of all stakeholder to build a relationship
of trust with companies.

An alternative to avoid the emergence of barriers to social license throughout
its life cycle is the application of the Stakeholder Impact Index (SII) [37]
designed for application in the construction industry, but which can be used
in the data-driven industry with some adaptations that will not be presented
in this article. According to Boutilier, the SII:

> “It helps identify and prioritize stakeholders on the basis of their likely
> impact on the progress of a project. It arithmetically combines subjective
> estimates of several factors including the legitimacy of the groups, their
> power, their level of vested interest and their probability of having an
> impact. One factor called “position” (i.e. position on the issue of the
> project proceeding or being halted) is very similar to the social license.
> Olander showed that the approach can be useful in guiding relations with
> stakeholders.”

Given the characteristics of the stakeholder theory and the social license, we
can observe that the five ethical risks pointed out by FLORIDI [22] could be
faced through the adoption of the idea of Social License in the case of
fintech, because it is possible to elaborate an Ethics Guideline with the
participation of stakeholders to avoid ethics shopping, being a great
advantage, since the ethical principles and values will be constantly updated.
In addition, ethics blue washing and dumping can be resolved through the
transparency of fintech in obtaining the social license and by the education
of stakeholder who will understand the risks and benefits arising from
fintech’s activities. In addition, the certification system can be created
with the participation of stakeholders who could contribute to the elaboration
of the evaluation criteria for obtaining certification. Consequently, there
will be greater recognition and trust in the certification system and Fintech
that obtain the certificate could strengthen their brand. Finally, ethics
shirking will be identified mainly through the idea of accountability and
transparency of Fintech’s activities to its stakeholders.

In a statement, we note that the Social License may be an alternative to
address the risks of ethical malpractices by Fintech. In addition, it allows
the construction of an environment of collaboration and trust between
companies and stakeholders. Consequently, everyone will benefit, as Fintech
will be able to scale by building a brand that is reliable and meets global
ethical requirements; and stakeholders will be able to actively participate in
the elaboration of ethical principles and values to be followed by companies
and could have a better control of the use of their personal data.

##  4 Methods and Results

This is an exploratory study aimed at the comprehension of consumers’ lack of
confidence regarding Fintech’s ethical concerns, especially regarding the use
of Artificial Intelligence. There are not many papers considering the
application of stakeholder theory and social license within Fintech. Thus, we
sought to identify the variables related to this theme and verify the extent
to which it is possible to use the idea of Social License to face the risks
arising from the high elaboration of recommendations and Ethics Guidelines.

The comparative method was used to analyze the Social License application in
the mining and construction industry and to understand the extent to which the
idea can be applied in Fintech. The deductive method was used to point out the
consequences resulting from the use of the theory of stakeholders and Social
License’s idea. Finally, a primary research approach to data-gathering was
used because there is no satisfactory existing data about this theme.

The research result was that the model of the association between Fintech is
the most appropriate for collaboration between companies and for building an
environment of trust between companies and stakeholders. Also, Social License
is interesting for constructing the idea of a brand of trust and the
participation of stakeholders in elaborating the Code of Ethics. Thus,
transparency and accountability are essential in this relationship between
companies and stakeholders to be possible the employment of a Social License
within Fintech.

In addition, Fintech’s participation in an association allows the costs
arising from the Social License employment to be distributed. Moreover, the
risk of ethics shirking is dissolved because the distribution of social
responsibility will be balanced within the association, and it will be
possible to trace malpractices.

Finally, the association model will allow a more effective dialogue between
Fintech and governments, and it could overcome problems such as legal licenses
for early-stage Fintech or the legislation change so that ML training avoids
the perpetuation of discrimination in society as we verified in the study of
Kelley and Ovchinnivov [18].

##  5 Conclusion

The approach suggests the construction of an association formed by Fintech for
stakeholder participation in elaborating, implementing, and correction of the
ethical principles applicable in all fintech activities. The Social License
seems to apply to the data-driven industry. It allows creating an environment
of trust and dialogue, much necessary for realizing the ethical principles
applicable to Artificial Intelligence.

The risks arising from the excess of Ethics Guidelines can be faced by using
the Social License and Fintech emerge as companies that need more significant
participation of stakeholders to face the problem of consumer distrust and
global-scale power. Moreover, mainly, due to the lack of economic resources of
the early-stage Fintech, it is important to create an association in which
responsibility is distributed so as not to hinder the start of the activities
of the new Fintech and to facilitate the adoption of ethical principles and
values from the beginning of its activities.

Finally, new qualitative and quantitative studies will test a model of the
association between Fintech, applying the stakeholder theory to the Social
License in this promising economic sector. Preliminary, we consider that the
Social License obtained under an association formed by Fintech is the best way
to avoid cosmetic solutions regarding ethical principles and values by the
data-driven industry.

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monomial lifting of $t$, with respect to ${\mathfrak{X}}_{\iota}.$

######  Proposition 8.2.16.

We have that $\widetilde{t}=\upharpoonleft\kern-3.50006ptt^{D}_{\iota}$.

###### Proof.

We want to apply Theorem 4.0.10. First, $S^{2}_{D}=X$ because of
[Fei21][Corollary 8.8, Corollary 7.9]. By [Fei21][Proposition 3.6], we have
that $\widetilde{I}_{uf}=I_{uf}$, $\widetilde{I}_{hf}=I_{hf}\sqcup D$ and
$B^{2}_{I\times I_{uf}}=B$. Hence, $\widetilde{t}$ is a $D$-pointed seed
extension of $t$. Moreover, condition 2 and 3 of Theorem 4.0.10 hold because
of [Fei21][Lemma 7.8, Corollary 7.11] and the discussion at the beginning of
[Fei21][Section 6.3]. Since
$\overline{\mathcal{A}}(\widetilde{t})=\mathbb{C}[{\mathfrak{X}}_{\iota}]$ by
Theorem 8.2.15, we have that
$\widetilde{t}=\upharpoonleft\kern-3.50006ptt^{D}_{\iota}$ by Theorem 4.0.10.
∎

It should be true that the seed $t$ equals $t_{\mathbf{i}}$, for a certain
$\mathbf{i}\in R(w_{0})$ adapted, in the sense of [GLS07][Section 1.3], to the
chosen orientation of the Dynkin diagram, but this is not clear to the author,
in general. Nevertheless, it is easy to verify this statement on small
examples.

Finally, if $G$ is not simply laced, the inclusion
$\overline{\mathcal{A}}(\widetilde{t})\subseteq\mathbb{C}[{\mathfrak{X}}]$
still holds and the first part of [Fei21][Conjecture 8.2] states that this
inclusion is actually an equality. Proceeding as in the proof of Proposition
8.2.16, if one could identify $\mathbf{i}\in R(w_{0})$ such that
$t=t_{\mathbf{i}}$ and prove that
$B^{2}=\upharpoonleft\kern-3.50006ptB_{\mathbf{i}}$, then Theorem 8.2.1 would
imply the first part of [Fei21][Conjecture 8.2]. For example, in the $G_{2}$
case, since everything is explicit by Example 8.2.14 and [Fei21][Appendix A],
we have that the conjecture holds for $G_{2}$.

######  Remark 8.2.17.

As a final remark, note that the cluster algebra $\overline{\mathcal{A}}(t)$
is categorified and the work of Fei produces a categorification for
$\upharpoonleft\kern-3.50006ptt_{\iota}$ which is compatible with the on on
$\overline{\mathcal{A}}(t)$. Moreover, [Fei21][Corollary 7.8] gives an
homological interpretation of the minimal lifting matrix $\nu_{\iota}$ of the
seed $t$, with respect to ${\mathfrak{X}}_{\iota}$, in terms of this
categorification. A more explicit understanding of the relation between the
minimal lifting matrix in term of this categorification seems to the author
the fundamental tool needed to prove [Fei21][Conjecture 8.2] via Theorem
8.2.1. Actually, comparing the minimal monomial lifting to the extension
construction of [Fei23][Section 2.3], could provide a possible lead to
accomplish this task.

###  8.3 The base affine space, or branching to a maximal Torus

We use the previous result to construct a cluster structure on
${\mathfrak{X}}:={\operatorname{Spec}}(\mathbb{C}[G]^{U^{-}})$. Note that
${\mathfrak{X}}={\mathfrak{X}}(G,T)$ and $T$ is a Levi subgroup of $G$, so the
results of this section, for $G$ simple, can can be deduced form the ones of
Section 8.1.1. One can also observe that, the results of Section 8.1.1 hold in
the case of $G_{I}=T$, even if $G$ is not necessarily simple, because the same
proofs work. See Remark 8.1.10. In this section, we reinterpret this cluster
structure on ${\mathfrak{X}}$, as a quotient of the one on
${\mathfrak{X}}(G\times G,G).$ We slightly modify the notation of Subsection
7.4.1.

Let $D_{l}=\\{\alpha_{l}\,:\,\alpha\in\Delta\\}$ and
$\widetilde{X}_{\alpha_{l}}=\Delta^{\varpi_{\alpha}}_{e,e}$. We have a
cartesian square

${G_{0}}$${G}$${T\times
U}$${{\mathfrak{X}}}$${\lrcorner}$$\scriptstyle{\widetilde{\phi}}$

where $\widetilde{\phi}:T\times U{\,\longrightarrow\,}{\mathfrak{X}}$ is the
$T$-equivariant open embedding induced by the product $T\times
U{\,\longrightarrow\,}G$ and the leftmost vertical map is defined by
$x\longmapsto([x]_{0},[x]_{+})$. Then
$({\mathfrak{X}},\widetilde{\phi},\widetilde{X})$ is homogeneously suitable
for $D_{l}$-lifting by Corollary 7.4.4.

Note also that we have a commutative diagram

${{\mathfrak{X}}\times T}$${{\mathfrak{X}}(G\times G,G)}$${T\times U\times
T}$${T\times T\times
U}$$\scriptstyle{\iota}$$\scriptstyle{(\widetilde{\phi}\text{,}id)}$$\scriptstyle{\phi}$
(68)

where the map $T\times U\times T{\,\longrightarrow\,}T\times T\times U$ is
defined by $(h_{l},u,h_{r})\longmapsto(h_{l},h_{r},u)$ and $\iota$ is the open
embedding induced by the inclusion $G\times T\subseteq G\times G$.

Let $\mathbf{i}\in R(w_{0})$. We denote by $t_{\mathbf{i}}$ (resp
$\widetilde{t}_{\mathbf{i}}$) the associated seed of $\mathbb{C}(U)$ when
$\mathbb{C}(U)$ is considered as a subfield of
$\mathbb{C}({\mathfrak{X}}(G\times G,G))$ (resp.
$\mathbb{C}({\mathfrak{X}}))$. We call $I_{\mathbf{i}}$ the vertex set of both
$\widetilde{t}_{\mathbf{i}}$ and $t_{\mathbf{i}}$. Let
$\widetilde{\nu}_{\mathbf{i}}\in\mathbb{Z}^{D_{l}\times I}$ and
$\nu_{\mathbf{i}}\in\mathbb{Z}^{(D_{l}\sqcup D_{r})\times I}$ the minimal
lifting matrix corresponding to $\widetilde{t}_{\mathbf{i}}$ and
$t_{\mathbf{i}}$ respectively.

######  Lemma 8.3.1.

We have that $(\nu_{\mathbf{i}})_{D_{l},\bullet}=\widetilde{\nu}_{\mathbf{i}}$

###### Proof.

Note that, for any $\alpha\in\Delta$,
$\iota^{*}(X_{\alpha_{l}})=\widetilde{X}_{\alpha_{l}}\otimes 1$. The statement
follows at ones from the fact that $\iota$ is an open embedding and the
diagram (68) is commutative. ∎

######  Theorem 8.3.2.

We have equality
$\overline{\mathcal{A}}(\upharpoonleft\kern-3.50006pt\widetilde{t}_{\mathbf{i}}^{D_{l}})={\mathcal{O}}_{\mathfrak{X}}({\mathfrak{X}}).$

###### Proof.

It’s easy to verify that ${\operatorname{Br}}(G\times
G,G)/(X_{\alpha_{r}}-1\,:\,\alpha\in\Delta)\simeq\mathbb{C}[G]^{U^{-}}$.
Geometrically, the isomorphism corresponds to the closed embedding
${\mathfrak{X}}{\,\longrightarrow\,}{\mathfrak{X}}(G\times G,G)$ defined by
$p\longmapsto\iota(p,e)$. By Theorem 8.2.1, $\mathbb{C}[G]^{U^{-}}$ is the
quotient of
$\overline{\mathcal{A}}(\upharpoonleft\kern-3.50006ptt_{\mathbf{i}}^{D_{l}\sqcup
D_{r}})$ by the ideal
$(\upharpoonleft\kern-3.50006ptx_{\mathbf{i},\alpha_{r}}-1\,:\,\alpha_{r}\in
D_{r})$. We conclude using Corollary 3.1.11 and Lemma 8.3.1. ∎

######  Corollary 8.3.3.

If $G$ is simply laced, the mutation equivalence class of
$\upharpoonleft\kern-3.50006pt\widetilde{t}_{\mathbf{i}}$ doesn’t depend on
$\mathbf{i}.$

###### Proof.

It’s a direct consequence of Corollary 8.2.2. ∎

For $\alpha\in\Delta$ and $w\in W$, we denote by $D^{\varpi_{\alpha}}_{e,w}$
the restriction of $\Delta^{\varpi_{\alpha}}_{e,w}$ to $U.$

######  Lemma 8.3.4.

For $k\in[l]$,
$\upharpoonleft\kern-3.50006pt\widetilde{x}_{\mathbf{i},k}=\Delta^{\varpi_{i_{k}}}_{e,(w_{0})_{\leq
k}}.$

Here $(w_{0})_{\leq k}$ is computed with respect to $\mathbf{i}.$

###### Proof.

By Lemmas 8.3.1 and 8.2.7 we have that
$\upharpoonleft\kern-3.50006pt\widetilde{x}_{\mathbf{i},k}=\widetilde{X}_{(i_{k})_{l}}(1\otimes\widetilde{x}_{\mathbf{i},k})$.
Hence

$\upharpoonleft\kern-3.50006pt\widetilde{x}_{\mathbf{i},k}((h,u))=\varpi_{i_{k}}(h)D^{\varpi_{i_{k}}}_{e,(w_{0})_{\leq
k}}(u)=\Delta^{\varpi_{i_{k}}}_{e,(w_{0})_{\leq k}}((t,u)).$

Since $T\times U$ is open in ${\mathfrak{X}}$, which is irreducible, this
proves the lemma. ∎

######  Lemma 8.3.5.

Let $w\in W$ and $\alpha\in\Delta$.

  1. 1.

There exist $\mathbf{i}^{\prime}\in R(w_{0})$ such that
$\Delta^{\varpi_{\alpha}}_{e,w}$ is a cluster variable of
$\upharpoonleft\kern-3.50006pt\widetilde{t}_{\mathbf{i}^{\prime}}^{D_{l}}$.

  2. 2.

If $G$ is simply laced, then $\Delta^{\varpi_{\alpha}}_{e,w}$ is a cluster
variable of
$\overline{\mathcal{A}}(\upharpoonleft\kern-3.50006pt\widetilde{t_{\mathbf{i}}}^{D_{l}})$,
for any $\mathbf{i}$.

###### Proof.

For the first statement, using Lemma 8.3.4, the proof is literally the same of
Proposition 8.2.10 . The second statement is an obvious consequence of the
first one and of Corollary 8.3.3. ∎

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