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Confucius and the data
DR. Sonia Bressler1
Abstract
This article explores the intersection of Confucian philosophy and data analysis, proposing a
novel approach to understanding and utilizing data through the lens of Confucian principles.
In a world where digital data plays a central role, the interpretation and ethical use of this data
are increasingly critical. Drawing on concepts from my book “Data-Philosophy,” this article
positions data-philosophy as a necessary bridge between the disciplines of data science and
philosophy, aimed at giving meaningful context to data while addressing fundamental human
questions.
Introduction to Data-Philosophy
Data-philosophy is an emerging discipline that integrates the analytical methods of data
science with the value-oriented questions posed by philosophy. It seeks to provide a holistic
approach to understanding and using data by considering not only its quantitative aspects but
also its qualitative implications. The aim is to transcend mere quantification and interrogate
the purpose and implications of data, viewing it as a carrier of meaning and value. This
approach emphasizes the importance of reflecting on the nature, origin, processing, and
interpretation of data to illuminate broader human conditions.
Applying Confucian Principles to Data Analysis
The article delves into how the philosophical principles of Confucius can be applied to data
analysis. Four key Confucian concepts are explored: Ren (仁), Li (礼), Yi (义), and Zhi (智).
Each of these principles offers a unique perspective on ethical data management and
interpretation.
Ren (仁) - Humanizing Data: Ren, often translated as “benevolence” or “humanity,” is
central to Confucian thought. In the context of data analysis, Ren implies a human-centered
approach that considers the impact of data on individuals and society. Data analysts are
encouraged to use data in ways that promote human well-being and respect for human
1Sonia Bressler has a doctorate in philosophy and epistemology, and teaches communications at university. She
is also founder of “La Route de la Soie- édition” Publishing house. Since 2018, she has been President of the
NGO AFFDU, founded in 1920 by Marie Curie. AFFDU, an association under the French law of 1901,
recognized as being of public utility, was founded in 1920, in the aftermath of the First World War, by women
academics with a great ambition for women and convinced that girls' education is both a factor of peace and the
key to the advancement of women. AFFDU, Association Française des Femmes Diplômées des Universités, is
the French section of Graduate Women International (GWI), an NGO in consultative status with the UN.
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dignity. This principle advocates for the use of data to enhance collective benefits, such as
improving healthcare while safeguarding individual privacy.
Li (礼) - Governance of Data: Li refers to rites and protocols, underscoring the importance
of norms and rules in Confucian society. In data management, Li can be interpreted as the
importance of ethical standards and practices. Adhering to protocols for data collection,
processing, and protection ensures ethical and responsible data use. European and Chinese
regulations like the GDPR and PIPL exemplify how Li can be applied to ensure transparency,
consent, and protection of personal data.
Yi (义) - Justice in Data : Yi represents justice and righteousness, suggesting that data
analysis should be conducted fairly and equitably. This involves combating biases within data
sets and ensuring that conclusions drawn from data are just and representative. The
application of Yi requires constant vigilance to prevent the misuse or unfair application of
data, such as in the development of algorithms that might otherwise perpetuate social
inequalities.
Zhi (智) - Wisdom in Data Interpretation: Zhi, or wisdom, emphasizes the importance of
intelligence and insight in decision-making. For data analysts, this means not only possessing
technical competence but also understanding the broader implications of their analyses.
Wisdom in data interpretation involves discerning meaningful patterns and avoiding
superficial conclusions. This principle advocates for using data to provide deep insights that
contribute to a more comprehensive understanding of complex issues.
Ethical and Legal Perspectives on Data
The article also addresses ethical and legal considerations in data management, comparing
European and Chinese approaches to data governance. The GDPR in Europe and evolving
data laws in China reflect different cultural perspectives on data use and protection. A
Confucian approach to data ethics emphasizes collective responsibility, benevolence, and
justice, suggesting a balanced perspective that respects individual rights while promoting the
common good.
By integrating Confucian principles with data analysis, we can approach data not just as a
technical resource but as a tool for enhancing human understanding and promoting ethical
practices. This interdisciplinary approach is essential for navigating the complexities of the
digital age and fostering a more humane and responsible use of data.
This article aims to initiate a dialogue on the role of Confucian philosophy in modern data
practices, encouraging a more thoughtful and ethical approach to data management and
interpretation. By reflecting on the teachings of Confucius, we can aspire to use data in ways
that are just, wise, and beneficial for all.
Keywords:
Confucius, Data, Data-Philosophy, Governance, Ethic, Ren (仁),Li (礼), Yi (义), Zhi (智).
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Introduction
In today's world, where digital data plays a central role, the question of how to interpret and
use it is becoming ever more acute. Data-philosophy, as I defined it in my essay
Data-Philosophie2, is a necessary bridge between the disciplines of data science and
philosophy. It proposes to give meaning to data, while taking into account the great questions
of our humanity.
At the dawn of the 21st century, our society is facing a challenge on an unprecedented scale:
the digital revolution. The breakneck pace of technological innovation and the explosion of
massive data have turned our way of living, working and thinking on its head.
Data-philosophy aims to explore the philosophical, ethical and social issues surrounding
digital data.
This article is an interdisciplinary exploration of the profound implications of digital data and
its use, informed by the philosophical principles of Confucius. We will examine how these
principles can inform data analysis, data visualization, and offer ethical perspectives on the
laws of data, both in Europe and China.
After defining data-philosophy, we will seek to see how fundamental concepts of Confucian
thought, such as Ren (benevolence, 仁), Li (rites and social norms, 礼), and Yi (justice, 义),
can provide valuable frameworks for data analysis and management. By integrating these
concepts, we can approach data not only as technical entities but also as elements carrying
human meanings and values.
Finally, we will address the ethical and legal issues surrounding data. By comparing
European regulations, such as the GPDR3, and Chinese approaches to data governance, we
will seek to develop a global data ethic that both respects cultural diversities and promotes
Confucian standards of justice and responsibility.
This is an invitation to reflection, discussion and debate, initiating a constructive dialogue
between different disciplines to illuminate our understanding of the complexity and
challenges posed by digital data.
1- Defining data-philosophy
Data-philosophy is the discipline at the intersection of philosophy and data science. In a
context where massive data increasingly shapes our understanding of the world,
data-philosophy aims to unite the analytical methods of data science with the questions of
meaning and value posed by philosophy. It seeks to understand how data can be interpreted to
enrich our understanding of the world and answer the fundamental questions of our existence.
Data science focuses primarily on the collection, analysis and interpretation of data, using
statistical techniques and machine learning algorithms to extract useful information.
However, these technical approaches often lack consideration for the ethical, social and
3GDPR: General Data Protection Regulation. https://gdpr-info.eu/
2Sonia Bressler, Data-Philosophie, éd. Route de la Soie - Éditions, 2023.
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philosophical implications of data. Philosophy, on the other hand, addresses questions of
meaning, value and global understanding, but needs the analytical tools of data science to
process information on a large scale.
Data-philosophy combines these two perspectives to offer a holistic approach. It asks
questions about the nature of data, its reliability, interpretation and use. By integrating
philosophical concerns into data analysis, it helps to shed light on deeper aspects of the
human condition and our interaction with the digital world.
1.1. Giving meaning to data
The data-philosophical approach goes beyond the simple quantification of data, and asks why
and how. It considers data not only as quantitative entities, but also as elements carrying
meanings and values.
Data, taken in isolation, has little meaning. Only by contextualizing them, by understanding
their origin and purpose, can we truly grasp their significance. Data-philosophy explores the
following questions: What does this data tell us about the world? How is it collected, and for
what reasons? What biases can influence their interpretation? What values underpin their
use?
1.1.1 Reflections on the nature of data
This approach is based on in-depth reflection on the nature of the data.
This reflection encompasses several key dimensions:
-Origin and context: data are never neutral; they are produced in specific contexts
and reflect particular intentions and practices. For example, health data is often
collected in clinical or research contexts, with specific objectives such as diagnosis or
the improvement of medical treatments.
-Collection and methodology: data collection methods influence the nature and
reliability of data. Methodological biases can occur, whether in sample selection,
questionnaire design or the use of measurement tools. Data-philosophy examines
these biases and seeks to minimize them to ensure a fair and representative
interpretation of the data.
-Interpretation and bias: the interpretation of data is subject to various biases,
whether cognitive, cultural or technological. For example, machine learning
algorithms can reproduce or amplify biases present in training data. Data-philosophy
offers tools and concepts for identifying and correcting these biases.
-Values and ethics: data is intrinsically linked to values. Their use raises fundamental
ethical issues such as privacy, autonomy and justice. For example, health data, while
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it can improve medical treatment, poses ethical challenges concerning patient privacy
and informed consent.
1.1.2 Applying these concepts to two examples
To illustrate these concepts, let's look at two types of data: health data and education data.
Health data is an excellent example of the complexity and challenges of data-philosophy.
This data can be used to develop personalized treatments and improve public health.
However, they also raise ethical questions about privacy, consent and patient autonomy. A
famous case in point is the use of genetic data by companies such as 23andMe4, which has
sparked debate about commercial use and the protection of individuals' sensitive data.
In education, data can help personalize learning and identify students' needs. However, their
collection and use raise questions about oversight, fairness and confidentiality. For example,
systems for tracking student performance can unintentionally reinforce inequalities if the data
is misinterpreted or biased.
1.1.3 Asking fundamental questions
Data-philosophy explores fundamental questions for making sense of data5:
- What does data tell us about the world? This question invites us to reflect on the
insights that data can reveal, but also on its limits and scope.
- How is data collected, and why? Understanding the data collection process enables us
to better assess its quality and relevance.
- What biases can influence their interpretation? Identifying and correcting biases is
crucial to accurate data interpretation.
- What values underpin the use of data? The ethical and social values that guide data
use must be explicit and aligned with principles of respect and justice.
By integrating these issues into data analysis, data-philosophy seeks to shed light on broader
aspects of humanity, such as dignity, respect and well-being. Ultimately, it aims to promote a
use of data that is both informed and ethical, taking into account its impact on individuals and
society.
1.2. The great questions of humanity
The great questions of humanity - such as justice, truth, ethics and well-being - are at the
heart of data-philosophy. Since ancient times, these questions have been explored by
philosophers from different cultures and traditions, contributing to a richer and more nuanced
understanding of what it means to live a good and just life.
5Boyd, d., & Crawford, K. (2012). “Critical Questions for Big Data: Provocations for a Cultural,
Technological, and Scholarly Phenomenon.” Information, Communication & Society, 15(5), 662-679.
4https://www.23andme.com/en-int/
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In Europe, philosophers such as Plato and Aristotle addressed questions of justice and truth.
Plato, in The Republic, examines what a just society means and how individuals can live in
harmony with moral principles. Aristotle, in Nicomachean Ethics, explores the notion of
virtue and the concept of ethical living, emphasizing the importance of well-being
(eudaimonia) for a fulfilled life.
In modern times, thinkers such as Immanuel Kant and John Rawls have continued this
exploration. Kant, in his Critique of Practical Reason, insists on the importance of morality
and ethics based on rationality and human dignity. John Rawls, in Theory of Justice6,
proposes principles of equitable justice that have profoundly influenced contemporary
political philosophy and discussions of equity and rights.
In China, Confucian philosophy has profoundly influenced perspectives on ethics, justice and
well-being. Confucius, in The Talks, emphasizes personal virtues and harmonious social
relations. Mencius, one of Confucius' greatest disciples, developed these ideas, emphasizing
the innate goodness of human beings and the importance of cultivating virtues for a just
society.
Laozi, founder of Taoism, offers a complementary perspective with the Dao de jing (道德
经)7, where he explores truth and harmony with nature, stressing the importance of balance
and non-action (wu wei) to achieve a state of well-being and inner peace.
By combining these rich philosophical traditions, data-philosophy seeks to apply these
timeless concepts to the contemporary challenges posed by the explosion of digital data. It
explores how these principles can guide the use of data to address questions of justice, truth,
ethics and well-being in our interconnected world.
1.2.1 Justice
Justice is a fundamental value that can be explored through data analysis. Data-philosophy
poses crucial questions: How can data be used to promote a fair distribution of resources?
How can we avoid biases in decision-making algorithms that can reproduce or amplify social
inequalities?
For example, the use of data in housing policies can reveal systemic discrimination and guide
interventions for a more equitable distribution of resources. However, it is imperative to
monitor algorithmic biases. Facial recognition algorithms, for example, have often been
criticized for their racial bias, illustrating the need to develop fairer and more equitable
systems8.
1.2.2 Truth
Truth is another key issue. Data-philosophy questions the veracity of data, the transparency of
collection and analysis processes, and the way in which data is presented to the public. It
raises critical questions about the integrity of information and the possible manipulation of
data to serve particular interests.
8O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens
Democracy. Crown Publishing Group.
7Tao-tö-king, « livre de la voie et de la vertu » in french.
6Rawls, J. (1971). A Theory of Justice. Harvard University Press.
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An example of this concern is the spread of false information or "fake news" via social
media. Data-philosophy proposes methods for verifying the veracity of information and
developing transparency tools that can help the public discern the truth9.
1.2.3 Ethics
Ethics are omnipresent in data-philosophy. It guides the use of data in a responsible manner
that respects individual rights. Debates on privacy, informed consent and digital surveillance
are at the heart of this reflection.
The collection and use of health data, for example, must balance potential public health
benefits with individuals' rights to privacy and autonomy. Ethical frameworks, such as those
proposed by the GRPD in Europe, aim to ensure that data is used fairly and transparently10.
1.2.4 Well-being
Well-being is a central concern. How can data be used to improve the quality of life of
individuals and communities? What, for example, are the consequences of using data on
people's mental and physical health?
Data on working environments, for example, can be analyzed to improve working conditions
and promote employee well-being. However, it is crucial to consider the psychological
impact of constant monitoring and performance-based appraisals, which can lead to stress
and anxiety11.
1.2.5 Interactions between major issues
These major issues do not stand alone, but often interact with each other. For example, the
quest for truth can conflict with ethical considerations when it comes to data transparency and
privacy. Similarly, promoting justice through data requires reflection on the ethical impacts
and truthfulness of the information used.
By integrating these major issues, data-philosophy strives to promote the responsible and
informed use of data. It ensures that data are not just technical tools, but also means of
promoting human well-being, while taking into account their impact on society and the
individual. This holistic approach makes it possible to develop data systems that are not only
efficient, but also fair, ethical and beneficial to all.
11 Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New
Frontier of Power. PublicAffairs.
10 Floridi, L. (2013). The Philosophy of Information. Oxford University Press.
9Boyd, d., & Crawford, K. (2012). “Critical Questions for Big Data: Provocations for a Cultural,
Technological, and Scholarly Phenomenon.” Information, Communication & Society, 15(5), 662-679.
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2- Defining data
Data is much more than just digital information; it is a reflection of our interactions, our
behaviors, and our social, economic and political systems. Indeed, data are crucial elements
that testify to the complexity and richness of human activities. The way in which data is
perceived and managed varies considerably according to cultural and legal contexts,
influenced by societal values, political priorities, and philosophical traditions specific to each
region.
Understanding data in the context of two different cultures - France and China - sheds light
on distinct approaches that can, when combined, offer new and innovative perspectives on
data governance. Each approach brings its own advantages and challenges, and their
combination could potentially lead to a more balanced and effective system.
2.1 The European conception of Data
In Europe, and particularly in France, the conception of data is profoundly influenced by the
General Data Protection Regulation (GDPR). This regulation, adopted by the European
Union in 2016 and implemented in 2018, defines a strict framework focused on the protection
of privacy and the rights of individuals. It highlights fundamental principles such as
transparency, consent, and the security of personal data. According to the GDPR, individuals
have the right to know how their data is collected, used and protected, reflecting a valuing of
the individual and his or her fundamental rights in the digital ecosystem.
2.1.1 Fundamental principles of the GDPR
The GDPR is based on several key principles:
- transparency: individuals must be informed in a clear and understandable way about
how their data is processed. This transparency is crucial to establishing a relationship
of trust between users and the entities collecting the data ;
- consent: personal data can only be collected and processed with the explicit consent
of individuals. This consent must be given freely, in an informed manner and for
specific purposes.
- security: entities must implement appropriate security measures to protect personal
data against breach, loss or unauthorized access.
As the Commission Nationale de l'Informatique et des Libertés (CNIL) points out, "personal
data must be collected fairly and lawfully, and for specified, explicit and legitimate
purposes"12.This concept emphasizes an instrumental and functional approach to
information, where regulation of data collection, processing and use is essential to protect
individual rights and freedoms.
12 Commission Nationale de l’Informatique et des Libertés (CNIL). “Les principes de protection des
données.” CNIL, 2022
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2.1.2 Tradition of respect for individual freedoms
This European approach is deeply rooted in a tradition of respect for individual freedoms and
privacy, inherited from democratic values and human rights. Philosophers such as John
Locke13 and Immanuel Kant14 stressed the importance of protecting the individual against
unwarranted intrusions, laying the theoretical foundations for modern regulations such as the
GDPR.
2.1.3 Challenges and criticisms
However, it is crucial to recognize that this vision is not without its critics and challenges:
- government access to data: laws such as the Patriot Act in the US have raised
concerns about government access to personal data. While the GDPR offers robust
protections, there are still tensions between national security and privacy;
- the use of algorithms for a variety of purposes can raise ethical and transparency
issues. Algorithms can be biased, and their opaque operation can make it difficult to
assess their fairness and impartiality. Notable cases of algorithmic discrimination,
such as racial bias in facial recognition systems, illustrate these challenges15.
The documentary Nothing to Hide by Marc Meillassoux and Mihaela Gladovic, and the work
of Eric Sadin16, highlight these complex issues and the ambiguities inherent in data
governance in Western societies.
The European conception of data, exemplified by the RGPD, seeks to balance technological
innovation with the protection of individual rights. This approach emphasizes transparency,
consent and security, while facing ongoing challenges related to governmental access to data
and the ethical use of algorithms. By integrating these principles into data governance,
Europe is striving to create a framework where technology can develop while respecting the
rights and dignity of individuals.
To properly apply philosophical principles to data analysis, it is crucial to understand the
cultural and legal contexts that influence the perception and management of data. Data are
not simply neutral entities; they are profoundly shaped by societal values, political priorities
and philosophical traditions specific to each region. Therefore, before exploring how
Confucian teachings can inform data analysis, we must first define what "data" means in
different cultural contexts. This understanding is essential for a nuanced and effective
application of ethical and philosophical principles.
16 Sadin, E. (2015). La Silicolonisation du Monde. L’Échappée.
15 O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens
Democracy. Crown Publishing Group.
14 Cf. Kant, E. (1788). Critique of Practical Reason. Cambridge University Press.
13 Locke, An Essay Concerning Human Understanding. Oxford University Press.
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2.2 The Chinese approach to data
In China, the conception of data follows a different logic, where data governance is closely
linked to the objectives of national development, security, and technological innovation. The
Personal Information Protection Law (PIPL), adopted in 2021, marks a significant step
forward in the protection of personal data in China. However, data management is also seen
as a strategic tool for socio-economic development and social stability.
2.2.1 Legislative framework and policy objectives
The PIPL17 establishes a rigorous legal framework for the protection of personal information,
aimed at strengthening data security and protecting citizens' privacy. However, unlike the
European approach, data governance in China is strongly influenced by the imperatives of
national security and economic development. Data is seen as a crucial resource for the
country, contributing to economic growth, technological innovation and social stability.
This pragmatic, utilitarian approach to data is rooted in the country's cultural traditions and
political objectives. In Mandarin, the word used to talk about "data" is 数据 (shùjù)18, which
is made up of two ideograms: 数(shù) meaning "number" or "quantity" and 据(jù) meaning
"to rely on" or "data". Together, 数据 (shùjù) captures the idea of quantified data used as a
basis for analysis and decision-making.
2.2.2 Cultural and strategic perspective
The Chinese perspective on data reflects a recognition of its strategic importance in
supporting decision-making and innovation. Data is seen as an essential national asset,
comparable to natural resources such as energy or water. This vision is aligned with the
Chinese government's development strategies, which emphasize technological innovation,
industrial modernization and national security.
As Qiang (2021) explains in his study of data governance in China, "data is seen as an
essential national resource, comparable to energy or water, which must be protected and
exploited for collective well-being and economic development.19" This approach reflects the
importance attached to the use of data for strategic purposes, such as artificial intelligence,
surveillance and economic planning.
2.2.3 Ethical and Practical Implications
The management of data in China also raises ethical and practical questions. The focus on
national security can lead to increased surveillance and restrictions on individuals’ privacy.
Transparency and consent, although important, are often subordinated to security imperatives
and political objectives. Critics highlight the risks of state surveillance and the lack of
transparency in data management.
19 Qiang, X. (2021). Data Governance in China: Implications for National Security and Economic
Development. Chinese Journal of International Law.
18 数据 (shùjù).” Modern Chinese Dictionary. Beijing: Commercial Press.
17 Personal Information Protection Law of the People’s Republic of China (PIPL). (2021). National
People’s Congress.
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- Surveillance and Privacy: The state plays a central role in data collection and analysis,
which can lead to extensive surveillance practices. Facial recognition technologies
and social credit systems illustrate how data can be utilized.
- Technological Development: Data is also a crucial driver for technological
development in China. Chinese tech companies, such as Alibaba and Tencent, play a
key role in innovation and the application of big data and artificial intelligence
technologies, supported by favorable government policies.
The Chinese approach to data, with its emphasis on national development, security, and
technological innovation, offers a distinct but complementary perspective to that of Europe,
which focuses on the protection of individual freedoms and privacy. Integrating these two
approaches presents unique challenges and opportunities. By comparing these perspectives,
we can identify strategies to combine the best practices of each system, aiming to create a
data governance framework that balances national security and development needs with the
protection of individual rights. This comparison of European and Chinese data governance
approaches is what we will now explore.
2.3 Comparison of European and Chinese Approaches
The European and Chinese approaches to data governance reflect distinct cultural and
political priorities, though they share some similarities in terms of personal data protection.
This section compares these approaches from various angles: priorities and values, regulatory
frameworks, transparency and consent, as well as ethical and practical issues.
2.3.1 Priorities and Values
In Europe, the focus is on protecting individual freedoms and privacy. Principles of
transparency, informed consent, and data security are at the core of regulation. This approach
is influenced by a long tradition of protecting individual rights against state and private
enterprise intrusions. Democratic values and human rights, as advocated by philosophers like
John Locke and Immanuel Kant, emphasize the importance of protecting the individual
against unjust intrusions (Locke, 1690; Kant, 1788).
In China, data governance emphasizes national development, public security, and
technological innovation. While personal data protection is a concern, it is often subordinated
to national security and economic development imperatives. Data is seen as a strategic
resource essential for the country’s growth and stability. This approach reflects collective
values and a priority on national security and collective well-being (Qiang, 2021).
2.3.2 Regulatory Frameworks
In Europe, the GDPR represents a rigorous regulatory framework that imposes strict
obligations on companies and governments regarding personal data processing. It aims to
ensure that citizens’ rights are proactively and reactively protected. The GDPR establishes
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clear principles of transparency, consent, and security, and imposes severe penalties for
non-compliance (GDPR, 2016).
In China, the PIPL also establishes rules for personal data protection, but it is integrated into
a broader framework of internet and information technology regulation, where surveillance
plays a significant role. The PIPL focuses on data protection in the context of national
security and development objectives. Regulation is often more flexible in terms of
surveillance, allowing extensive data use by the state for strategic purposes (PIPL, 2021).
2.3.3 Transparency and Consent
In Europe, transparency and consent are pillars of the GDPR. Individuals must be informed
about how their data is used and give explicit consent for data processing. One limitation of
the current consent model is that it is not always “informed.” To benefit from an application,
users often consent to data use without fully understanding what they are agreeing to (CNIL,
2022).
In China, while consent and transparency are also mentioned in the PIPL, their
implementation can be influenced by national security requirements and political objectives.
Individuals may have limited control over their data when security or national development
considerations are at play. Consent can be obtained more prescriptively, and transparency
may be limited due to security concerns (Qiang, 2021).
2.3.4 Ethical and Practical Issues
In Europe, ethical issues related to surveillance, algorithm use, and privacy protection are
hotly debated. Critics of the GDPR often point out implementation problems and conflicts
between security and privacy. For example, the complexity of algorithms and the opaque
nature of certain surveillance practices pose significant challenges (O’Neil, 2016;
Meillassoux & Gladovic, 2017).
In China, ethical issues include balancing data protection and national security, as well as
using data for social control. Critics highlight the risks of state surveillance and the lack of
transparency in data management. Facial recognition technologies and social credit systems
are examples of how data can be used to monitor and control citizens’ behavior (Qiang,
2021).
The European and Chinese approaches to data governance show marked differences in
cultural and political priorities, but they also share common goals of personal data protection.
In Europe, the focus is on protecting individual freedoms and privacy, while in China, data is
seen as a strategic resource essential for national development and security. These differences
highlight the importance of cultural and political contexts in how data is perceived and
managed. Combining European and Chinese perspectives could develop data management
strategies that incorporate the best practices of both approaches. This would address the
challenges and opportunities of a globalized and interconnected world, ensuring both
individual rights protection and strategic data use for collective well-being.
The comparison of European and Chinese data approaches highlights the diversity of data
governance methods. However, beyond legal frameworks and cultural contexts, it is essential
to integrate ethical and philosophical perspectives to ensure responsible and beneficial data
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use for society. The teachings of Confucius, with their deep humanism and ethical values,
offer a valuable framework to guide this integration. We will now explore how Confucius’s
philosophical principles can be applied to data analysis, enriching our approach to data
philosophy.
3. Philosophical Principles of Confucius Applied to Data Analysis
Confucius’ thought, articulated around his deeply humanistic and ethical teachings, offers
unique perspectives for the analysis of digital data. Confucius (551-479 BCE), one of the
greatest Chinese philosophers, developed a philosophy centered on humanity, morality, and
wisdom—values that resonate particularly in the current context of data management. In an
era where data influences many aspects of our daily lives, Confucian principles can guide us
toward a more thoughtful and ethical use of this information20.
Confucius’ teachings emphasize social harmony, personal virtue, and justice, essential
elements for ethical data governance. By exploring key concepts such as Ren (仁), Li (礼), Yi
(义), and Zhi (智), we can integrate a human and moral dimension into data science. Ren,
often translated as “benevolence” or “humanity,” urges us to consider the impact of data on
individuals and society. Li, referring to rites and social norms, highlights the importance of
ethical practices in data management. Yi, or justice, calls for fair and unbiased data analysis.
Finally, Zhi, wisdom, reminds us to consider the broader implications of our data analyses21.
Applying Confucian principles to data analysis can transform our approach to digital
technologies. For example, by integrating Ren, we can promote a data usage centered on
human well-being, respecting privacy and human dignity. Li helps us establish clear ethical
standards for data collection and processing, inspired by regulations like the GDPR in
Europe. Yi guides us toward fair and equitable data analysis, combating algorithmic biases.
Finally, Zhi pushes us to go beyond mere statistics to understand the social and ethical
implications of data22.
These principles enrich not only our understanding of data but also underscore the
importance of social responsibility and wisdom in its management and interpretation. By
integrating these Confucian values, we can develop a more ethical and human approach to
data science, ensuring that technology serves the common good and respects individual
rights. Confucius’ teachings thus provide a solid philosophical framework to address the
ethical challenges posed by the digital age, encouraging us to use data responsibly and
wisely23.
23 Cheng, C. Y. (2002). Confucian Ethics in Retrospect and Prospect. Springer.
22 Tu, W. M. (1998). Confucian Traditions in East Asian Modernity: Moral Education and Economic
Culture in Japan and the Four Mini-Dragons. Harvard University Press.
21 Hall, D. L., & Ames, R. T. (1987). Thinking Through Confucius. State University of New York Press.
20 Ames, R. T., & Rosemont, H. (1998). The Analects of Confucius: A Philosophical Translation.
Ballantine Books.
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3.1. Ren (仁) and the Humanization of Data
Ren, often translated as “benevolence” or “humanity,” is one of the pillars of Confucian
philosophy. Confucius himself describes it as the foundation of all virtues: “Ren means to
love others” (Analects, 12:22). Applied to data analysis, Ren implies a human-centered
approach, where the impact of data on individuals and society is always considered.
3.1.1 Applying Ren (仁) to Data Analysis
Applied to data analysis, Ren implies a human-centered approach, where the impact of data
on individuals and society is always considered. Data analysts, from this perspective, must
handle data with particular attention to promoting well-being and respecting human dignity.
This means that data-driven decisions should always aim to improve human conditions,
avoiding practices that could harm privacy or individual rights.
We can illustrate this with three examples: data analysis in healthcare, education, and the
workplace.
The use of health data to improve patient care is a concrete illustration of this principle. For
example, health information systems can analyze patient data to identify trends and risks,
enabling preventive intervention and personalized care. However, this must be done while
strictly respecting patient privacy and rights. The protection of personal data, as emphasized
by regulations such as the GDPR in Europe, is essential to ensure that the benefits of health
data analysis do not compromise individual rights (CNIL, 2022).
In the field of education, Ren can guide the use of data to improve student learning. For
example, learning data analysis can help identify students’ specific needs and tailor
pedagogical methods to better meet those needs. However, this must be done with particular
attention to the confidentiality of student information and respect for their autonomy24.
In the world of work, applying Ren means using employee data to improve working
conditions and well-being. For example, data on employee performance and well-being can
be used to identify areas of stress and implement support programs. However, it is crucial that
this use of data is transparent and respectful of employee privacy, ensuring that data is not
used in a punitive or discriminatory manner25.
3.1.2 Ethical Reflection
Ren encourages us to use data not only for technological or economic gains but primarily for
collective benefit and human development. This ethical approach is essential in a context
where data can easily be diverted for profit at the expense of individual and collective
well-being. For example, the monetization of personal data by tech companies raises
25 Colbert, A. E., Yee, N., & George, G. (2016). “The digital workforce and the workplace of the future.”
Academy of Management Journal, 59(3), 731-739.
24 Williamson, B. (2017). Big Data in Education: The Digital Future of Learning, Policy and Practice.
SAGE Publications.
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significant ethical questions about privacy respect and the exploitation of users’ data without
their informed consent26.
3.2. Li (礼) and Data Governance
Li, or rites and protocols, refers to the importance of norms and rules in Confucian society.
Confucius taught that respect for rites and social conventions is essential to maintaining
harmony and order: “If people are guided by laws and unified by punishments, they will
avoid the punishments but have no sense of shame. If people are guided by virtue and unified
by the rules of propriety, they will have not only a sense of shame but also a zeal to
improve.27”
3.2.1 Application of Li (礼) to Data Governance
In the context of data, Li (礼) can be interpreted as the importance of norms and ethical
practices in the management and analysis of data. This includes respecting protocols for data
collection, processing, and protection to ensure ethical and responsible use of data. By
establishing clear and transparent rules for data governance, we can create an environment of
trust and digital harmony.
From a practical perspective, we can look at the GDPR, ethical frameworks, and international
standards.
- The GDPR in Europe: Norms such as the General Data Protection Regulation
(GDPR) in Europe exemplify this principle of Li applied to the modern world of data.
The GDPR sets strict protocols for the collection, processing, and protection of
personal data, ensuring transparency and consent. For example, companies must
obtain explicit consent from users before collecting their data, and they must inform
users about how their data will be used28.
- Ethical and Policy Frameworks: In China, Li could be reflected in ethical and policy
frameworks that balance technological development with personal data protection.
The Personal Information Protection Law (PIPL), although integrated into a broader
context of internet regulation, also imposes protocols for personal data protection,
emphasizing the importance of ethical data governance29.
- International Standards: International standards such as the United Nations Guiding
Principles on Business and Human Rights also encourage ethical practices in data
29 Personal Information Protection Law of the People’s Republic of China (PIPL). (2021). National
People’s Congress.
28 General Data Protection Regulation (GDPR). (2016). Official Journal of the European Union.
https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A32016R0679
27 Analectes, 2:3
26 Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New
Frontier of Power. PublicAffairs.
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management. These principles state that businesses must respect human rights in all
their operations, including data collection and processing30.
3.2.2 Ethical Reflection
Respecting norms and protocols is essential to ensuring that data is used ethically and
responsibly. Li, as a Confucian principle, reminds us of the importance of structure and
discipline in data governance. By following ethical and transparent practices, we can not only
protect individual rights but also promote trust and social harmony.
- Transparency: Transparency in data management means that individuals must be
informed in a clear and understandable manner about how their data is used. This
includes information on the purposes of data collection, the parties with whom the
data is shared, and the protection measures in place.
- Informed Consent: Obtaining informed consent from users is a crucial aspect of Li.
Individuals must fully understand the implications of data collection and have the
ability to give or withdraw their consent at any time.
The practical implications of applying Li in data governance are vast. By establishing clear
protocols and adhering to ethical standards, organizations can minimize the risks of privacy
violations and data loss. Additionally, this can strengthen consumer and citizen trust in data
management systems.
3.3. Yi (义) and Data Justice
Yi, or justice and rectitude, is another key principle of Confucianism. Confucius said: “The
wise desire justice, the common man desires favor.” This principle emphasizes integrity,
fairness, and honesty—values that are essential in the field of data analysis. In the context of
data, Yi suggests that data analysis should be conducted in a fair and equitable manner. This
involves combating biases in data and ensuring that the conclusions drawn from analyses are
just and representative.
3.3.1 Application of Yi (义) to Data Justice
Applying Yi requires constant vigilance to ensure that data is not used abusively or unfairly.
Biases in data and algorithms can lead to systemic discrimination, which contradicts the
principles of justice and equity. Therefore, integrating Yi into data analysis demands
particular attention to biases and fairness throughout the data collection, processing, and
interpretation processes.
30 UNGP. (2011). “Guiding Principles on Business and Human Rights: Implementing the United Nations
‘Protect, Respect and Remedy’ Framework.” United Nations.
https://www.ohchr.org/sites/default/files/documents/publications/guidingprinciplesbusinesshr_en.pdf
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How can Yi be applied? Let’s consider three examples: the development of Artificial
Intelligence (AI) algorithms, equity in bank lending, and justice in judicial systems.
- In the development of AI algorithms, it is crucial to identify and correct biases that
could discriminate against certain groups of people. For instance, facial recognition
systems have been criticized for racial and gender biases, leading to higher false
recognition rates for women and people of color. Applying Yi in this context involves
developing transparent and fair algorithms that account for population diversity.
- Another example is the use of data in credit decision-making. Algorithms determining
loan eligibility can often reproduce socio-economic biases, systematically excluding
certain groups. By applying Yi, banks and financial institutions can ensure that their
credit models are fair and do not unjustly discriminate based on race, gender, or
socio-economic status.
- Judicial systems increasingly use algorithms to assess recidivism risks and assist in
sentencing decisions. However, studies have shown that these systems can be biased
against racial minorities31. Applying Yi in this field involves developing transparent
algorithms and implementing oversight mechanisms to ensure that decisions are just
and equitable.
3.3.2 Ethical Reflection
Yi (义) reminds us that justice must be at the core of all data-related activities, ensuring that
decisions made are fair and based on honest and accurate information. This also means that
organizations must be transparent about how they collect, process, and use data and be
accountable for their practices.
- Transparency is essential to ensuring justice in data analysis. Individuals and
communities must be informed about how their data is used and have the opportunity
to challenge decisions made based on this data.
- Organizations must be accountable for the fairness and justice of their data
management practices. This includes implementing mechanisms to detect and correct
biases and ensuring that data is used ethically and equitably.
The practical implications of applying Yi in data governance are therefore vast. By ensuring
that data is analyzed and used in a fair and equitable manner, organizations can not only
avoid discrimination and injustices but also strengthen user and customer trust in their
systems and practices.
31 Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). “Machine Bias: There’s software used across
the country to predict future criminals. And it’s biased against blacks.” ProPublica. -
https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
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3.4. Zhi (智) and Wisdom in Data Interpretation
Zhi, or wisdom, is an essential virtue in Confucian philosophy, emphasizing the importance
of intelligence and insight in decision-making. Confucius declared: “The wise man seeks
what is within himself. The unwise man seeks what is in others.32” Applying Zhi to data
analysis means that analysts must not only be technically proficient but also capable of
understanding the broader implications of their analyses.
3.4.1 Application of Zhi to Data Interpretation
In the context of data, Zhi implies that analysts must exercise discernment to interpret data in
a way that provides true understanding rather than merely an accumulation of numbers. A
wise interpretation of data requires looking beyond statistics to comprehend their impact on
individuals and society.
To understand this, we can consider three cases: strategic decisions for businesses, public
policies, and health.
- Business Strategic Decisions: Applying Zhi can be seen in the use of data for strategic
decision-making in businesses. For example, a company might analyze sales data to
identify consumption trends. However, instead of merely acknowledging these
apparent trends, a wise interpretation of the data would involve understanding the
underlying motivations of consumers, the socio-economic factors influencing these
trends, and the potential impacts of strategic decisions on all stakeholders33.
- Public Policies: Governments can use data to plan urban development and public
policies. A wise interpretation of urban data would include not only population
statistics and growth trends but also the social and environmental impacts of
development projects. For instance, implementing public transportation policies based
on a deep understanding of citizens’ needs can improve the quality of life and
promote sustainability34.
- Health: In public health, using data to manage epidemics requires a wise
understanding of social and behavioral dynamics. For example, the response to the
COVID-19 pandemic showed that decisions based solely on epidemiological models
without considering human behavior and social impacts can be ineffective. A wise
approach would integrate data on mobility, social interactions, and people’s attitudes
toward public health measures35.
35 Ioannidis, J. P. A., Cripps, S., & Tanner, M. A. (2020). “Forecasting for COVID-19 has failed.”
International Journal of Forecasting.
34 Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., … &
Portugali, Y. (2012). “Smart cities of the future.” European Physical Journal Special Topics, 214(1),
481-518.
33 Davenport, T. H., & Harris, J. G. (2017). Competing on Analytics: The New Science of Winning. Harvard
Business Review Press.
32 Analectes, 15:21.
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3.4.2 Ethical Reflection
Zhi reminds us that a wise interpretation of data requires looking beyond statistics to
understand their impact on individuals and society. This involves:
- Considering Context: Data should not be interpreted in isolation. It is crucial to
understand the context in which data was collected and the dynamics that can
influence its meaning.
- Anticipating Consequences: Analysts must anticipate the consequences of their
interpretations and data-based decisions. This includes short-term and long-term
impacts on individuals, communities, and the environment.
The practical implications of applying Zhi in data interpretation are vast. By exercising
wisdom, organizations can make better-informed decisions that consider broader
implications, minimizing risks and maximizing benefits for all stakeholders.
Conclusion
In conclusion, integrating Confucian philosophical principles into digital data analysis offers
an enriching and necessary approach for navigating the era of big data. The concepts of Ren
(benevolence), Li (rites and social norms), Yi (justice), and Zhi (wisdom) provide essential
humanistic and ethical perspectives to ensure that data is used responsibly and beneficially
for society. These Confucian principles enhance our understanding of data by placing it
within a broader context of human and social values.
The comparison between European and Chinese data concepts clearly shows that cultural
contexts profoundly influence data governance. The European approach, focused on
protecting individual freedoms and privacy, contrasts with the Chinese approach, which
emphasizes national security and economic development. These differences highlight the
importance of developing data governance frameworks that respect cultural values while
integrating universal ethical principles. Such integration addresses the diverse needs of
modern societies while ensuring ethical and equitable data management.
Applying Confucius’s teachings to data science reminds us of the importance of considering
the impact of data on individuals and society. By placing humans at the center of data
analysis, we can ensure that data management practices respect human dignity and promote
collective well-being. For example, Ren encourages a human-centered approach, using data
to improve the human condition without compromising privacy. Li reminds us of the
importance of ethical norms and transparent practices in data management. Yi emphasizes the
need to combat biases and ensure justice and equity in data analysis. Finally, Zhi underscores
the importance of wisdom and contextual understanding in data interpretation.
For the future, it is crucial to continue this interdisciplinary reflection, further integrating
philosophical and ethical perspectives into the development of information technologies.
Collaboration between philosophers, data scientists, legislators, and citizens is essential to
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creating a data ecosystem that promotes human well-being and social development. For
example, regulations such as the GDPR in Europe and the PIPL in China show the
importance of robust legal frameworks to protect individuals’ rights while enabling
technological innovation. However, these regulations must be constantly reevaluated and
adapted to address emerging challenges posed by the rapid evolution of information
technologies.
Moreover, the ethical challenges posed by the use of algorithms and big data, such as
algorithmic biases and surveillance, require continuous attention. Researchers and
practitioners must develop methodologies to identify and correct biases in algorithms, ensure
transparency in data collection and analysis processes, and promote a culture of responsibility
and respect for human rights in data management.
Ultimately, data-philosophy, enriched by the teachings of Confucius, offers a promising path
for navigating the complex challenges of our digital world. It invites us to rethink our
approach to data by integrating ethical and humanistic perspectives, promoting justice and
equity, and placing wisdom at the heart of our data interpretation. By embracing this
approach, we can develop data management systems that not only meet the technological and
economic needs of our societies but also respect fundamental human values and contribute to
collective well-being.
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