Data Ethics

Data ethics is defined as the comprehensive set of ethical principles, norms, and practices governing the collection, storage, processing, sharing, and use of data. This field encompasses not only the regulation of technical processes but also socially and individually accepted approaches regarding the purposes, boundaries, and responsibilities under which data are evaluated.

Today, with the acceleration of digitalization, companies, public institutions, and individuals are producing and processing far more data than ever before. The widespread adoption of technologies such as big data, artificial intelligence, and machine learning has amplified the economic and social impact of data. Indeed, by 2025, it is estimated that companies worldwide will generate approximately 463 exabytes of data per day. This scale brings to the forefront not only the informational processing capacity of the data revolution but also its legal, social, and ethical dimensions.

In this context, data ethics necessitates that technical fields such as statistics and computer science go beyond focusing solely on algorithm development, modeling, or computational efficiency; they must also consider the ethical acceptability of the methods developed and the ways in which these methods are applied. Issues such as privacy, security, discrimination, justice, transparency, and accountability are central to data ethics discussions.

These principles aim to ensure that data are processed fairly, transparently, and responsibly, thereby protecting individual rights, strengthening social trust, and preventing the misuse of data. Thus, data ethics functions not only as a framework for guiding technological development but also as a safeguard mechanism designed to minimize the risks individuals and societies face in the digital world.

Core Principles of Data Ethics

Data ethics is built upon a set of principles established to ensure the responsible management of data. These principles encompass not only technical regulations but also the establishment of social trust, the protection of individual rights, and institutional accountability. In our era of accelerating digitalization, this ethical framework is of critical importance for ensuring that data-driven processes are transparent, fair, and accountable.

Fairness

The fundamental principle is that data collection, processing, and usage must be conducted equally and impartially for all individuals. Algorithms and data sets must be designed to avoid generating bias or excluding specific social, ethnic, gender, or economic groups. For instance, if artificial intelligence systems used in hiring software disproportionately favor candidates of a particular gender or those from certain educational backgrounds, this constitutes a violation of data ethics. Therefore, the principle of fairness plays a critical role in preventing discrimination and promoting an equitable approach.

Transparency

Clear and understandable information must be provided regarding how data are collected, for what purposes they are processed, how long they are retained, and with whom they are shared. For example, when a mobile application seeks access to a user’s location data, it must clearly specify how these data will be used, stored, and when they will be deleted. Transparency enables users to make informed decisions and builds a bridge of trust between digital service providers and users.

Accountability

Both organizations that process data and, when necessary, regulatory authorities are responsible for the security, accuracy, and ethical use of data. This principle demands accountability in the event of ethical violations. Moreover, organizations are expected to develop preventive measures against potential data breaches and take transparent steps to address any issues that arise.

Consent

The collection and processing of data must, as a rule, be based on the clear, informed, and voluntary consent of the data subject. Users must be able to provide informed consent regarding the purposes for which their data will be used and must retain the right to withdraw this consent at any time. The implementation of the consent principle ensures that individuals maintain control over their own digital assets.

Privacy

The protection of personal information is one of the most fundamental elements of data ethics. Data collection and processing must be designed to safeguard individuals’ privacy. Privacy is not merely an individual right but also a universally recognized fundamental human right. Therefore, technological innovations must be developed in a manner that respects individuals’ personal information and is conducted only with their consent.

Security

Collected and stored data must be protected against risks of unauthorized access, disclosure, alteration, or misuse. This protection is not limited to technical measures such as encryption, authentication, and security protocols; it also includes administrative policies, institutional oversight mechanisms, and regulatory standards. The principle of security serves as a foundational pillar for maintaining data integrity and sustaining user trust.

The Importance and Necessity of Data Ethics

Data ethics represents a broader sphere of responsibility that extends beyond mere compliance with existing legal regulations. Legal frameworks such as the European Union’s General Data Protection Regulation (GDPR) compel companies to respect individuals’ fundamental rights and ensure data security. However, such regulations often establish only minimum standards. Ethical conduct enables institutions not only to avoid legal penalties but also to establish social trust and gain public credibility. Even if an organization’s data practices comply with the law, public perception of them as unethical can damage its reputation. Consequently, leading companies and institutions aim not merely to follow rules but to elevate their own ethical standards to higher levels.

The balance between data-driven innovation and individuals’ expectations of privacy and security lies at the heart of data ethics. While technologies such as big data, artificial intelligence, and machine learning offer the potential to enhance user experience and generate economic value, they also pose risks of intrusion into individuals’ personal spheres. Therefore, data ethics functions as a balancing mechanism that ensures technological progress enhances societal benefit without infringing upon individual rights.

Institutions that embrace ethical principles, by consistently prioritizing user safety and privacy, secure not only short-term commercial gains but also long-term sustainable success. Ethical practices help companies build transparent and honest relationships with users, thereby fostering a trust-based ecosystem. Institutions that build trust gain consumer loyalty and achieve competitive advantage. In this sense, data ethics can be regarded both as a responsibility that protects social well-being and as a strategic element at the organizational level.

Challenges and Ethical Dilemmas in Practice

Establishing a healthy and reliable data ecosystem requires not only strengthening technical infrastructure but also identifying and resolving potential ethical issues before they emerge. This process necessitates collaboration among different actors, transparent communication, and a sense of institutional accountability. However, in practice, organizations face various challenges in adhering to ethical principles. These challenges can be categorized under blind spots, diffusion of responsibility, and lack of psychological safety.

Blind Spots

Organizations may not always be aware of all the ethical risks they face. For example, a software engineer developing an algorithm to increase click-through rates may initially fail to anticipate that the system could reduce content diversity over time or exclude certain user groups. To prevent such ethical blind spots, teams must be regularly trained on ethical risks and data must be visible end-to-end. Risk identification becomes even more difficult when the source or endpoint of data lies outside the organization. Therefore, it is critical for organizations to collaborate with business partners who share similar ethical principles and embrace transparency.

Diffusion of Responsibility

Ethical issues often arise not from the mistake of a single individual but from long-standing systems or institutional processes. In such cases, responsibility for resolving the issue may become unclear. Employees may not feel accountable for problems they did not directly cause. This tendency delays problem resolution and can lead to the escalation of ethical risks at the institutional level. Therefore, a collective understanding of responsibility must be embedded in organizational culture, and addressing ethical issues must be recognized as the duty of the entire organization, not just a specific group.

Lack of Psychological Safety

Although managers expect employees to raise potential ethical concerns, younger, less experienced, or pressured employees may remain hesitant to do so. Raising issues may sometimes slow down processes or lead to personal negative consequences. As a result, employees may avoid reporting ethical violations they observe. To establish an ethically healthy work environment, managers must create a safe space where employees can openly express their concerns. Such an environment contributes to resolving ethical issues before they escalate and strengthens organizational learning.

Data Ethics and Sustainability: The ESG Example

Data ethics plays a central role not only in protecting individual rights or monitoring technological applications but also in ensuring the credibility of sustainability policies. Today, environmental, social, and governance (ESG) criteria are recognized as key indicators of responsible investment and corporate sustainability strategies. ESG scores rely on large data sets and the algorithms that process them, using metrics such as carbon emissions calculations, supply chain transparency, and social impact analysis. However, if the data used are unfair, biased, non-accountable, or unverifiable, such systems risk losing credibility and undermining their sustainability claims.

From a data ethics perspective, it is not only technical accuracy that matters but also the sources, methods, and intentions behind the production of data. As writer Cathy O’Neil has stated, “Algorithms are mathematical constructs that automate the errors of the past for the future.” This statement underscores that data-driven decision mechanisms, if improperly managed, can reinforce social inequalities.

The global impact of ESG criteria is substantial. It is estimated that approximately 40 trillion U.S. dollars in financial assets are currently managed according to ESG criteria. However, this widespread acceptance has also triggered methodological debates. Research conducted by MIT Sloan and a 2024 report by the London Stock Exchange reveal that different ESG data providers can evaluate the same company with scoring differences of up to 50 percent. Moreover, approximately 70 percent of investors find ESG data to be of low quality, incomparable, and often inconsistent. This situation is described in the literature as a “standardization crisis” and undermines the reliability of ESG systems. The absence of standards increases the risk of “greenwashing”—the manipulative use of environmental and social responsibility claims.

Data is not a passive record of reality but an input produced within the framework of social relations and power dynamics. As sociologist Bruno Latour notes, the processes of data production determine whose story becomes visible and whose is excluded. If data are not inclusive, the models built upon them operate as black boxes and lack accountability, increasing the likelihood that new inequalities will be generated under the banner of sustainability. Such an approach risks detaching sustainability claims from objective reality and laying the groundwork for a “post-truth sustainability” era shaped by perceptions and interests rather than facts.

Institutional Solutions and Practices

Data ethics cannot be secured solely through individual awareness or legal regulations. For the construction of a sustainable and reliable digital ecosystem, institutions must institutionalize ethical principles within their internal structures and operations. In this context, several key approaches have emerged that are applicable across sectors and can be adopted at the institutional level.

Data Ethics Boards

The creation of independent, multidisciplinary oversight mechanisms is one of the most important steps toward institutional ethical governance. These boards, composed of experts from diverse fields such as law, philosophy, sociology, computer science, and engineering, regularly review algorithmic models used within organizations. During this process, dimensions such as bias, equity, accountability, and human rights are tested to ensure that technological solutions are not only technically sound but also ethically reliable.

Transparent Governance Models

It must be clear and verifiable which data were used to train algorithms, how decisions are made, and through what processes these decisions are reached. Transparency is not limited to informing the public; it also involves the participation of independent audit bodies, regulatory agencies, and stakeholders in the process. Accountable governance mechanisms ensure continuous monitoring of institutional compliance with ethical principles.

Comprehensive Training Programs

Data ethics is not merely a concern for programmers, engineers, or data scientists; it is a responsibility that applies to all employees within an organization. Therefore, staff at all levels must be made aware of and trained on ethical risks. Training programs must cover core topics such as data privacy, bias mitigation, transparent decision-making, and accountability to foster a culture of ethical awareness at the institutional level.

Value-Oriented Partnerships

Data flows often extend beyond organizational boundaries; supply chains, business partners, and third-party service providers are integral parts of this process. Therefore, collaboration with institutions that share similar ethical values, transparency principles, and accountability frameworks is of critical importance. This ensures consistency in data processing and enables the management of ethical risks not only within the organization but across the entire ecosystem.

Artificial Intelligence and Data Ethics

Data ethics and artificial intelligence (AI) ethics are so deeply intertwined that they cannot be considered separately. AI systems derive their functionality and decision-making capacity largely from the data on which they are trained. Consequently, the patterns, biases, and gaps embedded in these data can directly influence the outcomes generated by the systems. Even an AI model designed with good intentions may, over time, produce discriminatory, erroneous, or socially harmful outputs. This necessitates the continuous ethical review of AI applications, not only from a technical perspective but also from an ethical one.

The relationship between data and AI ethics can be examined along three fundamental dimensions. First is the issue of data quality and diversity. If a model is trained exclusively on data collected from a specific geographic region, culture, or social segment, it is highly likely to generate biased results when applied in different contexts. Second is the principle of transparency and accountability. It must be clear and verifiable how AI models are trained, which data are included, and on what algorithmic logic decisions are based. The third dimension concerns the sharing of responsibility. When a decision made by an AI system leads to negative consequences, is only the programmer responsible, or is it the institution using the system? These questions represent one of today’s most contentious ethical dilemmas.

In the age of artificial intelligence, data ethics is viewed not merely as a component of good governance but as a prerequisite for fundamental concepts such as sustainability, social justice, and human rights. An AI system devoid of ethical principles may appear functional in the short term but risks long-term loss of trust, deepening social inequalities, and undermining sustainability goals.

Therefore, technology alone cannot produce correct, fair, and reliable outcomes without a strong institutional culture and a comprehensive ethical framework. Addressing data ethics and AI ethics together not only safeguards individuals but also ensures the continuity of public trust in technology and the construction of a more just digital ecosystem for future generations.