With the development of artificial intelligence technologies, companies and institutions have started to use automated decision-making systems to manage many processes faster and more efficiently. Credit assessments, recruitment, customer analysis, risk calculations and personalised services are increasingly supported by algorithms.

However, when a decision is made by AI systems instead of a person, or largely by such systems, important legal questions arise. When a decision made by an algorithm is wrong, who will be liable? How can people challenge that decision? If the system produces discriminatory results, what legal remedies are available?

The law on automated decision-making systems is one of the new-generation fields of law that aims to ensure AI-supported decision-making mechanisms are used in a way that is transparent, reliable, auditable and consistent with fundamental rights.

What Is an Automated Decision-Making System?

An automated decision-making system is a technological structure that analyses certain data and produces results without human intervention or with only limited human intervention.

These systems can carry out various assessments using predefined algorithms, machine learning models or AI technologies.

Examples of automated decision-making mechanisms include a bank’s system that assesses credit applications automatically, a company’s recruitment tool that ranks candidates using AI, or algorithms that analyse insurance risks.

The Legal Dimension of AI-Supported Decisions

In traditional decision-making, the reasoning of the decision-maker, the assessment method and responsibility can be identified more easily; with algorithmic decision-making systems, this process becomes more complex.

An AI system can produce a result by analysing very large amounts of data, but how that result was reached may not always be clear.

This has made the concepts of “explainability”, “transparency” and “human oversight” important from a legal point of view.

Where Are Automated Decision-Making Systems Used?

Automated decision-making systems are used today in many sectors. As the technology spreads, these areas of use are constantly expanding.

Examples include credit assessment and fraud detection systems in the financial sector, candidate assessment and performance analysis in human resources, risk calculation systems in insurance, diagnostic support technologies in healthcare and personalised recommendation systems in e-commerce.

However, legal risks become more significant where the decision affects individuals’ rights, economic opportunities or working life.

Automated Decision-Making Under KVKK

A large proportion of automated decision-making systems work by using personal data. For this reason, AI-supported decision-making should also be assessed separately from the point of view of personal data protection law.

Analysing a person’s data to make an automated assessment about them may raise various questions regarding the legal grounds for processing, the duty to inform, data security and the rights of the data subject.

Under the Law on the Protection of Personal Data No. 6698 (KVKK), individuals have the right to learn whether data about them is being processed, to request information and, in certain cases, to object.

Why Human Intervention Matters in Automated Decisions

Using AI systems in decision-making should not mean that the human factor is removed entirely.

Human control is especially important in decisions that affect a person’s fundamental rights.

The fact that an algorithm produces a purely mathematical result does not mean that the resulting decision is always fair or lawful. Human assessment, checking the system’s results and intervening where necessary are important for legal certainty.

The Risk of Algorithmic Discrimination

One of the most important legal risks in automated decision-making systems is that they may produce discriminatory results.

Because AI systems are trained on historical data, they can reproduce the biases present in the data sets used.

For example, recruitment systems that systematically disadvantage certain groups of candidates, or financial assessment algorithms that produce unequal results for certain people, may cause legal problems.

For this reason, it is not only the technical accuracy of algorithms that should be assessed, but also whether they are lawful and capable of producing fair results.

Automated Decision-Making Under the AI Act

The European Union’s Artificial Intelligence Act (AI Act) sets out various obligations, especially for high-risk AI systems that may affect individuals’ rights.

AI systems used in human resources, education, critical infrastructure, access to financial services and similar areas may be among those that require a more demanding legal assessment.

For these systems, issues such as risk management, data quality, record-keeping, technical documentation and human oversight become important.

Transparency and Explainability in Automated Decision-Making

Being able to understand why a decision affecting one’s life was made is an important legal issue.

However, in some AI models the decision-making mechanism may be complex, and it may not be possible to show clearly which data the system assessed and how.

This gives rise to the problem of explainability, often described as the “black box” problem, which is one of the central debates in AI law.

Managing Automated Decision-Making Systems in Companies

When using automated decision-making systems, companies should focus not only on the technological benefits but also on the legal risks.

The purpose of the algorithm, its data sources, its effect on the decision, the human control mechanism and the allocation of liability in the event of harm should be determined in advance.

It is especially important to put in place internal AI use policies for systems that affect employees, customers or users.

Contractual Risks in Automated Decision-Making Systems

Many of the AI systems companies use are obtained from third-party technology providers.

For this reason, contracts with service providers should clearly set out the scope of the algorithm’s operation, data use, security obligations, liability in the event of errors and audit rights.

Otherwise, companies may be left to face alone the legal consequences arising from the systems they use.

Legal Audit of Automated Decision-Making Systems

For AI-supported decision-making systems to be used reliably, regular legal audits are necessary.

Such an audit should assess the suitability of the data used, the purpose of the algorithm, the effects of its decisions and its compliance with the legislation.

As technology develops, companies need to manage not only data security but also algorithmic decision-making processes.

Automated Decision-Making and the Law in the Future

The role of artificial intelligence in decision-making will grow further in the coming years. For this reason, what matters for companies and institutions is not to avoid using the technology, but to be able to manage it with legal certainty.

When properly structured, automated decision-making systems can give businesses speed, efficiency and analytical power. However, legal control mechanisms need to be put in place so that these systems do not harm fundamental rights.

In the age of artificial intelligence, the businesses that thrive will not simply be those with powerful algorithms, but those that can manage the technology they use in accordance with the law.