How GDPR Affects User Profiling and Automated Decision-Making
Understanding the implications of data protection laws is crucial in today’s increasingly digital world. The General Data Protection Regulation (GDPR), which came into force in May 2018, is one of the most significant legislative frameworks affecting how organisations handle personal data. As such, it profoundly influences both user profiling and automated decision-making — two fast-evolving areas driven by advancements in artificial intelligence and data analytics. With individuals sharing more personal information online than ever before, and companies eager to harness this data for strategic advantage, the balance between innovation and privacy protection has moved into sharp focus.
In this discussion, we explore how these activities are regulated, the challenges and opportunities presented by GDPR, and how organisations can navigate the landscape responsibly while building trust and ensuring compliance.
The rise of data-driven decision-making
Advancements in computing power and the explosive growth of data have made it easier for organisations to extract insights from the information they collect about individuals. User profiling—a process that involves analysing an individual’s behaviour, preferences, and attributes—enables businesses to tailor services, personalise marketing campaigns, and optimise outcomes. Similarly, automated decision-making leverages algorithms to make determinations without human intervention in areas like credit scoring, job recruitment, retail recommendations, and even legal judgments.
While these techniques deliver efficiency, personalisation, and cost savings, they also raise significant concerns related to privacy, transparency, and fairness. When algorithms drive decisions that impact people’s lives, questions arise: Is the logic behind the decision explainable? Is there racial or gender bias built into the model? Can the decision be challenged?
These are precisely the types of issues GDPR aims to address within the European Economic Area—and by extension, globally, as its extraterritorial scope covers any entity processing data about EU citizens.
Profiling under legal scrutiny
Within the framework of GDPR, profiling is defined as any form of automated processing of personal data intended to evaluate certain aspects of an individual, particularly to analyse or predict aspects such as performance at work, economic situation, health, personal preferences, interests, reliability, behaviour, location, or movements.
The regulation does not ban profiling entirely; instead, it places constraints on when and how it can occur. Profiling is permitted when it is necessary for entering into or performing a contract, authorised by Union or Member State law, or based on explicit consent. This last condition—consent—is a cornerstone of GDPR, but gaining meaningful and informed consent can be a complex matter, especially when users may not fully understand what profiling entails or how their data is being used.
To comply, organisations must provide individuals with “clear and meaningful information” at the point of data collection. This includes explaining the logic behind the profiling, its significance, and the potential consequences of such processing. This level of transparency is not only a legal obligation but also an opportunity for businesses to build trust through responsible data stewardship.
The special case of automated decisions
One of the most widely discussed provisions of GDPR relates to the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal or similarly significant effects on the individual.
This clause has far-reaching implications. For instance, if a bank uses a purely algorithmic model to determine whether someone qualifies for a loan, and the model denies the application without any human involvement, this may be deemed unlawful under the regulation unless specific conditions are met. GDPR makes it clear that automated decision-making is only permissible if it is necessary for contract execution, authorised by law, or executed with the individual’s explicit consent.
Furthermore, even when automated decision-making is legally justified, data subjects are granted fundamental rights, including the right to obtain human intervention, express their point of view, and contest the decision. These protections ensure that individuals retain a degree of agency when machines make decisions that affect their lives.
Challenges in achieving compliance
Ensuring compliance with GDPR’s stipulations around profiling and automated decision-making is no minor task. For one, businesses must grapple with the technical complexity of explaining intricate algorithmic models in a way that is intelligible to the average person. Many AI systems, particularly those based on deep learning, operate as “black boxes” with limited interpretability.
The obligation to provide meaningful information about the logic involved in decision-making forces developers to pay close attention to model transparency and documentation. Data Protection Impact Assessments (DPIAs) are often required when high-risk processing is involved, especially where new technologies or extensive profiling are used. These assessments help organisations identify and mitigate risks to individual rights at the planning stage of a project, rather than after an issue has occurred.
Additionally, companies must rigorously assess whether consent has been lawfully obtained. Vague or pre-ticked consent forms do not suffice under GDPR. Consent must be freely given, specific, informed and unambiguous. In practice, this may mean redesigning user interfaces, updating privacy policies, and launching public awareness campaigns.
Ethical considerations beyond the law
While GDPR provides a legal framework, ethical considerations often extend further. Just because a business can profile users or deploy automated systems within the bounds of the law does not mean it should. Issues such as algorithmic bias and lack of inclusivity can result in discriminatory outcomes, even when compliance checkboxes are ticked.
Consider facial recognition technology, which has been found to perform poorly on individuals with darker skin tones. If such a technology is used to profile users or assess their suitability for certain roles or services, it could lead to grossly unfair outcomes. Similarly, predictive policing tools that evaluate the likelihood of individuals committing crimes in the future risk perpetuating biases endemic in historical crime data.
Given the potentially profound societal consequences of profiling and automation, some companies are embracing ethical AI principles. These often include fairness, accountability, transparency, and human-centricity. When implemented correctly, such values not only mitigate reputational risk but can also become competitive differentiators in a privacy-conscious marketplace.
Impact across sectors
The implications of GDPR for profiling and automation are particularly evident in sectors that heavily rely on personal data. In finance, for example, credit scoring models must be designed to allow individuals to understand why they were approved or denied. Banks must ensure that these decisions are not influenced by protected characteristics such as gender or race.
In recruitment, automated candidate screening tools are now under tighter scrutiny. CV parsing tools and candidate ranking AI must not only secure candidate data but also ensure decisions are made fairly and can be audited. Some tech companies have faced backlash for using training data that inadvertently led to gender discrimination in hiring algorithms.
Healthcare is another area where profiling is common, from predictive diagnostics to personalised treatment recommendations. However, the sensitive nature of health data makes the stakes even higher. In this context, GDPR’s requirement for explicit consent and strong security safeguards takes on heightened importance.
Even smaller-scale or seemingly innocuous applications such as e-commerce recommendation engines must be attuned to these legal boundaries. While such features add value for users, businesses must be upfront about how and why suggestions are made and ensure that data minimisation principles are respected.
Opportunities for innovation
While GDPR undoubtedly introduces constraints, it also acts as a catalyst for innovation. The need to provide interpretability has driven interest in Explainable AI (XAI), a field focused on making machine learning models more transparent. At the same time, Privacy-Enhancing Technologies (PETs) such as federated learning and differential privacy are gaining traction as ways to use information responsibly without compromising individuals’ data.
Organisations that treat GDPR as more than a compliance exercise often uncover new opportunities. Embedding privacy by design and by default into systems from the outset can streamline operations, reduce errors, and foster greater engagement with users. A growing number of consumers now prioritise data ethics when choosing brands, meaning that responsible profiling and decision-making can translate into increased advocacy and loyalty.
Looking to the future
As technology outpaces legislation, GDPR remains a living framework interpreted through evolving case law, regulatory guidance, and technological shifts. Data Protection Authorities (DPAs) across Europe continue to refine their positions on key areas like automated decisions, algorithmic accountability, and the right to explanation.
Looking ahead, ongoing developments in AI governance by institutions such as the EU’s AI Act may intersect with GDPR in significant ways. These newer regulations aim to classify AI systems by risk and impose additional obligations on developers and deployers. Combined, they create a layered regulatory environment that will increasingly shape how organisations build and deploy intelligent systems.
In a digital society where data is both a currency and a liability, getting user profiling and automation right requires more than legal awareness. It calls for a cultural shift toward openness, responsibility, and human dignity. By navigating this space thoughtfully, organisations can unlock the promise of data-driven technologies while upholding the rights and values essential to a free and fair society.