A One-Day CEMES-FIDMA Research Network Workshop on the Legal, Political and Intellectual Transformations of Market

Brief overview:

• Activity: Half-day interdisciplinary European research network workshop with researchers from Malmö University, Copenhagen University and FIDMA Network.
• Date: 4th December 2026, 9.00-15.00
• Venue: Malmö University, Faculty of Culture and Society
• To be a part of this exciting event please send an abstract to behrang.kianzad@mau.se no later than 15th September.

Summary

What does it mean to make digital fairness European? This halfday FIDMA-CEMES research workshop investigates how ideas of digital sovereignty, digital fairness, market order, consumer autonomy and democratic legitimacy have travelled through modern European political, legal and intellectual history and reappear today in EU digital regulation, AI governance and platform markets.
By bringing together researchers from law, history, political economy, philosophy, media studies and European studies, we hope this workshop will reposition digital sovereignty as a problem of modern European transformation rather than as a narrow technology-law issue.
This event is planned as a concentrated half-day research workshop hosted at Malmö University divided on a morning and an afternoon session with lunch break. Cost of travel and lodging will be provided for invited researchers.

Background

Fairness has returned to the centre of European public life. The Digital Markets Act, the Digital Services Act, the Data Act, the AI Act, GDPR and the renewed interest in Article 102 TFEU all mobilise vocabularies of fairness, contestability, transparency, autonomy, dependency and institutional accountability. Yet the meaning of fairness in Europe remains historically layered and politically contested. It cannot be understood only as a compliance term in current digital regulation. It belongs to a longer European story about just price, market order, social conflict, democratic legitimacy, ordoliberalism, consumer citizenship, welfare institutions and the changing relationship between public authority and private power.
This workshop therefore reframes digital fairness as a problem in modern European
studies. It asks how Europe’s intellectual, legal and political traditions have shaped the concept of fair market order, and how these traditions are now being reworked under conditions of platformisation, data extraction and AI-mediated decision-making. The central hypothesis is that contemporary EU digital regulation should be read not only as technical economic regulation, but as a new phase in Europe’s attempt to govern concentrated
private power through public norms of legitimacy.
This approach creates a stronger bridge between European studies and current digital market research. It links the history of European political economy, legal philosophy and market regulation to urgent contemporary questions: Who defines fairness in digital markets? How do European institutions transform moral and political
ideals into administrative rules? How do ideas of autonomy and citizenship survive when markets are organised through data, platform dependency and algorithmic infrastructures? And how does Europe’s regulatory self-understanding travel globally – through imitation, resistance, trade, standard-setting and geopolitical contestation?

Research questions

This workshop will connect scholars working on European legal history, competition and market regulation, political theory, philosophy, digital media, AI governance and global Europe.

1. How do EU digital market and AI laws reshape European ideas of sovereignty, autonomy, democracy, consumer citizenship and institutional legitimacy?
2. How do European fairness norms travel globally through regulation, standard-setting, corporate compliance and contestation by other regions?
3. Which interdisciplinary methods can make the study of digital fairness legible across history, law, politics, philosophy and media studies?
4. What is the interaction between digital sovereignty and digital fairness?

Programme

• Welcome address by CEMES & FIDMA
• Morning Panel: Digital fairness, digital sovereignty and the European regulatory state – DMA, DSA, AI Act, Data Act and GDPR as contemporary market-ordering projects and their interaction with consumer protection and consumer sovereignty
• Afternoon Panel: How to study digital sovereignty and digital fairness across law, history, philosophy, politics and media studies in the interface of law and policy
• Closing Roundtable

To be a part of this exciting event please send an abstract to behrang.kianzad@mau.se no later than 15th September.

Presented by Prof. Jörgen Hettne at the Second Annual Conference on Fairness in Digital Markets and Artificial Intelligence – Synthetic Imaginaries of Digital Fairness

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Presented by Prof. Dr. Mateja Đurović at the Second Annual Conference on Fairness in Digital Markets and Artificial Intelligence – Synthetic Imaginaries of Digital Fairness

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Presented by Teresa Rodríguez de las Heras Ballell at the Second Annual Conference on Fairness in Digital Markets and Artificial Intelligence – Synthetic Imaginaries of Digital Fairness

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Presented by Krzysztof Koźmiński and Jan Rudnicki at the FIDMA Second Annual Conference

Introduction

Artificial intelligence is quietly reshaping modern life. Algorithms decide who gets hired, who qualifies for a loan, what advertisements we see, and even how much we pay for insurance. These systems promise efficiency, speed, and objectivity. Yet behind that promise lies a growing legal and ethical concern: algorithmic discrimination.
Across Europe, courts, regulators, and academics are increasingly confronting a difficult question: what happens when automated systems reproduce or intensify social inequalities?
This issue is no longer theoretical. Real-world examples demonstrate how algorithms can disadvantage vulnerable groups, often in subtle and indirect ways. In response, the European Union is developing new legal tools designed to force organisations to think about fundamental rights before deploying high-risk AI systems.

Employment and Platform Work: When Algorithms Manage Workers

One of the clearest examples of algorithmic discrimination can be seen in platform work.
On-demand companies, such as Deliveroo and Uber, increasingly rely on algorithms to allocate work, evaluate performance, and rank workers. These systems often appear neutral on their face, yet they may disproportionately disadvantage certain groups.
A major example emerged in Italy in 2020 involving the food delivery platform Deliveroo. The company used a reputational ranking algorithm to prioritise riders when assigning work shifts. The algorithm measured “reliability” and “participation” without accounting for legitimate absences such as illness, childcare responsibilities, disability, or strike action.
In practice, this disproportionately harmed female workers, who are statistically more likely to undertake caregiving responsibilities. The Italian court held that the algorithm indirectly discriminated against women because it treated all cancellations identically, regardless of the reason.
This case reveals a key problem with algorithmic systems: even formally neutral criteria can reinforce existing social inequalities.
The EU has responded through the Platform Work Directive 2024/2831, which introduces stronger protections for platform workers. The Directive creates safeguards against harmful algorithmic management, including:

• Human oversight of automated decisions
• Transparency obligations for digital platforms
• Restrictions on certain forms of automated monitoring
• Rights to explanation and review of algorithmic decisions

Still, critics argue that the law does not go far enough, particularly in addressing gendered and intersectional harms.

Banking and Insurance: Credit Scores, Risk, and Invisible Bias

Banking and insurance are increasingly dependent on automated decision-making. AI systems can assess loan applications, calculate creditworthiness, predict financial risk, and determine insurance pricing far faster than human employees. However, efficiency can come at the expense of fairness.
One landmark case in this area is the SCHUFA decision before the Court of Justice of the European Union (CJEU).
SCHUFA, a German credit-scoring company, used automated systems to generate scores predicting whether individuals were likely to repay loans. When a woman was denied a loan based on her score, she requested detailed information about how the score had been calculated. SCHUFA refused to disclose the methodology.
The CJEU ultimately ruled that the scoring system constituted “automated individual decision-making” under Article 22 GDPR because the automated score significantly affected the applicant’s legal and economic position.
Yet transparency alone does not eliminate discrimination.
Many credit-scoring systems rely on variables such as income, employment history, or geographic location. Although these factors may appear neutral, they can indirectly disadvantage women, ethnic minorities, disabled persons, or economically marginalised communities.
This creates a difficult legal challenge because indirect discrimination is often harder to prove and easier to justify than direct discrimination.

Targeted Advertising and Online Retail: The Hidden World of Personalised Discrimination

Modern online platforms collect enormous quantities of personal data. This data allows companies to personalise advertisements, prices, and product recommendations with extraordinary precision. However, personalisation can quickly become discriminatory.
For example, shopping preferences may be used as proxies for gender, race, religion, or age. Certain consumers may see different prices, products, or opportunities based on inferred characteristics rather than explicit choices.
EU anti-discrimination law only partially addresses these risks.
While EU law prohibits discrimination in access to goods and services on grounds such as gender or racial origin, protection remains fragmented. Some forms of discrimination, particularly those based on age or religion in private commercial settings, may fall outside the scope of existing secondary legislation.
Data protection law offers some indirect safeguards.
Meta Platforms Inc and Others v Bundeskartellamt illustrated how GDPR protections can restrict the processing of sensitive personal data used for targeted advertising. The CJEU held that merely visiting websites or interacting with online content does not automatically mean users consent to their sensitive personal data being made public.
Still, GDPR protections have limits.
Users frequently consent to extensive data processing through terms and conditions they never fully read. Moreover, some protected characteristics are not comprehensively covered under GDPR’s rules on sensitive data.
As a result, discriminatory profiling can still occur despite formal consent mechanisms.

The Netherlands and the Rise of AI Accountability

Several major scandals in the Netherlands dramatically accelerated European discussions around algorithmic accountability.

The Childcare Benefits Scandal

Perhaps the most infamous example involved the Dutch childcare benefits system.
Dutch tax authorities used algorithms to detect welfare fraud among recipients of childcare benefits. The system disproportionately targeted individuals with immigrant backgrounds, leading to devastating financial and social consequences for thousands of families, leading to the resignation of the ‘Rutte III’ government

The SyRI Case

Another major controversy involved SyRI (System Risk Indication), a government tool designed to identify potential welfare fraud.
SyRI combined large datasets to flag individuals considered “high risk.” In practice, the system disproportionately targeted low-income and immigrant communities.
The Hague District Court ruled that the system violated the right to private life under Article 8 ECHR, criticising its lack of transparency and disproportionate effects.

The DUO Student Fraud System

A third scandal emerged from the Dutch student finance agency DUO.
The agency used risk-scoring algorithms to identify suspected student benefit fraud. Although the system did not directly use race or ethnicity, it relied heavily on geographic indicators that functioned as proxies for ethnicity.
Dutch authorities later concluded that the system was discriminatory and unlawful.
Together, these scandals demonstrated a recurring pattern:

• Algorithms can replicate structural inequalities
• Seemingly neutral criteria can operate as discriminatory proxies
• Lack of transparency makes accountability difficult
• Vulnerable groups often bear the greatest risks

The FRAIA: A New Model for Preventing Harm

In response to these scandals, the Netherlands developed the Fundamental Rights and Algorithms Impact Assessment (FRAIA).
Rather than focusing only on punishment after harm occurs, the FRAIA is designed as a preventative tool to help organisations identify risks to fundamental rights before deploying algorithmic systems.
The FRAIA asks organisations to evaluate four major areas:

1. Why is the algorithm being used?

This stage examines:

• The problem the algorithm aims to solve
• Whether non-digital alternatives exist
• The public values involved
• The legal basis for using the system
• Responsibility and accountability structures

2. What does the algorithm do?

This stage focuses on:

• Data quality
• Embedded biases
• Security risks
• Transparency and explainability
• Ownership and control of the system

3. How will the algorithm be implemented?

This section considers:

• Human oversight
• Context-specific risks
• Communication with affected individuals
• Auditing and review mechanisms
• Risks of stigmatisation and discrimination

4. Which fundamental rights are affected?

The FRAIA requires organisations to assess how algorithms may interfere with rights such as:

• Equality and non-discrimination
• Privacy and data protection
• Freedom of expression
• Procedural fairness

Importantly, the FRAIA openly acknowledges that many algorithms inherently produce unequal treatment because they rely on categorisation and profiling.
The framework therefore requires organisations to weigh the seriousness of rights infringements against the objectives pursued.

The EU AI Act and the FRIA

The European Union has now taken a major step toward institutionalising this approach through the EU AI Act. Article 27 of the AI Act introduces the Fundamental Rights Impact Assessment (FRIA) for certain high-risk AI systems.
This obligation applies not only to public bodies but also to several categories of private actors, including:

• Providers of public services
• Credit institutions using AI for credit scoring
• Insurance providers using AI for risk assessment and pricing

The FRIA represents an important shift in European law.
For years, debates about fundamental rights focused primarily on state action. The AI Act recognises that private corporations deploying AI systems can exercise enormous power over individuals’ lives.
The FRIA therefore reflects a broader trend toward extending fundamental rights obligations into private contractual relationships.

Fundamental Rights Beyond the State

One of the most significant legal developments underlying these changes is the growing recognition that fundamental rights can apply horizontally between private parties.
Historically, human rights protections primarily regulated relationships between individuals and the state. However, modern private corporations, especially digital platforms and AI developers, now wield immense influence over employment, access to credit, insurance, housing, and information.
The CJEU’s decisions in cases such as Egenberger and Bauer confirmed that certain provisions of the EU Charter can create direct obligations for private actors.
This matters enormously in the AI context. If private companies deploy AI systems that discriminate against individuals in areas regulated by EU law, fundamental rights protections may apply directly to those contractual relationships.
The AI Act reinforces this development by requiring certain private actors to proactively assess the impact of AI systems on fundamental rights.

Can Impact Assessments Really Prevent Discrimination?

Despite these legal developments, important concerns remain.
Impact assessments such as the FRAIA and FRIA are ultimately risk-management tools. They do not automatically eliminate discrimination.
Much depends on:

• How honestly organisations conduct assessments
• The quality of oversight and enforcement
• The transparency of AI systems
• The willingness of courts and regulators to intervene

There is also a structural problem within anti-discrimination law itself.
Direct discrimination is generally prohibited outright. Indirect discrimination, however, can often be justified if organisations can show a legitimate aim and proportionate means. Because many algorithmic harms take the form of indirect discrimination, companies may still defend systems that produce unequal outcomes.
Most victims of discrimination never bring legal claims. This makes prevention especially important.

Final Thoughts

Artificial intelligence is transforming contractual relationships across employment, finance, insurance, and digital commerce. The benefits are undeniable, but so are the risks.
The rise of algorithmic discrimination demonstrates that technology is never truly neutral. Algorithms reflect the data, assumptions, and institutional priorities embedded within them. The Dutch FRAIA and the EU’s FRIA under the AI Act represent important attempts to shift the focus from reacting to discrimination after harm occurs toward identifying risks before systems are deployed. Whether these frameworks succeed will depend on meaningful enforcement, transparency, and judicial oversight.
As AI systems become more deeply integrated into everyday life, the central legal challenge will not simply be whether algorithms are efficient. It will be the extent to which they remain compatible with equality, accountability, and fundamental rights in a democratic society.

Presented by Rayshan Dewnarain, Lilia Belyashka, and Aurelia Colombi Ciacchi at the FIDMA Second Annual Conference

We are pleased to announce the Second Annual Online Conference on Fairness in Digital Markets and Artificial Intelligence to be held online on 15 May 2026.

To register, please fill in our form below.

You can find the conference details in the attachment below.

FIDMA Second Online Conference on Fairness in Digital Markets and Artificial Intelligence

Conference Programme

09:30–9:45 Welcoming Address

Teresa Rodríguez de las Heras Ballell, President of the European Law Institute will mark ELI’s 15th Anniversary as part of the ELI in the World series

9:45–10:00 Introduction

Fabrizio Esposito, Behrang Kianzad, Emilia Mišćenić, Nada Ina Pauer, Contributing to Digital Fairness with Synthetic Imagination

10:00-11:00 Panel 1 – Fairness & Fundamental Rights (moderator Emilia Mišćenić)

Petra Weingerl, The Notion of Consumer in EU Law: Between Traditional Concepts and Digital Realities

Jess Haynie-Lavelle, Ethical Challenges of Artificial Intelligence (AI)

Andrés Chomczyk Penedo, A New Imaginary for Fairness? How the Common European Data Spaces Bridge Between Human-centric Protection and a Competitive Economy

11:00 – 11:10 Break

11:10-12:30 Panel 2 – AI & Dark Patterns (moderator Fabrizio Esposito)

Aytaj Almammadova, What is the “Meaning” of Meaningful Information? Crucial Fairness Criteria in AI Decision-Making

Roberta Montinaro, AI Chatbots, Digital Addiction, and Emotional Dependence: A Fairness Analysis under EU Law

Monica Schellemans, The Consumer and Creator Implications of Fairness in Music Recommender Systems

Sarah de Heer, Who Needs to Know? Transparency in Medical AI for Patients, Physicians, and Supervisory Authorities

12:30 – 13:00 Break

13:00 – 14:00 Panel 3 – Data Protection & Online Platforms (moderator Behrang Kianzad)

Leonardo Lazzaro, Beyond Consent: Rethinking Digital Fairness in the Age of Generative AI

Miłosz Malaga, From Fairness to Effectiveness: The DMA’s Rights-Centred Architecture

Maura Mattalia and Matteo Pignatti, Digital Sovereignty and the Euro: Legal Frameworks for State Control over Digital Service Providers in the European Union

14:00 – 14:15 Break

14:15 – 15:15 Panel 4 – Enforcement (moderator Nada Ina Pauer)

Laura Zoboli and Joanna Mazur, Enforcement Design in EU Digital Regulation: Lessons for the Digital Fairness Act

Viktória Harsági, The Role of Algorithms in Enforcing Claims in Civil Justice – AI in Decision-Making Process and Beyond

Talia Schwartz-Maor Berkley/Haifa, GenAI as an Inclusion Mechanism in Judicial Decision-Making

15:15-15:30 Closing Remarks

The Organising Committee

Emilia Mišćenić (University of Rijeka) • Fabrizio Esposito (NOVA School of Law and CEDIS) • Behrang Kianzad (Malmö University) • Nada Ina Pauer (Max Planck Institute for Innovation and Competition)

The conference is supported by Institute for Global Political Studies, Malmö University; Nova School of Law; European Law Institute Croatian Hub