Civil Law and Autonomous Management System Employment Liability in Europe

1. Introduction

Autonomous management system employment liability concerns situations where an employer uses AI, algorithms, automated decision-making, predictive analytics, or other autonomous management technologies to make or substantially influence employment decisions.

Examples include systems that autonomously:

allocate work;

determine working schedules;

calculate performance scores;

monitor employees;

determine remuneration;

recommend or initiate disciplinary action;

identify alleged misconduct;

decide whether a worker should receive assignments;

rank employees;

assess productivity;

recommend dismissal;

deactivate a worker's account;

predict employee turnover or reliability.

European law does not generally treat the algorithm as an independent legal employer. Responsibility normally remains with the employer, platform, controller, or other legally identifiable entity.

Recent European case law shows that courts are particularly concerned with human control, transparency, discrimination, privacy, employment status, procedural fairness and causation. A 2025 cross-European review identified 22 judgments and nine administrative decisions concerning algorithmic management and employment/privacy issues. (SSRN)

2. Meaning of Autonomous Management System

An autonomous management system can be understood as:

A technological system that independently performs or substantially influences functions traditionally performed by managers or supervisors.

It may use:

artificial intelligence;

machine learning;

automated scoring;

predictive analytics;

biometric monitoring;

geolocation;

productivity data;

employee communications;

attendance records;

customer ratings;

behavioural data.

Example

An employer operates a warehouse.

An AI system:

monitors each worker;

calculates productivity;

compares workers;

allocates future shifts;

issues warnings;

recommends termination.

If the system incorrectly identifies an employee as underperforming and the employee loses employment, several civil and labour-law questions arise.

3. Central Legal Principle

The most important principle is:

Automation of management does not automatically transfer legal responsibility from the employer to the algorithm.

The employer normally remains responsible for ensuring that its management system complies with:

employment law;

anti-discrimination law;

data-protection law;

contractual duties;

occupational-health and safety obligations;

collective-labour rights;

procedural requirements.

The EU's Platform Work Directive specifically addresses automated monitoring and decision-making, including decisions affecting access to work, earnings, working time, safety and health, promotion and contractual status. (BOE)

4. Main Categories of Employment Liability

Autonomous management can generate liability through several routes.

A. Wrongful dismissal

An automated system incorrectly identifies misconduct and contributes to termination.

B. Discrimination

An algorithm systematically disadvantages:

women;

older workers;

disabled workers;

workers taking protected leave;

trade-union members;

workers exercising labour rights.

C. Privacy violation

The employer collects excessive:

location data;

biometric data;

communications;

behavioural information.

D. Wage and remuneration disputes

An algorithm incorrectly calculates:

working time;

performance pay;

bonuses;

commissions;

deductions.

E. Working-time violations

Automated scheduling may create:

excessive working hours;

inadequate rest;

unpredictable schedules.

F. Occupational safety

Algorithmic productivity targets may encourage unsafe work.

G. Employment-status disputes

A company may describe workers as independent contractors while an algorithm effectively controls their work.

H. Procedural unfairness

The employee may receive a decision without understanding:

why it was made;

what data was used;

whether a human reviewed it;

how to challenge it.

5. Case Law 1 — Uber BV and Others v Aslam

UK Supreme Court, [2021] UKSC 5

Although the United Kingdom is no longer an EU Member State, this is an important European comparative authority on algorithmic management.

The dispute concerned Uber drivers and their employment status.

The Supreme Court considered the practical reality of the relationship rather than simply accepting the contractual description used by Uber.

Principle

The existence of technological mediation does not prevent a court from examining the real degree of control and subordination exercised by the enterprise.

The system's:

pricing;

allocation arrangements;

contractual structure;

monitoring;

performance mechanisms

were relevant to understanding the actual relationship.

Relevance

An employer cannot necessarily avoid employment obligations merely by saying:

“The algorithm manages the workers, not the company.”

The algorithm can be evidence of the company's organisational control.

6. Case Law 2 — Glovo / Spanish Supreme Court

Spanish Supreme Court, Social Chamber, Judgment No. 805/2020, 25 September 2020, ECLI:ES:TS:2020:2924

This is one of Europe's most important cases involving algorithmic management.

The claimant worked as a Glovo delivery rider.

The platform used an algorithm to allocate orders and its evaluation system affected access to work. The Supreme Court examined the actual organisational relationship between the rider and Glovo. (BOE)

Principle

The technological platform did not eliminate the existence of organisational control.

The Court found that the rider did not independently organise the productive activity, negotiate relevant conditions with establishments, or freely determine the economic structure of the service.

Autonomous-management significance

This case establishes an important concept:

Algorithmic control can constitute evidence of employer-like control.

Therefore, an autonomous management system may actually strengthen, rather than eliminate, the argument that the enterprise exercises managerial authority.

7. Case Law 3 — Deliveroo Italy / Bologna Court

Tribunale Ordinario di Bologna, Labour Division, Order of 31 December 2020

This case concerned Deliveroo's "Frank" algorithm.

The algorithm ranked riders according to factors including:

reliability;

participation;

attendance at booked work sessions.

Workers who failed to participate could receive lower scores and consequently reduced access to future work opportunities.

The problem was that the system did not adequately distinguish ordinary absence from legitimate reasons such as:

strikes;

illness;

disability;

family-care circumstances.

The Bologna court found the system discriminatory. (Cambridge University Press)

Principle

A technologically neutral rule can produce indirect discrimination.

The algorithm does not become legally neutral merely because it applies the same mathematical formula to everybody.

Important formula

Neutral algorithm + discriminatory effect = potential indirect discrimination

This is highly relevant to autonomous employment systems.

8. Case Law 4 — Uber Drivers v Uber BV, Amsterdam Court of Appeal

Gerechtshof Amsterdam, 4 April 2023

The Amsterdam Court of Appeal considered several cases involving Uber drivers and automated decision-making.

The disputes concerned:

account deactivation;

ride allocation;

pricing;

driver ratings;

fraud-probability scores;

information concerning automated decisions.

The court found that several processes constituted automated decision-making of substantial importance to the drivers and ordered Uber to provide relevant information concerning the factors underlying decisions. (Rechtspraak)

Particularly important principle

Human involvement cannot necessarily be treated as sufficient merely because a human technically appears somewhere in the process.

The case raised the question whether human intervention was meaningful rather than merely symbolic. (DOI)

Employment relevance

Suppose:

AI recommends termination → manager clicks "approve."

A court may need to examine whether the manager genuinely reviewed the case or simply rubber-stamped the algorithm.

9. Case Law 5 — SCHUFA, C-634/21

CJEU, Case C-634/21, 7 December 2023

This was not an employment case, but it is highly relevant by analogy.

The CJEU interpreted Article 22 GDPR, concerning automated individual decision-making.

It held that automated establishment of a probability value could itself constitute an automated individual decision where a third party substantially relies upon that value in establishing, implementing or terminating a contractual relationship. (Eur-Lex)

Employment application

Suppose an employer uses an AI system to calculate:

“Employee termination probability = 92%.”

The manager then automatically follows that score.

The SCHUFA reasoning demonstrates why courts may look beyond the final human signature and examine the actual technological decision-making process.

Key principle

Human approval does not necessarily eliminate the legal significance of automated decision-making.

10. Case Law 6 — Bărbulescu v Romania

ECtHR Grand Chamber, Application No. 61496/08, 5 September 2017

This case concerned workplace monitoring of employee communications.

The Grand Chamber examined the employee's privacy rights under Article 8 ECHR and the employer's interests in monitoring workplace communications. (vLex)

Principle

Workplace monitoring must be accompanied by appropriate safeguards and a proper balancing of competing interests.

Relevant considerations include:

whether the employee was informed;

the extent of monitoring;

the degree of intrusion;

legitimate reasons for monitoring;

consequences for the employee;

whether less intrusive methods were available.

Autonomous management relevance

AI management systems can monitor:

keystrokes;

location;

communications;

productivity;

facial expressions;

physical movements;

behavioural patterns.

Therefore, Bărbulescu provides an important foundation for analysing automated employee surveillance.

11. Case Law 7 — López Ribalda and Others v Spain

ECtHR Grand Chamber, Applications Nos. 1874/13 and 8567/13, 17 October 2019

This case involved covert video surveillance of employees in a supermarket.

The Grand Chamber considered the compatibility of workplace surveillance with Article 8 ECHR. (app.lexploria.com)

Principle

The legality of employee monitoring requires consideration of proportionality and the circumstances surrounding the surveillance.

Autonomous-management relevance

Modern AI management may transform ordinary cameras into:

facial-recognition systems;

behaviour-analysis systems;

productivity-monitoring systems;

emotion-recognition systems.

Thus, a simple CCTV system can become a much more intrusive autonomous management technology when combined with AI analytics.

12. Case Law 8 — Uber/Ola Automated Decision Cases

The Amsterdam Court of Appeal's 2023 judgments concerning Uber and Ola are particularly important because they address the intersection of:

employment + automated decision-making + GDPR + transparency.

The court considered automated:

ride allocation;

price calculation;

driver ratings;

fraud scores;

account deactivation.

The decisions affected drivers' ability to earn income and continue using the platforms. (Rechtspraak)

The cases demonstrate that algorithmic management can affect not merely privacy but the economic existence of the worker.

13. Employer's Duty of Human Oversight

Modern European regulation increasingly emphasises meaningful human oversight.

The 2024 Platform Work Directive requires specific safeguards around automated monitoring and decision-making. It includes requirements concerning information, human oversight and review of significant decisions. (BOE)

For serious decisions, the basic concept is:

Algorithm → Human review → Reasoned decision → Worker challenge

rather than:

Algorithm → Automatic punishment

14. Automated Dismissal

Dismissal is one of the most legally sensitive applications.

Imagine:

AI detects unusual behaviour → assigns fraud score → automatically terminates worker.

Potential legal questions include:

Was the system accurate?

Was the worker informed?

Was the data lawfully obtained?

Was the decision discriminatory?

Was there meaningful human review?

Could the worker challenge the decision?

Was the contractual/employment procedure followed?

Was the decision based upon protected characteristics?

Was the algorithm sufficiently tested?

Who is legally responsible?

Under the Platform Work Directive, certain significant decisions concerning restriction, suspension or termination of platform workers' accounts must involve human involvement and review mechanisms. (BOE)

15. Automated Performance Evaluation

An employer may use an AI system to calculate:

Employee Performance Score = 74/100

The score might incorporate:

productivity;

attendance;

customer ratings;

speed;

error rate;

working hours.

The legal problem is that a numerical score can conceal important contextual information.

For example:

Employee missed work because of protected medical leave.

The algorithm may record:

“Absence = lower reliability.”

This is similar to the problem identified in the Bologna Deliveroo case, where the ranking system did not sufficiently distinguish legitimate reasons for non-participation. (Cambridge University Press)

16. Algorithmic Discrimination

Autonomous management can generate discrimination through:

Direct discrimination

The system expressly uses a protected characteristic.

Example:

“Reduce promotion probability for women.”

This would raise obvious legal problems.

Indirect discrimination

The system uses apparently neutral criteria that disproportionately disadvantage a protected group.

Example:

Availability every evening = high performance.

That may disadvantage workers with certain protected family or disability-related circumstances.

Proxy discrimination

The algorithm does not use race, sex or disability directly but uses correlated variables.

Example:

postal code → socioeconomic prediction → employment score.

17. Trade-Union Rights

Algorithmic management can affect collective labour rights.

An algorithm may:

identify union participation;

reduce shifts;

predict collective action;

rank workers;

identify "high-risk" workers;

alter work allocation.

The EU's current regulatory framework specifically addresses concerns about automated processing used to predict the exercise of fundamental rights, including freedom of association and collective bargaining. (BOE)

The Deliveroo Bologna case illustrates how an apparently neutral algorithm can interfere with workers exercising collective rights. (Cambridge University Press)

18. Privacy and Employee Data

Autonomous management can process enormous quantities of personal information.

Potential data includes:

GPS location;

biometric information;

communications;

productivity;

health-related information;

behavioural patterns;

customer ratings;

attendance;

workplace interactions.

Article 22 GDPR is especially important where decisions are based solely or substantially upon automated processing.

The CJEU's SCHUFA judgment confirms that Article 22 can apply where automated scoring plays a determinative role in a subsequent contractual decision. (Eur-Lex)

19. Right to Explanation

An employee may reasonably ask:

“Why was I given this score?”

or:

“Why was I removed from the work schedule?”

or:

“Why did the system identify me as fraudulent?”

The Amsterdam Uber cases demonstrate the practical importance of access to information about automated decision-making. (Rechtspraak)

The 2024 Platform Work Directive also establishes information and review mechanisms concerning automated decisions affecting platform workers. (BOE)

20. Trade Secrets vs Worker Rights

Employers may argue:

“The algorithm is a trade secret.”

That does not automatically resolve the worker's rights.

The Amsterdam Uber litigation demonstrates the tension between:

algorithmic transparency;

GDPR rights;

protection of business secrets.

The Amsterdam court required relevant information concerning automated decisions while considering the company's business-secret arguments. (Rechtspraak)

The legal challenge is therefore:

Transparency without unnecessary disclosure of proprietary source code.

A court may focus on meaningful information about:

categories of data;

important factors;

decision logic;

consequences;

reasons for a particular decision.

21. Occupational Health and Safety

Autonomous management can create physical and psychological risks.

Example:

An AI system continuously increases productivity targets.

Workers respond by:

working faster;

taking fewer breaks;

skipping safety procedures.

If injury occurs, potential claims may concern:

employer's duty of care;

workplace safety legislation;

negligence;

contractual employment duties;

algorithmic risk assessment.

Therefore:

An algorithmic management system can itself become part of the workplace risk environment.

22. Algorithmic Workload Allocation

Consider a warehouse system:

AI allocates 150 tasks per worker.

Worker A receives 80 tasks.

Worker B receives 150.

Worker C receives 200.

If the allocation system repeatedly gives excessive workloads to particular employees, the employer may face questions concerning:

discrimination;

equal treatment;

health and safety;

contractual duties;

working-time requirements;

occupational stress.

The relevant issue is not simply:

“Was the algorithm technically functioning?”

but:

“Was the employer's use of the algorithm legally permissible?”

23. Employer Liability for Algorithmic Errors

A useful civil-law framework is:

Stage 1 — System

Identify the algorithm or autonomous management system.

Stage 2 — Function

Determine what it was designed to do.

Stage 3 — Decision

Identify what decision it made or influenced.

Stage 4 — Human role

Determine whether a person actually reviewed the decision.

Stage 5 — Legal duty

Identify the applicable employment/data-protection/anti-discrimination duty.

Stage 6 — Error or unlawful conduct

Determine whether the system:

produced inaccurate information;

discriminated;

violated privacy;

breached contractual obligations;

created unsafe conditions.

Stage 7 — Causation

Connect the system's operation to the employee's loss.

Stage 8 — Damage

Determine:

lost wages;

lost employment;

reputational harm;

discrimination damage;

privacy harm;

other recoverable losses.

24. Causation in Autonomous Employment Systems

Consider:

Incorrect data → AI score → manager decision → dismissal → financial loss

The employee may need to demonstrate:

incorrect data existed;

the algorithm relied upon it;

the algorithm generated the relevant output;

the employer relied upon the output;

the decision caused the employment loss;

the loss is legally recoverable.

This creates a multi-stage technological causation chain.

25. Who Is Responsible?

Possible responsible parties include:

But responsibility is fact-specific.

The mere fact that an external vendor created the algorithm does not automatically transfer the employer's employment-law obligations to that vendor.

26. Autonomous System and Employment Contract

An employment contract creates reciprocal obligations.

Employer obligations may include:

remuneration;

lawful management;

non-discrimination;

health and safety;

privacy;

good faith;

procedural fairness where required.

Employee obligations may include:

performing work;

following lawful instructions;

protecting confidential information;

complying with safety procedures.

If an autonomous system imposes an instruction, the employer may still be responsible for determining whether that instruction is lawful.

27. Platform Work Directive

Directive (EU) 2024/2831 is especially important for algorithmic management.

It defines automated monitoring and automated decision-making systems and covers systems affecting matters such as:

recruitment;

access to assignments;

organisation of work;

earnings;

safety and health;

working time;

promotion;

contractual status;

account restriction or termination. (BOE)

The Directive therefore provides an important European framework for human oversight and accountability in algorithmic management.

28. Difference Between Traditional and Autonomous Management

But legally:

Changing the managerial instrument does not necessarily change the underlying employer's legal obligations.

29. Important Case-Law Principles

30. Key Legal Formula

A useful examination formula is:

Autonomous Management Liability =

Automated System + Employment Duty + Unlawful/Defective Decision + Causation + Worker Damage + Responsible Employer/Actor

For discrimination:

Algorithm + Neutral Criterion + Disparate Effect + Protected Ground + Insufficient Justification = Potential Discrimination

For privacy:

Employee Data + Monitoring/Profiling + Insufficient Legal Basis/Safeguards + Interference = Potential Data-Protection Liability

For dismissal:

Automated Assessment → Employment Decision → Human Review → Procedural Compliance → Causation → Remedy

31. Practical Example

Suppose a European company uses an AI HR system.

The system analyses:

productivity;

attendance;

customer feedback;

working speed;

overtime;

employee communications.

It gives Employee A a “high termination risk” score.

The company automatically terminates A.

Later it emerges that:

the system incorrectly interpreted approved leave as absenteeism;

the score was disproportionately affecting a particular group;

no meaningful human review occurred;

the employee was not told why the decision was made.

Potential legal issues include:

employment-law violation;

discrimination;

GDPR automated decision-making;

inaccurate personal data;

inadequate human review;

procedural unfairness;

wrongful termination;

compensation.

The relevant reasoning can draw on SCHUFA, Deliveroo, the Amsterdam Uber cases, Bărbulescu and López Ribalda.

32. Future Development

European employment law is moving toward a model in which autonomous management must satisfy:

Transparency + Human Oversight + Accountability + Non-discrimination + Data Protection + Worker Participation

The European Parliament has also called for meaningful human oversight, explanations of algorithm-supported employment decisions and the possibility of human review. (Eur-Lex)

This is particularly important as autonomous management moves beyond platforms into:

factories;

warehouses;

offices;

hospitals;

logistics;

financial institutions;

call centres;

professional services.

33. Conclusion

Autonomous management system employment liability in Europe arises when an employer delegates managerial functions to algorithms or AI and those systems affect workers' employment rights.

The central legal lesson from European case law is:

The use of an algorithm does not remove the employer's legal responsibility.

The most important questions are:

Who controls the system?

What employment decision did it make?

What data did it use?

Was the data accurate and lawfully obtained?

Was the decision automated?

Was there meaningful human intervention?

Did the system produce discriminatory effects?

Did it interfere disproportionately with privacy?

Did it affect employment status, pay, working time or dismissal?

Can the worker obtain an explanation or review?

Did the decision cause legally recoverable loss?

Which employer, platform, developer or other actor bears the relevant legal responsibility?

Exam-ready conclusion

In European civil and employment law, autonomous management systems do not possess independent legal responsibility merely because they make or support employment decisions. Liability generally remains attributable to the employer or other legally responsible entity and is assessed through employment contracts, labour law, anti-discrimination rules, data protection, privacy, occupational safety and civil remedies. The cases of Glovo, Deliveroo, Uber, SCHUFA, Bărbulescu and López Ribalda demonstrate that algorithmic control must remain subject to legal accountability, meaningful human oversight, transparency and protection of workers' fundamental rights.