ALGORITHMIC WORKPLACE GOVERNANCE

1. Introduction

Algorithmic workplace governance refers to the use of algorithms, Artificial Intelligence (AI), automated decision-making systems, and data analytics to manage employees and regulate workplace relationships. These systems may be used in recruitment, employee monitoring, task allocation, performance evaluation, scheduling, promotion, disciplinary action, wage calculation, and termination.

Algorithmic governance changes the traditional model of workplace management because decisions that were previously taken by managers or supervisors may increasingly be made or influenced by computer-based systems.

The principal legal concerns include transparency, discrimination, privacy, accountability, human oversight, due process, and protection of workers' collective rights.

2. Meaning of Algorithmic Workplace Governance

Algorithmic workplace governance may be defined as the systematic use of computational rules, algorithms, artificial intelligence, and employee data to make, recommend, or implement decisions concerning workers and employment relationships.

Examples include:

AI-based recruitment screening;

Automated shift allocation;

Algorithmic performance scoring;

Employee productivity monitoring;

Automated attendance systems;

Predictive disciplinary systems;

Automated termination or deactivation;

Algorithmic wage and incentive calculations;

Workforce forecasting; and

AI-assisted promotion decisions.

Thus, an algorithm can operate as a form of managerial authority rather than merely as a technological tool.

3. Major Features

A. Automated Decision-Making

Algorithms may make or substantially influence decisions affecting employees. For example, a system may classify an employee as a low performer and recommend disciplinary action.

Where automated processing produces significant consequences, legal requirements concerning transparency, review, and safeguards may become applicable.

B. Continuous Employee Monitoring

Algorithmic systems can collect information concerning:

Working hours;

Location;

Productivity;

Attendance;

Customer ratings;

Communication patterns; and

Behavioural indicators.

Excessive monitoring may raise questions of privacy, proportionality, and lawful data processing.

C. Algorithmic Performance Management

Employers may use algorithms to calculate performance scores for determining:

Bonuses;

Promotions;

Work allocation;

Disciplinary action; and

Continued employment.

A major legal issue arises where workers do not understand how their scores are calculated or cannot challenge an inaccurate score.

D. Algorithmic Control

Platform businesses may exercise managerial control through algorithms involving:

Task allocation;

Pricing;

Worker ratings;

Incentives;

Penalties;

Fraud detection; and

Account suspension.

Therefore, managerial control can exist even where there is no traditional human supervisor.

4. Algorithmic Workplace Governance and Discrimination

Algorithms can reproduce discrimination contained in historical data or create discriminatory outcomes through apparently neutral criteria.

Discrimination may arise through:

Biased training data;

Historical employment patterns;

Proxy variables;

Inaccurate datasets;

Inappropriate performance indicators; and

Failure to consider individual circumstances.

Case Law: Filcams CGIL Bologna and Others v. Deliveroo Italia Srl

The Bologna Labour Court examined Deliveroo's algorithmic system for allocating delivery shifts. The system relied upon criteria concerning worker participation and reliability.

The court found that the apparently neutral system could produce discriminatory consequences because it did not sufficiently distinguish between different reasons for workers' non-participation.

Legal Principle: An algorithm that appears neutral may nevertheless create indirect discrimination when it fails to take legally relevant circumstances into account.

5. Algorithmic Transparency

Transparency is a fundamental requirement of responsible algorithmic governance.

Workers should, where legally applicable, be informed about:

The existence of automated decision-making;

The important factors used by the system;

The significance of the decision;

The consequences for the worker; and

Available procedures for challenging the decision.

Case Law: SCHUFA Holding AG, Case C-634/21

The Court of Justice of the European Union considered automated scoring under the GDPR. The case concerned the legal significance of automated probability calculations used in decision-making.

Legal Principle: Automated scoring can fall within legal protections concerning automated individual decision-making where it substantially influences decisions affecting an individual.

6. Meaningful Explanation of Algorithmic Decisions

Algorithmic systems should not operate as completely unexplained "black boxes."

Case Law: SCHUFA, Case C-203/22

The CJEU examined the meaning of the right to receive meaningful information about the logic involved in automated decision-making.

The decision emphasised that information concerning algorithmic logic must be sufficiently meaningful to allow the individual to understand the relevant processing and exercise legal rights.

Legal Principle: Algorithmic transparency requires meaningful information rather than merely informing a worker that an algorithm was used.

7. Human Oversight

Human oversight is particularly important when an algorithm makes decisions concerning employment status, disciplinary action, or access to work.

A human reviewer should have genuine authority to:

Examine the algorithmic decision;

Consider additional information;

Correct an erroneous decision; and

Reverse the automated outcome where appropriate.

Case Law: Uber – Amsterdam Court of Appeal

The Amsterdam Court of Appeal considered automated processes used by Uber, including fraud detection and account deactivation.

The litigation raised important questions concerning automated decision-making and the adequacy of human intervention.

Legal Principle: Merely formal or symbolic human involvement may not necessarily constitute meaningful human oversight.

8. Algorithmic Termination and Deactivation

One of the most serious issues arises where algorithms determine whether a worker should continue working.

For example:

Algorithmic monitoring → Low score → Fraud prediction → Automatic suspension → Loss of work

Such systems can have substantial consequences for workers.

The law may therefore require consideration of:

Procedural fairness;

Explanation of the decision;

Accuracy of data;

Human review;

Opportunity to challenge;

Non-discrimination; and

Applicable employment protections.

9. Algorithmic Workplace Governance and Privacy

Algorithmic management frequently depends upon extensive employee data.

Information may include:

Location data;

Attendance records;

Biometric information;

Productivity data;

Communication records;

Behavioural information; and

Performance information.

The collection and processing of such information can create significant privacy concerns.

Employers should therefore consider:

Lawful processing;

Purpose limitation;

Data minimisation;

Accuracy;

Security;

Transparency; and

Appropriate retention periods.

10. Algorithmic Governance and Trade Unions

Algorithmic workplace systems may also affect freedom of association and collective labour rights.

For example, an algorithm could monitor or classify:

Union participation;

Collective activity;

Strike participation;

Worker organising; and

Collective workplace actions.

An algorithm should not treat legally protected collective activity as ordinary misconduct or poor performance.

Algorithmic systems should therefore respect:

Freedom of association;

Collective bargaining;

Trade-union rights;

Lawful industrial action; and

Protection against anti-union discrimination.

11. Important Case Laws

1. SCHUFA Holding AG, Case C-634/21

Principle: Automated scoring can constitute legally significant automated decision-making where it substantially influences decisions concerning an individual.

2. SCHUFA, Case C-203/22

Principle: Individuals may require meaningful information concerning the logic involved in relevant automated decision-making.

3. Filcams CGIL Bologna and Others v. Deliveroo Italia Srl

Principle: A seemingly neutral algorithm may create indirect discrimination when it fails to consider legally relevant reasons for worker behaviour.

4. Uber – Amsterdam Court of Appeal

Principle: Algorithmic systems concerning fraud detection and account deactivation may raise questions of automated decision-making, transparency, and meaningful human intervention.

5. Uber – French Cour de Cassation, 25 January 2023

The French Cour de Cassation examined the employment relationship between Uber and a driver.

Principle: The technological structure of a platform does not by itself remove traditional employment-law questions concerning managerial control and the legal nature of the working relationship.

6. Ola – Amsterdam District Court

The litigation concerning Ola drivers involved questions concerning access to information about algorithmic processing.

Principle: Algorithmic management can create important questions concerning workers' access to information and the ability to understand and challenge automated systems.

12. Principles of Good Algorithmic Workplace Governance

A responsible algorithmic workplace governance framework should include the following principles:

1. Human Oversight

Important employment decisions should receive genuine human review.

2. Transparency

Workers should receive understandable information concerning significant algorithmic decisions.

3. Non-Discrimination

Algorithms should be regularly examined for discriminatory effects.

4. Data Protection

Employers should collect and process only appropriate and legally permissible employee information.

5. Accuracy

Workers should have mechanisms for correcting inaccurate data.

6. Right to Challenge

Employees should have an effective procedure for challenging significant algorithmic decisions.

7. Accountability

Employers should remain legally accountable for workplace decisions made through systems they deploy.

8. Auditability

Important algorithmic systems should be documented and periodically audited.

9. Protection of Collective Rights

Algorithmic systems should not undermine trade-union rights or lawful collective activity.

10. Proportionality

Employee monitoring and automated decision-making should be proportionate to legitimate workplace objectives.

13. Conclusion

Algorithmic workplace governance represents a major transformation in the organisation and management of employment. Algorithms can improve efficiency, workforce planning, performance measurement, and administrative decision-making. However, their use also creates significant legal issues concerning discrimination, privacy, transparency, accountability, due process, and workers' collective rights.

The emerging case law demonstrates that algorithmic decisions cannot necessarily be treated as purely technical decisions. Where algorithms substantially influence employment relationships, existing principles of employment law, equality law, privacy law, and procedural fairness may become relevant.

Therefore, effective algorithmic workplace governance requires a balance between technological efficiency and protection of workers' legal rights. Human oversight, transparency, non-discrimination, accountability, data protection, and an effective right to challenge automated decisions are essential elements of a legally responsible algorithmic workplace.