Algorithmic management in HR: what it is and how to govern AI-assisted decisions

Categoria: Decisão | Liderança | IA

Reading time: 8 minutes

Algorithmic management in HR is not a distant trend. It already exists in organisations that use systems to screen applications, allocate shifts, monitor productivity, recommend training, identify turnover risk or support performance assessments.

The problem is not the use of technology to organise information or reduce administrative work. The problem begins when a system starts influencing decisions about people without the organisation knowing exactly how it works, what data it uses, what impacts it may create and who is accountable when it fails.

The question is therefore no longer “does our company use artificial intelligence in HR?”. A more useful question is: in which decisions are we allowing automated systems to influence people, work and opportunities?

What is algorithmic management?

Algorithmic management is the use of software, data and automated models to support or carry out tasks traditionally performed by managers: directing work, allocating tasks, monitoring activity, assessing performance, recommending actions or influencing decisions about people.

A machine does not need to decide on its own whom to hire or dismiss for algorithmic management to exist. It is enough for the system to materially change how work is organised, observed, assessed or rewarded.

An HR platform is not automatically a form of algorithmic management. A system becomes more relevant when it ceases to be merely an administrative record and starts producing ratings, priorities, predictions or recommendations that influence human decisions.

In their research on algorithmic control at work, Kellogg, Valentine and Christin identify six recurring mechanisms:

• Restrict: limit the options available to employees or managers.
• Recommend: suggest tasks, actions, routes, priorities or behaviours.
• Record: continuously collect data on activity, time, location or performance.
• Rate: assign scores, rankings, risk levels or assessments.
• Reward: encourage certain behaviours through incentives, access or visibility.
• Replace: automate or reconfigure decisions previously made by people.

These mechanisms are not necessarily negative. They can increase consistency, reduce administrative workload and enable better-informed decisions. But they change the relationship between data, management and people. That requires governance.

Where algorithmic management may already be present

• Recruitment: CV screening, candidate ranking, skills analysis, targeted job advertisements or automated interview tools.
• Performance: productivity dashboards, activity alerts, results ratings, analysis of work patterns or identification of deviations.
• Shifts and tasks: automatic schedule allocation and distribution of work based on availability, previous performance or individual characteristics.
• Retention: models that flag people with a higher likelihood of leaving or lower levels of engagement.
• Development: recommendations for training, learning pathways or internal mobility based on profile and usage data.

The relevant point is not only which tool is being used. It is the specific purpose for which it is used. A dashboard may simply support analysis. But, in practice, it can also become a mechanism that affects assessments, promotions or the distribution of opportunities.

What recent research shows

As part of my PhD in Management, I conducted a bibliometric and systematic review on algorithmic management and AI ethics with Helena Oliveira. The analysis began with 449 review articles identified in the Scopus and Web of Science databases and resulted in a final corpus of 313 reviews published between 2020 and 2025.

The central pattern is not that technology is inherently fair or unfair. It is that algorithmic management creates new tensions between efficiency, autonomy, transparency, organisational justice and accountability.

The literature is consistent on one point: abstract ethical principles are not enough. Transparency, fairness, human oversight and data protection must be translated into concrete processes, clear responsibilities and a genuine ability to question decisions.

The risk does not lie in using algorithms to support management. It lies in using algorithms without knowing which decisions they influence, which criteria they reinforce and who can challenge the outcome.

Four risks that HR should not ignore

1. Mistaking available data for actual performance

Systems assess what they can observe and measure. But what is measurable does not necessarily correspond to what is most valuable.

Speed, the number of completed tasks, response time or digital presence may be useful indicators. But they do not automatically capture the quality of relationships, collaboration, judgement, creativity, the ability to anticipate problems or contribution to how the team functions.

When an indicator becomes the dominant criterion, people tend to adapt their behaviour to what the system measures. The organisation may then begin optimising visible signals instead of improving relevant outcomes.

2. Reproducing bias through historical data and criteria

An automated system does not create bias in a vacuum. But it can reproduce or amplify existing patterns when it learns from historical data, inappropriate proxies or criteria that have never been questioned.

For example, a model trained on past recruitment decisions may learn patterns that reflect previous preferences, inequalities or exclusions. A seemingly neutral rule may disadvantage particular groups if the data used reflect unequal starting opportunities.

The risk is not inevitable. But it requires the organisation to ask: what data feed this system, which variables influence the outcome and who checks for unequal impacts?

3. Creating opacity where explanation is needed

A person does not need to understand the complete code of an algorithm to know how they are being assessed. But they should be able to understand, in clear terms, what data are used, for what purpose and how they can challenge a decision that affects them.

Explainability does not mean disclosing trade secrets or oversimplifying a complex technical reality. It means being able to explain, in a relevant way, how the tool contributes to a decision and what safeguards exist when the outcome appears inappropriate.

Without this, management may appear arbitrary even when the intention is to create consistency. When people cannot understand or challenge an outcome, trust quickly erodes.

4. Diluting responsibility between the provider, the system and the manager

When a platform rates a candidate, flags turnover risk or recommends an assessment, it is easy to hear: “the system decided”.

But someone selected the system. Someone configured it using defined criteria. Someone fed it with collected data. And someone approved its use in a decision.

Responsibility does not disappear because technology is involved. On the contrary, it becomes even more important to define who decides, who supervises, who validates the data, who responds to incidents and who can stop using the tool when problems arise.

The European regulatory framework has already changed

This is no longer merely a discussion about good practice.

The European Artificial Intelligence Regulation, known as the AI Act, becomes generally applicable from 2 August 2026. Systems potentially classified as high-risk include those used for recruitment and selection, application screening, candidate assessment, decisions concerning working conditions, promotion, termination of employment contracts, task allocation based on individual characteristics, and the monitoring or assessment of performance.

Not every HR tool will automatically be classified as high-risk. The classification depends on its intended purpose and whether the system materially influences the outcome of a decision. Strictly procedural, preparatory or supporting tasks may be treated differently.

The GDPR also remains fully applicable. Article 22 establishes limits on decisions based exclusively on automated processing when they produce legal effects or similarly significant impacts on an individual. In certain circumstances, it provides safeguards such as human intervention, the opportunity to express one’s point of view and the right to challenge the decision.

Important note: this article does not replace legal advice, a data protection assessment or a compliance analysis. The classification of a tool depends on its context of use, the decisions it influences, the data processed and the safeguards in place.

Seven questions to ask before using AI in people-related decisions

• Which specific decision does this system support or influence?
• What data are used and which variables carry the greatest weight in the outcome?
• Does the system recommend, rate, monitor or decide?
• Who carries out an effective human review before a decision with significant impact is made?
• How can a candidate or employee request clarification or challenge the outcome?
• What tests are carried out to identify errors, biases or unequal effects?
• Which responsibilities remain with the organisation and which belong to the provider?

If these questions do not have clear answers, the problem is not only technological. It is a management problem.

How to create governance without unnecessary bureaucracy

An organisation does not need to create a complex committee for every new software feature. But it does need a minimum structure when technology influences people and relevant decisions.

That structure can begin with four simple practices:

• Usage map: identify which tools use candidate or employee data and which decisions they are used for.
• Impact criteria: distinguish administrative tools from systems that rate, recommend or influence relevant decisions.
• Defined oversight: assign a person or team responsible for validating use, data, incidents and critical decisions.
• Right to review: ensure that automated outcomes can be reviewed by a person with the authority, context and genuine ability to change the decision.

The aim is not to surround technology with bureaucracy. It is to ensure that efficiency, fairness and responsibility do not come into conflict because of poor design.

This challenge is connected to how organisations assess performance. As we discuss in Organisational performance: why measuring is not the same as managing, an indicator can inform a decision, but it cannot replace the management needed to interpret context, define priorities and mobilise action.

Technology can support HR. It cannot replace the responsibility of managing people.

Algorithmic management can increase consistency, help identify patterns and free HR teams from repetitive tasks. Denying that would serve little purpose.

But people management still requires judgement, context, proportionality and the ability to explain decisions to those affected by them.

At VAGAR, we help organisations structure HR, performance and leadership practices so that technology strengthens the quality of management – instead of creating new areas of opacity, unfairness or uncritical dependence on the system.

Explore VAGAR’s work in HR Structuring, Results, Performance Management and Recognition, or book an Initial Diagnostic for Organisations.

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Evidence base

Regulation (EU) 2024/1689 – Artificial Intelligence Act, particularly Article 6 and Annex III on high-risk AI systems.

Oliveira, H., & Pires da Silva, L. (2026). Algorithmic Management and AI Ethics: A Systematic Literature Review (2020–2025). Unpublished academic paper, PhD in Management, University of Évora.

Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410.

Meijerink, J., & Bondarouk, T. (2023). The duality of algorithmic management: Toward a research agenda on HRM algorithms, autonomy and value creation. Human Resource Management Review, 33(1), 100876.

Langer, M., & Landers, R. N. (2021). The future of artificial intelligence at work: A review on effects of decision automation and augmentation on workers targeted by algorithms and third-party observers. Computers in Human Behavior, 123, 106878.

Regulation (EU) 2016/679 – General Data Protection Regulation, particularly Article 22 on automated individual decision-making.