Artificial Intelligence in HR

Recruitment and Selection of Employees

Recruiting new people is one of the first areas where many companies deploy AI.

Automated resume prescreening: AI systems can sift through hundreds of resumes in an instant, searching for keywords or required qualifications to pre-screen candidates. This saves recruiters time and helps identify the most suitable applicants free from human bias. Modern tools can schedule interviews and coordinate communication with candidates – for example, automatically sending invitations to selected applicants, suggesting suitable dates based on calendars, or sending online tests. 

Chatbots and voicebots then handle the initial contact: they quickly answer frequently asked questions from job seekers at any time, even outside working hours. 

According to recruitment agency experience, up to 47% of conversations with a recruitment chatbot take place in the evenings or on weekends – meaning candidates do not have to wait for business hours and receive answers immediately. 

This improves their recruitment experience while eliminating routine queries from the HR team's agenda. AI also helps with job ad creation – generating text and even images for job openings, which the company can then use on career pages or social networks. 

Thanks to AI, a wider pool of candidates can be reached with an attractive job ad created quickly and tailored to the given position. In all these applications, however, it is crucial to ensure that algorithms do not favor or disadvantage certain groups of applicants – we will address this in the section on bias and discrimination.

Most of the mentioned AI applications in HR comply with the AI Act if implemented ethically and transparently. However, it is important to ensure that there is no manipulation, exploitation of vulnerability, social scoring, or emotion recognition of employees, which are practices prohibited by the AI Act. We recommend carefully assessing each AI application in HR for compliance with the AI Act and, if necessary, consulting with ARROWS.

Automation of HR Processes

HR professionals spend a significant portion of their time on repetitive administrative tasks – this is precisely where AI can help significantly.

• Processing routine HR agenda: AI can automatically process forms and requests (e.g., leave requests, changes in personal data) and enter them into internal systems. An internal HR support chatbot can handle employee queries regarding benefits, payroll, or company policies, thereby relieving the HR department.

• Shift and schedule planning: Advanced algorithms can optimally schedule employee shifts based on attendance, availability, and operational needs data. This saves companies time when creating schedules while taking worker preferences into account, if available.

• Onboarding newcomers: AI can guide a new employee through basic onboarding tasks – from filling out entry documents and getting acquainted with internal regulations to a virtual workplace tour via an interactive chatbot.

• HR data analysis and reporting – e.g., automatically generating regular reports on turnover, sick leave, or payroll costs, and identifying patterns or anomalies that management should pay attention to. The result is faster processes and fewer errors caused by routine manual work.

According to surveys, the automation of repetitive tasks is among the greatest benefits of AI in HR. 

Companies can thus dedicate the saved time to more strategic activities where human judgment and empathy are irreplaceable.

Employee Performance Evaluation

The area of performance evaluation and management is also moving into a new era thanks to AI. Traditional annual employee evaluations are supplemented by continuous performance data analysis, which can be partially automated.

Analysis of work results: For sales teams, AI can monitor sales and KPIs in real time and alert managers to fluctuations – for example, identifying employees with declining numbers so they can receive timely support or training. In customer centers, some systems use AI to evaluate the quality of phone calls (tone of voice, keywords) or request resolution speed and provide immediate feedback to workers.

Predictive HR analytics even promises to predict which employees are at risk of leaving (so-called flight risk) based on various indicators – project engagement, absences, team mood, etc. Managers can then act proactively, for example, by offering career advancement or addressing satisfaction before a valuable colleague resigns. These tools can significantly refine and speed up talent management. On the other hand, caution is necessary: a "black-box" algorithm that would decide on promotions or bonus amounts on its own without clear criteria is risky for a company both in terms of fairness and legally. It is no coincidence that the upcoming European AI Act classifies systems for evaluating worker performance and behavior as high-risk and imposes strict requirements on them. 

This means that in practice, a human should always have the final say – AI should serve as a supporting tool providing data and recommendations, but the final decision (e.g., whom to promote or how to evaluate) must be made by a manager, taking context and the human perspective into account.

Employee Monitoring

Employee monitoring is a sensitive area where technology is generally penetrating more and more – and AI adds new possibilities, but also controversies. Companies can use AI for tracking attendance and working hours – for example, a facial recognition camera upon arrival (instead of a punch clock) or automatic evaluation of login logs into the system for recording work from home.

Some companies are testing tools that monitor computer activity (keystrokes, mouse movement, open applications) and evaluate employee productivity. AI can analyze large volumes of such data and look for patterns – for example, alerting that a certain employee often spends time on non-work activities online, or conversely, identifying overloaded workers who do not even take breaks.

In logistics, AI systems are used for tracking vehicles and drivers (GPS trackers combined with algorithms for route optimization and detecting deviations), while in manufacturing, camera systems monitor compliance with safety rules. Modern AI cameras can, for example, automatically recognize whether workers are wearing helmets and vests and alert to violations. Such automated safety audits can prevent injuries.

Furthermore, there are experiments with communication analysis – for example, an AI tool can evaluate anonymized summaries of emails or chats in a team and measure "mood" or engagement (sentiment analysis) so that HR can detect workplace dissatisfaction in time. However, these possibilities run into privacy boundaries. Under Czech legislation, the law clearly states that an employer must not infringe upon an employee's privacy without a serious reason through systematic monitoring or control of communication. Even justified monitoring must be proportionate, and employees must be informed in advance – we will discuss the details in the legal section. 

For now, AI can help with monitoring – but companies must very carefully weigh what and how they monitor to avoid exceeding legal limits and losing the trust of their people.

Training and Development

Artificial intelligence can also personalize and streamline employee education. Traditional training often offers the same to everyone, whereas AI can analyze individual needs and performance and suggest a tailored development plan accordingly. For example, a customer service employee struggling with a specific software skill can receive a recommendation for a specific e-learning module or coaching in that area thanks to AI. AI tools track how individuals perform in knowledge tests or tasks and recommend relevant educational programs based on identified gaps. 

This personalized approach increases learning efficiency – people do not waste time learning what they already know, but focus on what they need to improve. Some companies already use chatbots as virtual instructors: an employee can ask the chatbot anything (e.g., "How do I correctly fill out a travel order?" or "What should I do in case of a customer complaint?") and the AI will immediately provide an answer, including links to relevant internal guidelines or materials. This enables just-in-time learning – a person acquires the necessary knowledge at the moment they are dealing with it.

AI can also generate training content – from automatically creating presentations based on provided materials, through translations and localization of courses, to simulations of situations in virtual reality for practice (e.g., training a conversation with a client in a simulated environment). Resulting in faster and cheaper creation of training sessions that can be easily adapted to various roles.

For large companies with hundreds or thousands of employees, AI helps scale development – providing every employee with a "coach" at their fingertips via an application or intranet. However, it is important to ensure that AI recommendations in the area of development align with the strategic needs of the company and that there is no undesirable "pigeonholing" of employees (e.g., an algorithm stopping to offer career progression to someone based solely on previous results). Therefore, here too, HR must have the final say and treat AI as an auxiliary tool.

Legal Aspects of AI in HR

The deployment of AI in HR brings not only technological but also legal challenges. Employers must respect a range of legal regulations – from personal data protection (GDPR) to labor law protection of employees and non-discrimination bans. Below, we analyze the key legal aspects that Czech companies should focus on.

Personal Data Protection and GDPR

Employee data as personal data: Most of the HR agenda revolves around the personal data of employees – whether it is candidate resumes, attendance information, performance, or training. According to the GDPR definition, personal data is any information relating to an identified or identifiable natural person. 

Therefore, if the use of AI involves processing data of specific employees (which is the case in most HR scenarios), all obligations of a controller under GDPR must be met.

Legal basis for processing: The company must have a clear legal ground for processing data. In the context of HR, this will most often be the performance of a contract (e.g., payroll processing, which is necessary for the employment contract) or the legitimate interest of the employer (e.g., streamlining recruitment using AI). If the company relies on legitimate interest, it should conduct a so-called balancing test – documenting that its interest (e.g., recruitment automation) does not outweigh the rights and freedoms of the employees. 

Transparency and information: Employees and applicants must be informed about what data and for what purpose the AI processes. GDPR (Article 13) imposes an obligation to provide the data subject with clear information, including the fact that automated decision-making or profiling is taking place. If a company deploys AI, it should therefore update its employee privacy notices, internal data processing policies, and perhaps even the wording in employment contracts or consents. Employees continue to have their rights under GDPR – the right to access data, rectify inaccuracies, or object to processing or request erasure. The company must ensure that even when AI is used, people can exercise these rights. For example, when an AI system evaluates performance, the employee should be able to find out what data the system records about them and, if necessary, have it corrected.

Contract with the AI provider: Often, the AI tool for HR is provided by an external company (for example, AI-based recruitment software). In such a case, the provider is typically in the role of a personal data processor on behalf of your company (the controller). GDPR requires having a data processing agreement concluded with the processor, binding them to comply with data protection rules. 

The company should ensure that the AI service provider secures the data, does not use it for other purposes, and enables the company to fulfill its obligations to data subjects. Also, pay attention to the data on which the AI learns: if you provide your HR data to the supplier to "train" the model, this is also personal data processing, and you must have a legal basis and contractual arrangements in place.

Risk assessment and DPIA: Regulation emphasizes assessing whether the deployment of a new technology poses high risks to employee rights. AI is often considered a new type of processing, potentially high-risk, so the obligation to conduct a DPIA (Data Protection Impact Assessment) may apply. 

For example, if a company wants to introduce an algorithm for screening applicants, it should analyze the risks beforehand – what if the algorithm unfairly excludes someone, what about a data breach, etc. If the DPIA reveals high risks that cannot be mitigated, the Office for Personal Data Protection (ÚOOÚ) must be consulted. In any case, involving the company's Data Protection Officer (DPO) (if the company has one) is highly recommended when deploying AI in HR.

Data security: AI systems tend to be data-intensive and sometimes operate in the cloud – making it all the more important to pay attention to cybersecurity. A leak of sensitive employee data (evaluations, salary, personal files) could have serious consequences for the company. Both GDPR and the Czech Labour Code impose an obligation on the employer to protect employees' personal data from misuse. In practice, this means implementing technical and organizational measures – database encryption, access control (authorized users only), regular audits, training for AI tool administrators, etc. If, for example, AI were to generate answers to queries and someone entered sensitive HR data (such as a salary list) into it, this data could be sent to the provider's servers – this is also a security risk that employees must be warned about (see recommendations below).

Underestimating GDPR compliance does not pay off – fines for violations can reach up to EUR 20 million or 4% of the company's global turnover. 

However, the employer's reputation is equally important: a data leak scandal or employee complaints about privacy violations can damage trust and corporate culture.

the ARROWS legal team in Olomouc

Discrimination and Bias in AI

Risk of algorithmic discrimination: AI in HR promises objectivity because it decides based on data. However, without the right measures, it can instead carry over and amplify biases present in the data. A classic example is a recruitment algorithm that "learns" from historical data to prefer a certain type of candidate. If, for example, a company historically hired mostly men for IT positions, the AI might adopt this pattern and begin systematically disadvantaging women's resumes – without anyone explicitly intending to do so. A well-known case is Amazon's experiment, which developed an internal AI to evaluate resumes: after some time, they found it gave lower scores to female applicants because the historical data was "biased" in favor of men. 

For example, the algorithm penalized mentions like "captain of the women's chess club" simply because the word "women's" did not appear in the data of successful candidates. 

Such behavior is, of course, undesirable and impermissible in the context of labor law.

Prohibition of discrimination: The Czech Labour Code and the Anti-Discrimination Act explicitly prohibit discrimination in employment based on protected characteristics such as gender, age, racial or ethnic origin, religion, sexual orientation, etc. This applies to recruitment, remuneration, promotion, and termination of employment. If a decision made by AI leads to unjustified disadvantage of a person due to any of these characteristics, the employer bears responsibility – just as if a manager had discriminated directly during an interview. Companies must therefore carefully monitor how AI decides and test whether it exhibits bias before deployment. For example, with a CV screening tool, tests with fictitious profiles (woman vs. man with the same qualifications, etc.) can be conducted to verify that the results are not systematically different.

Furthermore, it is advisable to limit the use of inputs that could serve as proxies for sensitive characteristics – typically home address (which may correlate with an ethnic minority), length of parental leave (which may favor men over women), candidate photos, etc.

Obligations of employers: In practice, an AI system should never make an irreversible decision entirely on its own. European law (GDPR Article 22) prohibits people from being subjected to purely automated decisions with a significant impact, unless special safeguards exist. An example would be if an algorithm entirely on its own, without human assessment, decided on the acceptance or rejection of an applicant – such a procedure is (with exceptions) prohibited because it fundamentally affects the person. 

The company should therefore ensure that a human is always involved in the process, who can re-evaluate the AI's recommendation. This not only fulfills the legal requirement but also reduces the risk of discrimination – a human recruiter can detect a suspicious pattern (e.g., "AI has filtered out all women, that's not right") and intervene.

Training and tuning models: Responsible AI providers today emphasize so-called ethical AI – models designed to minimize bias. For a company developing its own AI solution, this means including a fairness tuning phase in development. For example, algorithm weights can be adjusted or balancing mechanisms added if it is found that a certain group performs worse without a legitimate reason. If you are buying an AI product from a third party, ask the supplier how they addressed the issue of bias and whether they conducted discrimination tests.

Monitoring AI decision-making: It does not end with deploying AI – outputs must be continuously monitored. It is recommended to keep records (logs) of how the AI made decisions and regularly analyze them, for example, in terms of the ratio of accepted/rejected candidates by gender, age, etc. The mandatory fundamental rights impact assessment (so-called ALTAI – Assessment List for Trustworthy AI), which the new AI Act will likely require for high-risk systems, can also help with this. In short, a company must supervise AI much like it supervises its managers – setting boundaries and checking that it adheres to them. Ignoring this obligation does not pay off: in addition to fines, there is a risk of lawsuits from rejected applicants or employees who feel discriminated against, as well as a loss of reputation.

Employee Monitoring and Their Rights

Right to privacy vs. employer control: As outlined above, modern technologies give employers unprecedented opportunities to monitor what employees do. From a legal standpoint, however, employee privacy is protected and cannot be infringed upon excessively. The Czech Labour Code (Section 316) states that an employer may monitor the use of its production and working tools (computers, phones, vehicles, etc., provided to the employee) only in a reasonable manner and to the extent necessary. 

This means that it is not possible to monitor all employee activity across the board and without limits – monitoring must have a reasonable ground (e.g., protection of property, safety, trade secrets) and must be as non-invasive as possible. Covert monitoring of communication of an employee (reading private emails, wiretapping calls) is explicitly prohibited unless there is a serious reason consisting in the special nature of the employer's activity. 

Such a reason could be, for example, the need to prevent leaks of highly classified information in a specific industry, but in a standard company, there is usually no legitimate reason for it. Even where it exists (e.g., a security agency may have a reason to monitor whether a guard is actually patrolling the premises), the principle of proportionality applies – monitoring only what is necessary and no more.

Obligation to inform: Crucially, the employer must inform employees in advance about any monitoring, its scope, and methods. It is therefore not enough to have it hidden somewhere in an internal directive – employees must be demonstrably informed about whether and how they are monitored.

For example, if a company deploys AI that analyzes emails for keywords (even if only for cybersecurity), it must warn employees that corporate mail is monitored and what data is involved. The Labour Code does not require employees to consent to this (consent cannot be coerced), but they must be aware of it.

AI and monitoring: The use of AI by the employer does not change these obligations – it does not matter whether the monitoring is performed by a human or an algorithm. If, for example, AI evaluates camera footage and reports suspicious behavior, the same limits apply as for standard camera systems. Furthermore, using AI again involves personal data processing, so GDPR applies – for example, facial recognition on a camera is biometric data, which has a special sensitive status.

European oversight: Not only Czech courts but also the European Court of Human Rights have repeatedly commented on employee monitoring. In the well-known case of Bărbulescu v. Romania, the ECHR stated that dismissing an employee for using a corporate email for private purposes was unjustified because the employee had not been clearly informed in advance about the scope of monitoring. 

This is an important lesson for companies: even if an employee violated the rules (using a work computer privately), evidence cannot be obtained through covert monitoring they were unaware of. In conclusion, when implementing AI monitoring tools (attendance cameras, computer tracking, etc.), pay attention to legal boundaries.

Set up monitoring so that it is targeted, not blanket; record only necessary data; strictly exclude monitoring of areas where the law prohibits it (changing rooms, toilets, rest areas – where employees have absolute privacy). Always inform employees openly about what is being monitored and why. Also consider the ethical side – excessive monitoring can disrupt workplace relationships. Use AI in monitoring primarily for safety and truly serious reasons, not for micromanaging every step of an employee.

It is therefore essential that employers conduct a thorough legal compliance analysis before implementing facial recognition technologies and consider whether such processing of biometric data is necessary and proportionate to the purpose they wish to achieve. The Czech legal team at ARROWS, specializing in combining AI, HR, and GDPR, will be happy to help you with this. We have the experience. In 2018, an inspection on this topic took place at Metrostav.

Given that the legal framework in this area may evolve, it is advisable to monitor the current positions of the ÚOOÚ and any legislative changes that could affect the possibilities and conditions of biometric data processing in the private sector.

Responsibility for Decisions Made by AI

Who bears responsibility: Imagine a situation – an AI system selects a candidate whom the company hires, or conversely, recommends dismissing a certain employee. Who is legally responsible for this decision? From a legal perspective, it is always the employer. AI is not a legal entity and is not responsible for its actions – it is a tool, much like accounting software. If an error occurs (discrimination, unjustified termination of contract, etc.), liability toward the affected employee or applicant will be borne by the company, not the AI provider or the "computer." It is therefore in the employer's interest to keep AI decision-making under control.

Automated decisions in employment relationships: As already mentioned, purely automated decisions with a major impact on individuals are in principle prohibited by GDPR. In HR, this includes decisions such as hiring/not hiring, promotion/non-promotion, termination, significant changes in salary or bonuses, etc. A company should never find itself in a situation where a decision "was made by AI and no one else saw it." Firstly, it would be legally contestable, and secondly, it would be difficult to defend such a decision – how would you explain in court why someone was dismissed if the answer was "the algorithm decided, and we actually don't know why"?

Requirement of human oversight: It is good practice (and soon a legal obligation under the AI Act) to ensure human oversight over AI. This means designating individuals who understand how the system works, can intervene or shut it down if necessary, and who primarily take over final decision-making at critical moments. For example, AI can suggest a ranking of candidates, but the recruiter must review the top selection and confirm or adjust the decision. When using AI for attendance monitoring, AI can flag suspicious patterns (frequent late arrivals), but the manager must speak with the employee and find out the reasons before taking disciplinary action.

Auditability and explainability: Responsibility is also linked to the company being able to substantiate the reasons for a decision. With AI, we often talk about the issue of "explainable AI" – especially with so-called black-box algorithms, it is difficult to trace back why they decided the way they did. However, if a person's career depends on a decision, it is only fair to be able to provide them with an explanation. For a company, this means preferring AI tools that are transparent or provide a so-called explanation interface – e.g., displaying the candidate's score with the justification "does not meet requirement XY" instead of a simple "rejected." It is also advisable to save logs of the AI's activity so that it is possible to trace how it arrived at a certain result.

Appealing AI decisions: If an employee or applicant challenges a decision in which AI was involved (e.g., they suspect they were discriminated against by the algorithm), they should be able to address it in the same way as if it were a manager's decision. The company should have an internal process to investigate such a complaint – typically involving HR and IT experts to analyze whether the AI erred. In an extreme case, the dispute would be resolved by a court or the labor inspectorate, and the company would have to defend its procedures.

Supplier vs. user: Of course, externally the employer bears responsibility, but if the failure was caused by an error in the AI tool, the company can subsequently claim damages from the supplier (if they breached the contract, etc.). This is also why it pays off to choose reputable and proven AI systems and have contractual arrangements for what happens if the tool does not perform as expected (including the supplier's legal compliance commitments). Currently, the EU is also discussing special rules for AI liability (the AI Liability Directive), which should make it easier for injured parties to seek compensation – this is also a signal to companies that liability is taken seriously.

Závěr je jasný: za AI si ručí firma sama a podle toho k ní musí přistupovat – jako k nástroji, který je potřeba pečlivě nastavit, kontrolovat a v případě potřeby korigovat.

Practical Recommendations for Czech Companies

Implementing AI in HR is more than just a technical project – it also requires setting up the right internal rules, an ethical framework, and communication with employees. Below we present recommendations that will help use AI in HR responsibly and in compliance with the law.

Setting Internal Rules for AI in HR

AI strategy and policy: The first step should be to develop an internal strategy for using AI. Company management should clarify in which areas they want to use AI, what goals they are pursuing (e.g., speeding up recruitment, reducing administrative burden), and what the limits are. This is followed by creating an internal policy or directive that establishes rules for using AI in HR.

It should answer questions such as: Who is responsible for the operation and oversight of AI tools? What types of decisions can AI make independently, and where is escalation to a human required? How will the deployment of a new AI tool be tested and approved? How will its outputs be monitored and potential issues resolved? Such a directive will ensure that everyone in the HR team acts consistently and consciously.

AI governance in practice: It is recommended to establish, for example, an internal commission or council for AI governance in the company. This could include representatives from HR, IT, the legal department, and management. Its task would be to approve new AI tools, oversee their operation, and resolve ethical dilemmas that may arise. In a smaller company, this might just be a regular meeting of the HR manager with IT and a lawyer regarding AI use. It is important that AI does not spin out of control simply because someone introduced it "secretly" without the knowledge of others and without addressing risks.

Records and audit: Keep an inventory of all AI systems you use in HR, including a description of their function, supplier, processed data, and risk mitigation measures taken. This inventory is useful both for internal needs (you can easily find out what is running where) and for potential communication with authorities or during audits (you have prepared how you meet the requirements of GDPR, the AI Act, etc.).

Failure procedures: Define what to do if an AI tool fails or causes an incident – for example, if a recruitment algorithm starts producing strange results. It should be clear who has the authority to suspend the system, whom to inform (management, DPO, IT security), and how to carry out corrective action. This will prevent chaos in a crisis situation.

Ethical Guidelines and Codes for AI

In addition to hard rules, it is proven useful to establish ethical principles that the company will follow regarding AI. These can build on existing corporate values or a code of ethics and expand them to include AI. Some organizations adopt an AI ethical charter, committing to certain principles – e.g.: "We will use AI transparently and fairly; AI decisions are always subject to human review; we care about data privacy and security; we ensure equal treatment and non-discrimination; we are responsible for the impacts of AI on our employees." Such statements not only help internally but can also serve externally to build trust (e.g., a company can communicate to candidates that it uses AI ethically and in compliance with GDPR during recruitment).

External inspiration: There are many frameworks and recommendations to lean on. The European Commission published the Ethics Guidelines for Trustworthy AI (2019), which define principles such as human oversight, technical robustness, privacy, transparency, diversity and non-discrimination, and accountability. The OECD has its AI Principles, and for example, the US government published the Blueprint for an AI Bill of Rights (2022) protecting employees' rights in connection with AI. 

Specifically for HR, there are recommendations from professional organizations (SHRM, CIPD, etc.), often emphasizing three pillars: legality, ethics, and inclusion. A company does not have to invent everything from scratch – it can adopt proven principles such as: "AI must not violate employee rights, it must be deployed with their trust and for the benefit of both parties, AI decisions must be explainable and reviewable, we will introduce new technologies sensitively and with regard to the impact on people," etc. 

As Jaromír Staroba, president of the industry organization ABSL, emphasizes, it is important for companies to monitor the global development of regulations and guide their innovation teams according to ethical guidelines. Practically, this means training developers and AI users on ethical risks, consulting on AI deployment with various stakeholders (e.g., trade unions, if they exist), and avoiding solutions where it is not clear how they work. Staroba warns against "black-box" AI tools for HR without documentation and transparency – it is better to avoid these. 

Instead, prioritize solutions that allow for auditing and where the supplier openly shares information about the algorithm (at least at the level of explanation, not necessarily source code).

Culture and values: Introduce the topic of AI into corporate culture as well. If a company emphasizes employee care, it should approach AI with the mindset that it should serve people, not replace or excessively monitor them. Ethical use of AI becomes real when employees themselves support it – which happens when they see that AI helps them rather than harms them. Therefore, build a culture where innovation goes hand in hand with responsibility.

Communicating with Employees About AI

Transparency toward employees: For employees to accept AI positively, they must understand why and how it is used. It is therefore crucial to communicate openly from the very beginning. If you plan to deploy AI in recruitment or internal processes, inform employees in advance. Explain what tasks the AI will perform (e.g., "the chatbot will answer your payroll queries" or "the new system will analyze attendance and suggest shifts") and emphasize what people will get out of it – faster HR services, less administration for them, etc. Also reassure them that AI will not be misused – e.g., that it is not used for disproportionate monitoring or evaluation without context. If you introduce AI into recruitment, you can also communicate this to candidates (e.g., mentioning in the job ad that initial selection is done by an algorithm to ensure fairness).

Training and awareness: Not every HR specialist or manager understands what AI actually entails. Therefore, it is advisable to organize internal training or workshops on AI in HR – presenting opportunities as well as risks (privacy, bias) and outlining new processes. Involve both the HR team and managers, and even broader employees if it concerns them (e.g., training for managers on how to correctly use AI outputs when evaluating subordinates). The goal is to eliminate fear of the unknown – many employees may have concerns about whether AI will "spy" on them or even replace them. Open discussion will help clarify that AI is a tool designed to make people's work easier, not to punish them.

Involving employees in the design: If possible, involve employee representatives (perhaps from different departments) in the pilot deployment of AI. They can provide valuable feedback, point out practical issues, and at the same time act as ambassadors who can then better explain the innovation to colleagues.

AI policy for employees: In addition to the employer's use of AI, there is also the issue of employees using publicly available AI (e.g., ChatGPT at work). It is good to communicate clear guidelines to employees: what they can and cannot enter into external AI tools. For example, warn them not to input sensitive company information into public AI services that could store or misuse it. 

Explain this clearly – e.g., "Anything you write into an AI generator can be stored and potentially accessed by strangers, so it is forbidden to enter personal data of colleagues, customers, or non-public information about the company." 

Employees often do not know how these technologies work, so education is key.

Communication during an AI error: If an incident occurs (for example, AI incorrectly categorizes a high-performing employee as "low performance"), communicate it openly and apologize for any mistakes. Show that you are aware of the limits of AI and that you will do your best to rectify it and ensure fairness. This will maintain trust – people appreciate when a company admits that technology is not infallible and puts human judgment first.

Future Development and Upcoming Legislation

The field of AI is developing very dynamically – and with it, the legislation trying to keep pace. In the coming years, companies face fundamental changes in AI regulation, especially from the European Union level, which are worth monitoring and preparing for.

The AI Act and AI Regulation in the EU

The most significant upcoming regulation is the Artificial Intelligence Act (AI Act), which was approved at the EU level in 2024 and will gradually enter into force (full application is expected around 2026). The AI Act introduces a comprehensive framework for AI regulation with a risk-based approach. It classifies individual AI applications into categories: prohibited use (e.g., so-called social scoring of citizens), high-risk systems, systems with limited risk, and minimal risk. 

Specifically, AI in HR (recruitment, management, and monitoring of employees) is classified under the law's definitions as a high-risk area. 

This means that strict requirements will apply to AI tools used, for example, for candidate selection, decisions on promotion or termination of employment, performance evaluation, or monitoring employee behavior. 

Obligations for companies (AI users): The AI Act distinguishes between obligations for AI system providers and their users (so-called deployers). The deployer will typically be a company that deploys, for example, purchased AI recruitment software. Key obligations of a user of high-risk AI will include: ensuring adequate human oversight over the system, strictly following the manufacturer's instructions, validating input data (i.e., monitoring the quality and representativeness of the data we "feed" into the AI), monitoring AI results during operation, and reporting any serious incidents or malfunctions to the supplier. 

Furthermore, keeping records (logs) of AI activity and even conducting a fundamental rights impact assessment (to prove that the system does not harm human rights, e.g., does not discriminate). Companies will also have to inform their employees (or their representatives) that they are subject to decisions made by an AI system at work or during recruitment. This again emphasizes the principle of transparency.

Sanctions under the AI Act: Non-compliance with obligations for high-risk systems will be punished with fines – the adopted regulation sets a fine of up to EUR 15 million or 3% of global annual turnover (Article 99(4)). It is therefore clear that the EU takes the use of AI in HR very seriously and wants to prevent abuse or ill-considered deployment that could harm employees.

What to do right now: Although the AI Act is only beginning to be put into practice, companies should start preparing now. It is recommended to conduct an audit of all AI or advanced algorithms you use or plan to use in the company – to determine whether they fall under the AI Act's definition (which is quite broad) and whether they happen to fall into the high-risk category (if they concern employees, there is a high chance they do). Furthermore, monitor the development of implementing regulations and standards that the EU is preparing for the AI Act – they will specify technical details, such as how exactly to perform those impact assessments or how to record AI use. 

Companies should update internal procedures to comply with the new requirements – this includes, for example, introducing the aforementioned human oversight, ensuring training for persons working with AI, adjusting contracts with suppliers (so they fulfill their obligations), and so on. 

Those who start on time will gain a competitive advantage because they will not have to hastily redo processes at the last minute. The AI Act may seem like an administrative burden, but its goal is similar to GDPR – to increase trust in technologies by using them responsibly. For companies, this can ultimately mean that well-set AI processes will give them a head start (they will be able to use AI to the fullest without fearing legal issues).

Further Legislative Development

In addition to the AI Act, other regulations related to AI are being discussed. The European Commission withdrew the proposal for the AI Liability Directive in October 2025; for damages, the new Directive (EU) 2024/2853 on liability for defective products, which also includes software, is relevant. In the field of labor law, we can expect the topic of AI to be reflected in the agendas of labor inspections and possibly also in collective bargaining. Trade unions in some countries are already calling for a right to disconnect in relation to AI – so that workers are not evaluated solely by dry metrics 24/7.

There is also talk of a right to an explanation of AI decisions and the right to refuse to be evaluated automatically. We already have some of these principles in fact thanks to GDPR, but we can expect them to be strengthened. Czech legislation will have to implement European standards – e.g., adjusting the competences of the ÚOOÚ, labor inspectorates, and potentially anchoring certain obligations in the Labour Code or the Employment Act (e.g., regarding informing applicants/employees about AI).

Industry dynamics: AI technologies are developing rapidly, and the legal framework will constantly adapt to them. Companies should therefore monitor updates – participate in seminars, read professional articles, or use the services of legal advisors specializing in IT law and HR. It is also worthwhile to share experiences in the industry – e.g., exchanging insights within professional meetings of HR managers on how they address AI and what has worked for them.

Ethics as a permanent topic: In addition to laws, there will also be pressure from the public and employees themselves. Generations entering the job market (Millennials, Gen Z) are sensitive to values and will ask how a company uses AI. It could become part of employer branding to say "we use AI responsibly." On the horizon are also reflections on collective rights – e.g., whether employees should co-decide on the introduction of AI in the workplace (through works councils), etc. These are all questions that the coming years will bring.

Conclusion

Artificial intelligence offers HR departments great opportunities to streamline work and make decision-making more data-driven. It can help Czech companies find talent faster, better motivate and develop employees, and maintain competitiveness. At the same time, however, it is not a panacea – it requires careful implementation, continuous monitoring, and, above all, respect for the people it affects. Legal risks are not negligible: from fines for GDPR violations to lawsuits over discrimination and damage to the employer's reputation. This practical guide has emphasized that the key is balance – using AI where it brings clear benefits, but always with common sense, human oversight, and within the boundaries of law and ethics. If a company approaches AI as a tool that serves people (and not vice versa), it can get the most out of it in HR with minimal risk. The future of HR will likely belong to smart collaboration between humans and machines – let us prepare for it responsibly and with open eyes.