PhD Sample Proposal: The Morality of the Algorithm: The Legal and Ethical Implications of AI Adoption in HRM Decision Support in the European Retail Industry
Abstract
This research proposal aims to investigate the complex legal and ethical challenges associated with the adoption of artificial intelligence (AI) technologies in human resource management (HRM). These concepts have already been used in the past by certain retail organisations, such as Amazon in the US. Since these cases revealed multiple ethical issues associated with recruitment bias and ethical controversies, there exists an urgent need for modern companies to understand how technologies can be utilised in an ethical and lawful manner.
The situation is further complicated by the introduction of multiple regulatory acts in this field, including the EU Artificial Intelligence Act. By adopting a mixed-methods approach, this study will explore the experience of HRM professionals from this context to develop a framework for responsible AI implementation in the retail industry. The findings will provide actionable guidelines for key stakeholders, helping them navigate the evolving regulatory landscape while utilising AI technologies in a fair manner without sacrificing their efficiency benefits.
1. Introduction and Problem Statement
The European retail industry has been affected by a variety of problems in previous decades, including COVID-19, political tensions, Brexit, and other trends reducing the amount of resources available to firms (Damnjanovic et al., 2025; Deschenes, 2022). Automation in multiple spheres offers a promise of unprecedented efficiency gains. Big data and other AI-powered instruments have already helped organisations achieve tangible improvements in such areas as logistics, supply chain management, and marketing (Bughin, 2023). With that being said, HRM represents a more complex and challenging sphere for automation due to the involvement of multiple legal and ethical questions. The European labour market is generally characterised by a highly diverse workforce and strict regulations related to recruitment, onboarding, and other elements of the employment life cycle (Sparrow & Makram, 2022). In terms of AI, the EU Artificial Intelligence Act, introduced in 2024, classifies such systems as high-risk HR elements and introduces strict obligations for firms using them. Partially, these regulations can be related to adverse past cases, such as Amazon’s AI-powered recruitment instruments showing strong racial, gender, and other types of bias without external supervision from human HR specialists.
These problems largely stem from several areas, including algorithmic bias caused by the use of historical data for training that was inherently skewed towards certain recruitment patterns or the overall lack of transparency, making AI algorithms a ‘black box’ for HR professionals (Collings & Mellahi, 2020; Malik et al., 2022). With most retail firms having to use third-party information technology (IT) solutions instead of developing their own internally, this can lead to a lack of understanding of how certain decisions are made (Bughin, 2023). This forms the main problem analysed by this thesis, namely, how retail industry practitioners can become aware of the legal and ethical implications of AI adoption in HRM and what measures they must implement to make these practices ethically sound and legally compliant. The analysis will incorporate a variety of areas, including algorithmic bias, discriminatory biases, lack of transparency, dehumanisation of HR practices, and the risks of invasion of employee privacy due to automated performance monitoring and supervision (Kaur & Gandolfi, 2023).
2. Literature Review
2.1. Key Ethical Problems of Artificial Intelligence Utilisation in HRM
The problems related to AI ethics largely stem from several key areas associated with both the nature of the analysed technology and its practical implementation by modern organisations (Jatoba et al., 2023; Nawaz et al., 2024). The first factor is mainly related to the whole concept of supervised machine learning. AI systems base their decisions on the earlier analysed data used for ‘training’ them. However, this information can include various biases, making the resulting automated algorithms discriminatory against certain groups missing or underrepresented in the original datasets. These problematic elements can include gender, race, education, and other characteristics, resulting in a ‘filter bubble’ affecting both the recruitment and progression perspectives of disadvantaged groups affected by such unfairness (Smarn et al., 2025). This problem is directly related to the second issue of transparency and ease of interpretation. Some of the articles of GDPR and the EU Artificial Intelligence Act highlight the term ‘right to explanation’ (Linera & Meuwese, 2025). With many retail firms lacking in-depth knowledge of AI technologies, the newly introduced regulations may be difficult to comply with for them. In the worst-case scenario, they may not be able to fully explain the reasons for specific decisions due to the limited interpretability of advanced decision-making support platforms and their lack of transparency.
2.2. Advantages Offered by AI for HRM Practitioners
The increasing interest in AI solutions is explained by the benefits offered by big data and similar technologies to HR professionals (Habbal et al., 2024; Qasaimeh & Jaradeh, 2022). First, such platforms can screen thousands of CVs, parse cover letters, and interpret verbal and non-verbal information collected via interview videos in a brief period of time. The automation of these operations can allow specialists to delegate this routine to the AI and focus on the appraisal of the results in order to find top talent available in the market (Khdour et al., 2025; Smarn et al., 2025). Second, such tools can monitor and interpret big data related to employee behaviours, communication patterns, task completion rates, and other selected metrics (Priyashantha, 2023). This allows managers to recognise top-performing workers, identify the need for additional training and support, offer more specialised and targeted career guidance suggestions, and predict turnover intentions (Jarrahi, 2018). With that being said, the complexity of AI tools further suggests that such decision support strategies may be inherently biased and may lead to incorrect interpretations without sufficient human control (Iqbal, 2018). For example, employees erroneously tagged as ‘flight risk’ can receive less attention and develop genuine motivations to leave the company.
2.3. Regulatory Barriers to AI Implementation
The most notorious landmark documents in the studied sphere applicable to the EU retail industry are the EU Artificial Intelligence Act and GDPR (Linera & Meuwese, 2025). Both of them prescribe specific procedures and strategies in recruitment, HRM, and employee data handling spheres that firms have to strictly observe. In terms of conformity assessment, these acts prescribe specific compliance requirements ensuring that the utilised systems provide sufficient transparency and human oversight over AI operations (Sachan et al., 2024). These requirements also characterise the use of these platforms as a ‘high-risk’ HRM area, which implies additional attention from regulators in the near future. Overall, Article 22 of the GDPR and multiple provisions of the EU AI Act imply that firms cannot use fully automated applications for processing, profiling, and firing instruments without clear human oversight (Linera & Meuwese, 2025). These strict provisions create a narrow ‘middle ground’ where firms have to strictly observe all of these instructions while also ensuring that they capture the value offered by AI solutions (Sparrow & Makram, 2022). The challenge is further intensified by the existing gap in the literature on the topic related to the lack of information about context-specific applications of these concepts.
3. Research Questions and Objectives
The proposed study will be guided by the following main research question,
How can European retail companies navigate the ethical and legal challenges related to AI technologies and ensure their responsible and effective implementation in HRM decision support?
This question will be addressed via the following research objectives:
- To identify and critically assess the main ethical risks (bias, lack of transparency, privacy concerns) associated with the existing AI tools used in HRM within the European retail sector.
- To evaluate the level of awareness, preparedness, and compliance of European retail organisations with AI-related legal challenges, including the EU AI Act, specifically regarding high-risk AI systems in HR.
- To explore the experiences and perceptions of key stakeholders such as HR professionals, legal/compliance officers, and employees regarding the fairness, transparency, and impact of AI-driven HR decisions in the retail context.
- To develop and propose an evidence-based governance framework for ensuring the ethically and legally compliant implementation of AI decision-support tools in HRM tailored to the needs of the retail industry.
4. Proposed Methodology
4.1. Research Design
The proposed study will adhere to the mixed-methods sequential exploratory design involving two phases (Creswell & Clark, 2018). The first one will include the collection of qualitative data from selected industry stakeholders in the form of semi-structured interviews and focus groups. This phase will generate rich and detailed information, capturing the experiences and concerns of experienced practitioners in the sphere of European retail. Insights obtained during this stage will be used for the development and refinement of survey forms used for the second stage (Korstjens & Moser, 2018). This consequent phase will be focused on line-level HR specialists, legal/compliance officers, and employees. It will collect quantitative data measuring their preference for specific practices, main attitudes towards AI-powered HRM tools, and levels of awareness regarding key risks, challenges, and threats related to their implementation. This design is substantiated by the fact that the studied sphere is a rapidly developing field in terms of ethical and regulatory concerns, making it difficult to develop effective survey instruments entirely based on existing literature (Lambert, 2019). Hence, a combined sequential exploratory strategy will allow the author to combine initial assumptions with interview and focus group insights to ensure that the developed tool for capturing quantitative data fully reflects the contextual particularities of European retail.
4.2. Data Collection Methods
4.2.1. Qualitative Data Collection
In-depth semi-structured interviews will be conducted with 10-15 participants, including senior HR managers from leading European retail chains, AI ethics experts working in the retail sector, and senior legal and compliance officers specialising in employment legislation and data protection. Respondent selection will be based on purposive sampling and snowball sampling strategies, where the participants will be selected based on their deep knowledge of the studied field and will be asked to recommend their colleagues sharing similar degrees of awareness (Cohen et al., 2018). Two to three focus groups will be formed from 6-8 participants each to stimulate group dynamics and explore the collective views of HR managers and legal experts. They will be asked to share their perceptions of the impact of AI on their work and the challenges encountered by their firms in relation to it (Hall, 2020).
4.2.2. Quantitative Data Collection
The quantitative phase of the study will be organised in the form of an online survey (Mukherji & Albon, 2018). The structured questionnaire based on Likert-scale response options will be delivered to HR professionals, legal/compliance officers, and employees from UK retail firms. The sampling strategy can be characterised as convenience non-probability sampling. The researcher will use several online platforms as well as their own industry contacts to reach the aforementioned groups in the retail sector, including electronics, grocery, fashion, and other sub-sectors (Daverne-Bailly & Wittorski, 2022). Considering the large population size of this segment, the selected sample size was set to approximately 370 respondents. This ensures that the results are statistically significant and generalisable to the industry as a whole, with the total number of firms in it exceeding 10,000 organisations.
4.3. Data Analysis
4.3.1. Qualitative Data Analysis
Interviews and focus groups will be recorded in real time to minimise interference or loss of data (Braun & Clarke, 2021). The recordings will be transcribed afterwards and processed using thematic analysis in NVivo. This procedure will involve coding the data and identifying recurrent patterns and themes related to the ethical and legal implications of AI adoption in HRM (Saunders et al., 2019).
4.3.2. Quantitative Data Analysis
Quantitative survey data collected via online forms will be downloaded in numeric format and uploaded into the SPSS package (Hennink & Kaiser, 2022). The analysis will include descriptive statistics (frequencies and means) as well as correlation analyses and T-tests to examine the links between different variables. Considering the intention to develop an evidence-based governance framework for ensuring the ethically and legally compliant implementation of AI decision-support tools in HRM, the author will seek to explore both obvious relationships between variables and less direct connections, such as the relationship between company size and its perceived compliance readiness (Bryman, 2016).
4.4. Ethical Considerations
All participants will be provided with highly detailed informed consent forms to ensure their full understanding of the study’s aim and objectives (Stutchbury, 2017). The data will be stored on a password-protected device without transferring it to any third parties and will be anonymised to guarantee full anonymity and confidentiality (Byrne, 2022).
5. Timeline and Expected Contributions
5.1. Project Timeline
The following timetable presents an overview of key activities leading to the completion of this project.
Figure 1
Gantt Chart
5.2. Theoretical and Practical Contributions
The proposed study will contribute to academic knowledge on AI adoption in HRM by bridging the fields of artificial intelligence, technology legislation, and business ethics. It will develop an empirically tested framework for understanding the complex interplay of these concepts in the contemporary retail context (Jatoba et al., 2023). It will also extend theoretical discussions on algorithmic HRM by introducing the dimension of regulatory compliance as a source of additional risks. From a practical standpoint, this study will help retail firms avoid legal risks by adopting clear guidelines for auditing and mitigating potentially problematic areas (Smarn et al., 2025). This will allow them to fully utilise the advantages offered by AI technologies while reducing exposure to state penalties and compliance threats.
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