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Ethical Oversight of HR Predictive Models: A Necessity for Artificial Intelligence

The integration of artificial intelligence into human resources encompasses various aspects: task automation, process simplification, as well as data analysis and processing. However, it is crucial to emphasize the importance of ethical oversight of HR data when using AI to ensure responsible use of this technology. How can adequate ethical supervision of an AI HR predictive model be ensured to reconcile operational efficiency with fundamental ethical principles? 

The Importance of Ethical Oversight of AI HR Predictive Models 

In a context where artificial intelligence is increasingly mobilized in various functions, its use must be guided by ethical principles and respect for human rights, especially in the field of human resources. To establish an ethical environment within a company in HR, it is essential to follow and adopt the following approach: 

  1. Ensure Transparency and Accountability 

Today, companies have a greater need to adopt a transparent approach to the algorithms used and the data collected. Employees must have a clear understanding of how their personal data is collected, stored, and processed. This transparency enhances the trust of all employees in the AI solution used while reinforcing the company’s responsibility for the ethical use of AI to create a fair and ethical work environment. 

  1. Ensure Equity and Inclusion 

In the context of ethical oversight of AI systems in human resources, one of the fundamentals is the establishment of equity and inclusion. Based on criteria such as age, gender, marital status, or other personal criteria, algorithms can unfortunately convey inherent biases and favor certain candidates over others. It is imperative that these AI systems are designed and trained to rigorously respect human rights to avoid any form of discrimination. This ensures that each person has equal opportunities, which is not only ethical but also beneficial for the overall performance of the company. 

  1. Respect Privacy and Confidentiality 

A company must ensure that its employees’ personal data is stored securely and is in no way used for illegal or discriminatory purposes. It is equally crucial to transparently inform employees about the use of their data, how it is collected, and processed. To preserve privacy, it is essential to establish well-defined mechanisms to ensure that collected data is deleted when no longer needed. This method reinforces employees’ trust in the ethical treatment of their personal information within the company. 

  1. Ensure AI Algorithm Recommendations Adhere to Ethical Standards 

Ensuring that recommendations generated by AI algorithms rigorously adhere to ethical principles is fundamental. Every piece of information, recommendation, or other action proposed by artificial intelligence must be evaluated against ethically acceptable criteria. The goal is to design not only algorithms that incorporate ethical standards from their inception but also to establish monitoring and regulatory mechanisms to detect and correct any ethical deviations. 

  1. Interpretation of Results Recommended by AI 

The use of AI in HR can provide many useful recommendations. To ensure ethical oversight of AI systems in HR, all results generated by this technology must be subject to human decision-making to provide a more balanced and situationally appropriate perspective. This approach ensures that AI is used as a support tool, thus preserving the human and ethical aspect at the heart of decisions in the field of human resources. 

Importance of Ethical Oversight of AI in the HR Sector: Examples of Use Cases 

  • Usage in HRIS (Human Resources Information Systems): 

HRIS, designed to manage and process personal and sensitive employee data, must interpret and use data ethically. It is imperative that their interpretation and use follow ethical guidelines, ensuring the privacy and confidentiality of all employees’ data. 

  • Usage in Bots: 

The use of bots, especially in self-service functionalities of HCM/HRIS systems, is becoming increasingly popular. Establishing ethical oversight of chatbot models is mandatory to ensure their compliance with established HR guidelines. This ensures that chatbots, as automated interfaces, operate in harmony with the company’s values and goals while adhering to rigorous ethical standards. 

  • Usage of Semantic Analysis to Source Candidates on Social Networks: 

This use case involves automated analysis of CVs and the identification of the best profiles. Establishing ethical oversight for the collected data is crucial, ensuring its use within a well-defined framework. This guarantees that AI operates in accordance with principles of confidentiality, non-discrimination, and respect for candidates’ rights, contributing to the establishment of a fair and just recruitment process. 

AI and HR: How Does TOP Promote Ethical Oversight of Predictive Models? 

In an increasingly automated world, TOP, the solution for augmented management, takes into account the ethical aspects of AI predictive models in HR by implementing well-defined oversight mechanisms. 

This SaaS solution judiciously respects the GDPR established and followed by the CNIL, avoiding the use of sensitive data within algorithms or those of service providers. For example: gender, age, and other sensitive data according to the CNIL. On a broader scale, TOP also aligns with the legal framework being established by the European Union, notably the AI Act. 

Additionally, the TOP team has implemented a dashboard for algorithm supervision, allowing users (managers, HR partners, HR managers, or directors) to monitor and make informed decisions, avoiding over-processing on certain job categories or profiles. 

TOP teams conduct a thorough analysis of societal data, which is often personal in nature, to detect any potential biases. Using their data analysis capabilities, they carefully study the impact of criteria such as gender, age, or marital status on the evolution of resignation risk prediction, ensuring that these factors do not inappropriately affect results in accordance with rigorous ethical standards. 

In the figure below, we demonstrate a monitoring dashboard for studying the impact of an ethically sensitive factor on the prediction of resignation rates. 

We can see that gender does not impact the prediction results of resignation rates from an ethical standpoint. It is observed that there is no bias, hence no influence, and the result is ethically acceptable. 

In addition to providing an ethical model, the TOP teams assist managers in using the solution, raising awareness that this SaaS is a decision support tool, intended to be used with a human perspective. 

Therefore, it is necessary today to supervise artificial intelligence models, especially in the field of human resources. Dealing with numerous personal data and societal effects, it is crucial to correlate the notion of ethics and avoid any bias that could compromise the accuracy of artificial intelligence algorithms. This is a point that the solution has integrated from the beginning to offer a performant and ethical solution. 

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