Exploring ethical considerations in the potential implementation of ai-driven recruitment systems in the public sector

AI-driven recruitment systems offer improved hiring outcomes and enhanced efficiency, which transform traditional recruitment methods. Public sector implementation of AI systems raises ethical concerns around transparency, fairness, accountability, and data privacy, crucial in the maintenance of public trust. Bias in AI presents risks to equitable hiring, highlighting the need for regular audits and bias-detection tools. The study proposes a framework for ethical considerations to guide just and fair AI-driven recruitment in the Philippine public sector, emphasizing diversity, social equity, and trust in public service. Ethical considerations ensure adherence to societal values and operational efficiency in public sector hiring. The study adopts Descriptive Research Design to describe the sample characteristics and area of interest. Quantitative research techniques analyzed the survey data to identify correlations between ethical considerations and effective implementation of AI-driven recruitment systems in the public sector. A survey questionnaire was used as the primary data-gathering instrument, which was tested for validity and reliability. Respondents of the study are government agency employees and HR professionals, selected by Purposive Sampling Design. Statistical tools used are percentage, frequency, mean, and standard deviation. Pearson Correlation Analysis was used to measure the significant impact of key ethical considerations on the effectiveness of the potential implementation of AI-Driven recruitment systems in the public sector. Multiple Regression Analysis measured which of the key ethical considerations significantly achieve effectiveness in the potential implementation of AI-Driven recruitment systems in the public sector. Bias, transparency, accountability, fairness, and diversity are key ethical considerations in AI-driven recruitment systems in the public sector. Bias, transparency, and diversity were found significant in the effective achievement of AI-driven recruitment systems. Accountability and fairness were not significant in AI recruitment implementation. As diversity in the Philippines is highly pronounced, considerations on bias become essential to ensure support of AI systems for equitable public service delivery. Through bias mitigation, the Philippine government can effectively promote a fair, diverse, and trusted hiring process, vital in the creation of representative and capable workforce serving the best interests of the public. Transparency is crucial for achieving success in AI-driven recruitment implementation in the Philippine public sector, specifically in the promotion of accountability, public trust, and in providing support for legal compliance, fairness, and adaptability, thus creating a robust framework for effective and ethical AI recruitment practices. The significance of transparency in the Philippine public sector lies in ensuring fairness, fostering of trust, and alignment with regulatory and ethical standards, crucial for legitimacy and accountability of the public sector. In the effective implementation of AI-driven recruitment in the public sector, diversity was found as critical factor, contributing to the promotion of social equity, inclusivity, and fairness in hiring practices. Embracing diversity ensures the alignment of diversity with the government’s mandate for the provision of equal employment opportunities and reflection of the country’s diverse linguistic, cultural, and socioeconomic backgrounds. The Philippine public sector has lower perceptions of the direct impact of accountability on effective AI-driven recruitment systems, as they put priority to efficiency, reliance on legal standards, distributed nature of responsibility, and cultural trust, lessening the immediate need for stringent measures of accountability. Accountability is believed to play a supportive rather than a central role in determining the effectiveness of AI-driven recruitment. The perspective was found not to entirely negate accountability, but rather to contextualize the relative impact on practical outcomes of implementation. AI-driven recruitment systems’ technical complexity, involving the opaque nature of machine learning models, limits traditional accountability measures, making it challenging to assign individual accountability for specific recruitment outcomes. Fairness is seen as non-significant to the effective AI-driven recruitment systems in the Philippine public sector, as efficiency, objective data, resource constraints, transparency, accountability, short-term recruitment outcomes, and merit-based selection, can take precedence over considerations of fairness. The perspective is assumed to balance societal and ethical goals for ensuring a holistic approach in public sector hiring. As the public sector considers operational efficiency as the primary measure of success, fairness becomes secondary in the achievement of recruitment objectives.