With the increasing use of artificial intelligence in human resource management, decisions such as recruitment, performance evaluation, promotion, and employee rewards have become increasingly dependent on algorithms and data-driven models. When employees do not have a clear understanding of how these systems make decisions, they may feel distrustful, or threatened in their position. Therefore, given the importance of the topic, this study was conducted to investigate the effect of transparency and explainability of artificial intelligence algorithms in human resource systems on employee cooperation intentions. This study is applied in terms of purpose and descriptive, survey, and correlational in nature and method. Its statistical population consists of employees of Ferdows Hospital, and 240 people were selected using the sample size formula. Cronbach's alpha was used to assess the reliability of the questionnaire, and the reliability of all criteria was higher than 0.7 and the entire questionnaire was 0.92, and therefore the reliability of the questionnaire was confirmed. Content and construct validity (confirmatory factor analysis) was used to examine the validity of the questionnaire. Data analysis was performed using SPSS24 software and Smart PLS4.0 software. The findings from the hypothesis testing showed that the model has a good fit and all hypotheses were confirmed. The results of the hypothesis testing showed that after examining the fit of the measurement and structural models, the test of the research hypotheses showed that the transparency of artificial intelligence algorithms has a positive and significant effect on the intention to cooperate (β=0.42, t=2.72) and the explainability of artificial intelligence algorithms has a positive and significant effect on the intention to cooperate (β=0.88, t=27.92). In the mediation relationships, trust in AI systems plays a mediating role in the relationship between AI algorithm transparency and cooperation intention (β=0.35, t=2.78), and perceived justice plays a mediating role in the relationship between AI algorithm explainability and cooperation intention (β=0.28, t=2.14). In addition, digital self-efficacy plays a moderating role in the relationship between AI algorithm transparency and cooperation intention (β=0.25, t=2.43), while the moderating role of digital self-efficacy in the relationship between AI algorithm explainability and cooperation intention was not confirmed.