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In the construction industry, the advent of teleoperation and robotic technologies is revolutionizing traditional recruitment prac-tices, introducing new criteria for identifying qualified workers. This evolution presents significant challenges for employers aiming to recruitworkers who can maximize organizational utility. Although contract theory offers a promising solution to these challenges, its inherent self-disclosure property could inadvertently lead to privacy breaches, such as revealing gender-related information. Such disclosure risk mightintensify existing biases, notably gender bias, within the sector. To this end, we proposed deep reinforcement learning (DRL)-based contracttheory. Firstly, the trained DRL model will produce unpredictable contract bundles, restricting employers’ access to workers’ privacy. Sub-sequently, to ensure employers adopt DRL-based contract theory, we utilized blockchain to supervise contract bundle generation. Finally,given that the DRL models are homogenous among employers, we integrated transfer learning to reduce unnecessary overhead. Simulationexperiments conducted using US labor force statistical data demonstrated that our work can effectively mitigate potential gender bias byaugmenting the contract selection rights for female workers from 72.73% and 60% to 96.97% and 95% in comparison with traditionalcontract theory while maximizing employers’ utility. In addition, with the integration of transfer learning, the training overhead ofDRL-based contract theory can decrease by 50%. The meaning and significance of the results lie in the innovative integration of contracttheory, deep reinforcement learning, and transfer learning into the recruitment framework, significantly advancing the body of knowledge inunbiased workforce development. DOI: 10.1061/JCEMD4.COENG-15330. © 2024 American Society of Civil Engineers. 

Energy justice advocates for the equitable and accessible provision of energy services, mainly focusing on marginalized communities. Adopting machine learning in analyzing energy-related data can unintentionally reinforce social inequalities. This perspective highlights the stages in the machine learning process where biases may emerge, from data collection and model development to deployment. Each phase presents distinct challenges and consequences, ultimately influencing the fairness and accuracy of machine learning models. The ramifications of machine learning bias within the energy sector are profound, encompassing issues such as inequalities, the perpetuation of negative feedback loops, privacy concerns regarding, and economic impacts arising from energy burden and energy poverty. Recognizing and rectifying these biases is imperative for leveraging technology to advance society rather than perpetuating existing injustices. Addressing biases at the intersection of energy justice and machine learning requires a comprehensive approach, acknowledging the interconnectedness of social, economic, and technological factors.