Artificial Intelligence (AI) is widely used in hiring practices to identify the most suitable candidate for a vacancy. This is due to the promise of higher overall efficiency and lower costs. However, these AI-powered tools may create an inaccurate conception of the applicant's suitability to the vacancy by numerically quantifying context-dependent variables. If only this inaccurate conception is used to judge an applicant, this is a violation of the applicant's autonomy over their self-representation. This paper argues that such a problem could be solved by adopting a broader design scope - Socio-Technical Systems Design (STSD). STSD approaches have not been widely applied to AI or hiring practices yet. Therefore the main contribution of this paper is to bridge this gap by exploring possible STSD approaches which can be applied to ensure the applicants' autonomy over self-representation. This paper suggests combining methodologies and design principles from two STSD approaches - Design for Values and Systemic Design. The findings from Design for Values suggest that key stakeholders should be involved in the design process. Therefore, the designers should conduct a stakeholder analysis to identify the key stakeholders, followed by an investigation to explore the stakeholders' needs, and the values which the proposed system could implicate. Systemic Design offers design principles that should be utilized during the design process. These principles consist of: expanding the problem space; focus on the relationship between the key stakeholders; and follow an iterative, experimental, and evolutionary design approach throughout the design process. This nuanced, stakeholder-centric approach results in an inclusive, transparent, multi-discipline, and socially aware process which is necessary to understand the complex social context, and its socio-ethical issues.