Clinical decision support systems is a collection name for a lot of Artificial Intelligence systems used in healthcare. These systems are designed to help health workers make decisions faster and make the healthcare environment as a whole more efficient. Decisions made by these systems often weigh heavy on the ethical side giving advice on what kind of care a patient receives. The ethics of these decision mean it is even more important for these systems to act fair and unbiased towards all patients. This however is not always the case though. This paper will explain and dissect the issues of unfairness within clinical decision support systems and will compare and adapt different socio-technical design methodologies to form a advice on designing for fair clinical decision support algorithms.

Machine learning algorithms were used in in the past decade to assist humans with recruitment and grades assessments in the academic field. For the most part, the algorithms either exacerbated existing biases or output unfair results. This could often be traced back to an ill-implementation of the systems in the social context. The academic admission process is defined as setting goals, locating candidates, ranking and accepting them. To properly integrate machine learning in such process, one may follow the Value Sensitive methodology, which suggests designing a technical system around a social value. This methodology takes into account the various stakeholders, values and technical solutions available. Later, the system should be iteratively improved and constantly evaluated and examined so that it still serves the core values as defined.

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.

Companies have been looking for automation in their hiring practices and Artificial Intelligence offers a solution. A popular opinion on the topic is that AI-enabled hiring will enhance talent acquisition overall, eliminating bias and bettering results. However, deployed AI hiring tools often do not offer a solution but worsen the problem by violating ethical and moral barriers. Thus, it is crucial to consider the socio-technical perspective of this topic when designing software systems integrated into hiring practices. As part of AI-enabled recruitment, CV screening software is deployed in numerous conglomerates worldwide and is the centre of attention of this paper. The theoretical results delivered via this research originate from a variety of scientific articles of a multi-disciplinary nature. This paper examines how CV screening software can be designed to estimate professional proficiency while taking into consideration the needs and moral values of the stakeholders involved. A literature study has been conducted to derive the results. The research examines three design methodologies (Systemic design, Value Sensitive Design and Human-centered design) that help the design team to address the aforementioned issues and considers their impact on the design process of a CV screening tool regarding proficiency evaluation. The results of the paper conclude that it is crucial to assemble a design team that is multi-disciplinary in nature to elicit and embed the stakeholders' values into the design of a technical system of this nature.

A significant portion of academic graduates have difficulty finding a first job after graduation. Research shows that the expectations of academic graduate job seekers and employers do not align and this graduation project confirms that job seekers and employers do not speak the same language. On the one hand, job seekers do not seem very able to communicate their skills and abilities in a convincing manner. On the other hand, employers do not seem very able to communicate the job requirements effectively. In this graduation project, Jeroen ter Haar Romenij validated and developed a vacancy platform in collaboration with the start-up HelloCareer. The platform allows academic graduate job seekers to explore job opportunities with the use of a job task language. With this task-language, HelloCareer aims to bridge the gap between educational study programs and actual job profiles on the labour market. The task language enables job seekers and employers to express their preferences, respectively for a future job and a future employee, in a uniform language, thereby reducing the asymmetry of information between the two parties, ultimately resulting in better matches. During this graduation project, a task-language for the three master programmes that are a part of the TU Delft’s faculty of Industrial Design Engineering was co-developed with academic graduate job seekers. Based on intense involvement of both the job seekers and the employers, crucial learnings were acquired on how to best define and apply the task-language and shape the design of the task-based vacancy platform. The way in which the preferences of the job seekers are represented by the platform has a direct effect on the job opportunities that are presented to them. Therefore, the value of autonomy over self-representation is highly at stake and has been put central in the development of the task-based vacancy platform. To engrain this way of thinking in the design process, a design for values approach has been chosen. Through empirical research that was conducted with academic job seekers, it has been explored what the value of autonomy over self-representation means for them in the context of the vacancy platform. As a result, these insights have shaped the design of the task-based vacancy platform which is described in this thesis. The final result is of this graduation project is a User Interface Design that demonstrates in a clear and practical manner how the task-based vacancy platform operates.