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Human-designed systems are increasingly leveraged by data-driven methods and artificial intelligence. This leads to an urgent need for responsible design and ethical use. The goal of this conceptual paper is two-fold. First, we will introduce the Framework for Design Reasoning in Data Life-cycle Ethical Management, which integrates three existing frameworks: 1) the design reasoning quadrants framework (representing engineering design research), and 2) the data life-cycle model (representing data management), and 3) the reflexive principles framework (representing ethical decision-making). The integration of three critical components of the framework (design reasoning, data reasoning, and ethical reasoning) is accomplished by centering on the conscientious negotiation of design risks and benefits. Second, we will present an example of a student design project report to demonstrate how this framework guides educators towards delineating and integrating data reasoning, ethical reasoning, and design reasoning in settings where ethical issues (e.g., AI solutions) are commonly experienced. The framework can be implemented to design courses through design review conversations that seamlessly integrate ethical reasoning into the technical and data decision-making processes. 

There have already been numerous reported computer algorithm biases that systematically discriminate against certain content, individuals, or groups and that have had serious impacts on society. This session examines an information literacy component focused on algorithm bias in the presenter’s undergraduate-level course on data management. The course meets the university’s requirements for the certificate “Applications in Data Science”. The detected cases, and perspectives of undergraduate students in regard to privacy, fairness, and ethics are shared and discussed.