Re-Shape: A Method to Teach Data Ethics for Data Science Education

Data has become central to the technologies and services that human-computer interaction (HCI) designers make, and the ethical use of data in and through these technologies should be given critical attention throughout the design process. However, there is little research on ethics education in computer science that explicitly addresses data ethics. We present and analyze Re-Shape, a method to teach students about the ethical implications of data collection and use. Re-Shape, as part of an educational environment, builds upon the idea of cultivating care and allows students to collect, process, and visualizetheir physical movement data in ways that support critical reflection and coordinated classroom activities about data, data privacy, and human-centered systems for data science. We also use a case study of Re-Shape in an undergraduate computer science course to explore prospects and limitations of instructional designs and educational technology such as Re-Shape that leverage personal data to teach data ethics

Convergence Of Numerical Method For Multistate Stochastic Dynamic Programming

Convergence of corrections is examined for a predictorcorrector method to solve Bellman equations of multi-state stochastic optimal control in continuous time. Quadratic costs and constrained control are assumed. A heuristically linearized comparison equation makes the nonlinear, discontinuous Bellman equation amenable to linear convergence analysis. Convergence is studied using the Fourier stability method. A uniform mesh ratio type condition for the convergence is results. The results are valid for both Gaussian and Poisson type stochastic noise. The convergence criteria has been extremely useful for solving the larger multi-state problems on vector supercomputers and massively parallel processors