AI ethics on Friday, 1 November 2019

Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm

With societal inequalities continuing to increase and organizations providing the vast majority of people with their income, we wanted to assess the ways in which organizational practices are implicated in the burgeoning of social and economic inequality. Following an integrative review of the literature drawn from across the social sciences, we found that the multiple ways in which five major organizational practices—hiring, role allocation, promotion, compensation, and structuring—are enacted

Abstract The concept of meaningful work has recently received increased attention in philosophy and other disciplines. However, the impact of the increasing robotization of the workplace on meaningful work has received very little attention so far. Doing work that is meaningful leads to higher job satisfaction and increased worker well-being, and some argue for a right to access to meaningful work. In this paper, we therefore address the impact of robotization on meaningful work. We do so by ide