Studying human-to-computer bias transference

It is generally agreed that one origin of machine bias is resulting from characteristics within the dataset on which the algorithms are trained, i.e., the data does not warrant a generalized inference. We, however, hypothesize that a different ‘mechanism’ may also be responsible for machine bias, namely that biases may originate from (i) the programmers’ cultural background, including education or line of work, or (ii) the contextual programming environment, including software requirements or developer tools. Combining an experimental and comparative design, we study the effects of cultural and contextual metaphors, and test whether each of these are ‘transferred’ from the programmer to the program, thus constituting a machine bias. Our results show that (i) cultural metaphors influence the programmer’s choices and (ii) contextual metaphors induced through priming can be used to moderate or exacerbate the effects of the cultural metaphors. Our studies are purposely performed with users of varying educational backgrounds and programming skills stretching from novice to proficient.

Generalized Inference on Stress-Strength Reliability in Generalized Pareto Model

The article focuses on the inference of stress-strength reliability in generalized Pareto model using the generalized variable approach and bootstrap percentile method. Simulation studies are conducted to obtain expected lengths and coverage probabilities of confidence intervals constructed using the generalized variable and the bootstrap percentile methods. An example consisting of real stress-strength data is also presented for illustrative purposes.

Detecting Changes under Multivariate Normal Distributions via the Generalized Inference

It is commonly encountered in many fields to detect whether a change occurs on a population after a special process. Based on observations for describing the population before and after the process, we formulate this problem as two statistical hypotheses testing problems within a framework of multivariate statistical analysis and then propose a generalized inference approach to solve them. The corresponding generalized p values and their calculation details are provided. The proposed method is also extended to multiple testing problems. Simulation studies show that the proposed p values have satisfactory frequentist performance. We illustrate our methods with a real application in manufacturing of bearings that are used in medical devices.