Resources for Teaching Ethics and Computing

The National Science Foundation\u27s Undergraduate Faculty Enhancement program sponsored two workshops devoted to developing resource materials that could be used in teaching ethics and computing. Participants in the workshops were faculty who teach in undergraduate Information Systems, Computer Science, or Computer Engineering programs. The teaching resources developed through the workshops are available to faculty through a web site http://marathon.csee.usf.edu/~kwb/nsf-ufe/. The web site contains over 50 model class exercises, reviews of videos that might be used in teaching, and additional resources

Pornography on the Dean\u27s PC: An Ethics and Computing Case Study

A real case study in which a technician discovers pornography on an administrator\u27s personal computer is developed for use in teaching ethics and computing. The case highlights issues of employee rights and responsibilities in using employer-owned computing resources, competing responsibilities in professional codes of ethics, claims about rights to privacy and free speech, and ethical decision-making. Analysis of the case emphasizes the need for strong critical thinking skills

A Real Balanced Dataset For Understanding Bias? Factors That Impact Accuracy, Not Numbers of Identities and Images

The issue of disparities in face recognition accuracy across demographic groups has attracted increasing attention in recent years. Various face image datasets have been proposed as 'fair' or 'balanced' to assess the accuracy of face recognition algorithms across demographics. While these datasets often balance the number of identities and images across demographic groups. It is important to note that the number of identities and images in an evaluation dataset are not the driving factors for 1-to-1 face matching accuracy. Moreover, balancing the number of identities and images does not ensure balance in other factors known to impact accuracy, such as head pose, brightness, and image quality. We demonstrate these issues using several recently proposed datasets. To enhance the capacity for less biased evaluations, we propose a bias-aware toolkit that facilitates the creation of cross-demographic evaluation datasets balanced on factors mentioned in this paper

Reducing Effects of Plagiarism in Programming Classes

Large programming classes are traditionally an area of concern for maintaining the integrity of the educational process. Systematic inspection of all program solutions for evidence of plagiarism can be done using an automated tool. The Measure Of Software Similarity tool developed by Alex Aiken at the University of California at Berkeley analyzes a set of programs to detect evidence of “duplicates.” However, experience in applying this sort of plagiarism detection in a large programming class indicates that the main long-term effect may be to simply shift the source of plagiarism. This possibility leads to considering the reason for fighting plagiarism and then to exploring additional techniques aimed at reducing the perceived motivation for plagiarism