Calculating error rates for filtering software

  • Authors:
  • Paul J. Resnick;Derek L. Hansen;Caroline R. Richardson

  • Affiliations:
  • University of Michigan School of Information;University of Michigan School of Information;University of Michigan Medical School and VA Health Services Research and Development Center, Ann Arbor, Michigan

  • Venue:
  • Communications of the ACM - End-user development: tools that empower users to create their own software solutions
  • Year:
  • 2004

Quantified Score

Hi-index 0.00

Visualization

Abstract

Surveys in the U.S. have found that 95% of schools [4], 43% of public libraries [5], and 33% of teenagers' parents [8] employ filtering software to block access to pornography and other inappropriate content. Many products are also now available to filter out spam email.Filtering software, however, cannot perfectly discriminate between allowed and forbidden content, resulting in two types of errors. First, under-blocking occurs when content is not blocked that should be restricted. Second, over-blocking occurs when content is blocked that should not have been restricted. Steps can be taken to reduce the frequency of errors, and to reduce their costs (for example, by providing easy appeals processes, quick overrides, and corrections) but some errors are inevitable.