Automatic classification of human values: Applying computational thinking to information ethics

Abstract

This paper describes an ongoing interdisciplinary collaboration between researchers in information ethics and information retrieval who seek to automate the process of detecting and classifying values expressed in human communication.This effort is an example of computational thinking (Wing, 2006) in practice, as it involves applying computational techniques to the field of ethics, which has previously been thought of as a purely humanistic endeavor.As this paper explains, automatic classification has potential benefits relative to the surveys and manual content analysis that have previously been used to study human values, with significant promise to revolutionize both information ethics and artificial intelligence.Historically, surveys have been used to examine the values of different individuals, groups, organizations, professions, and cultures (e.g., Inglehart, 2008;Rokeach, 1967;Schwartz, 1992).However, surveys have several limitations.First, in all human subjects research conducted under Institutional Review Board (IRB) protocols, surveys involve voluntary participation, leading to participation bias.Since not everyone who is asked to participate in a survey need do so, true random sampling is not feasible, making representative sampling highly unlikely, and opening the possibility that there are unmeasured systematic biases in terms of who does and does not participate.Second, as in all research that involves direct questioning, survey participants may not answer questions accurately, leading to response bias.Inaccurate answers may result from insufficient reflection, retrospective bias (in which subsequent events change past perceptions), self-deception, or conscious or subconscious decisions to withhold information or sugarcoat answers.Due to these limitations, it is problematic to rely entirely on surveys to understand human values and how they may impact the design and use of information technologies. Content analysis provides an alternative (or supplement) to survey research on human values.Content analysis can be used to produce open-or closed-coding of human values in recorded communication, and can be undertaken at the word, phrase, sentence, multiple-sentence, paragraph, or document level.A trained coder can identify statements motivated by values and which values are present in those statements.A coder may be able to detect the presence of values that might have entered into the speech or writing of an individual at a subconscious level, or which an individual might not choose to express directly, allowing for more robust detection of values than in a survey.However, manual content analysis suffers from significant limitations and tradeoffs.First, manually coding text is time-consuming, making large-scale research projects costly (often prohibitively costly in less well funded research areas).Second, human coders are subject to various degrees of bias and inconsistency (for example, reflective adjustment of the coding process can sometimes be beneficial, but recoding previously coded documents can be even more time-consuming, and failing to recognize the need to recode earlier documents can yield inconsistent results).While much can be learned through manual content analysis, its costs and limitations restrict the utility of the method for large data sets.Automatic content analysis can yield many of the benefits of content analysis while addressing some of the limitations (Cheng et al., 2008).Our research team has trained computers to both 15508390,

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