Abstract
In this article we consider automated journalism from the perspective of bias in news text. We describe how systems for automated journalism could be biased in terms of both the information content and the lexical choices in the text, and what mechanisms allow human biases to affect automated journalism even if the data the system operates on is considered neutral. Hence, we sketch out three distinct scenarios differentiated by the technical transparency of the systems and the level of cooperation of the system operator, affecting the choice of methods for investigating bias. We identify methods for diagnostics in each of the scenarios and note that one of the scenarios is largely identical to investigating bias in non-automatically produced texts. As a solution to this last scenario, we suggest the construction of a simple news generation system, which could enable a type of analysis-by-proxy. Instead of analyzing the system, to which the access is limited, one would generate an approximation of the system which can be accessed and analyzed freely. If successful, this method could also be applied to analysis of human-written texts. This would make automated journalism not only a target of bias diagnostics, but also a diagnostic device for identifying bias in human-written news.
Highlights
In the current news media landscape, examining and acknowledging underlying bias is an important step in strengthening newswork and rectifying trust in journalism
We have briefly described what automated journalism is, including a description of the two archetypical technical methods to conduct news automation: rulebased and based on machine learning
We have identified two major categories of bias that can appear in the output of such systems: content bias and language bias
Summary
In the current news media landscape, examining and acknowledging underlying bias is an important step in strengthening newswork and rectifying trust in journalism. Media and Communication, 2020, Volume 8, Issue 3, Pages 39–49 tomated journalism through the technology employed In their view automated journalism is about the employment of Natural Language Generation methods for producing news text. We synthesize how these methods and ideas apply to diagnosing automated journalism itself for bias They would quite naturally be of interest to researchers, as they would increase our understanding of the news media They would be of interest to thirdparty interest groups as a method for highlighting potential biases against any one of multiple demographics. Distinguishing between ‘acceptable’ bias, such as exhibited in personalized sports news, and ‘unacceptable’ bias, e.g., favoring certain ethnicities, is a value ridden process Both are examples of ‘selectivity,’ as suggested by Hofstetter and Buss Due to the effects of media on audience perceptions, consciousness of bias and embedded values in automated journalism is of paramount importance
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