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Government use of algorithmic decision-making (ADM) systems is widespread and diverse, and holding these increasingly high-impact, often opaque government algorithms accountable presents a number of challenges. Some European governments have launched registries of ADM systems used in public services, and some transparency initiatives exist for algorithms in specific areas of the United States government; however, the U.S. lacks an overarching registry that catalogs algorithms in use for public-service delivery throughout the government. This paper conducts an inductive thematic analysis of over 700 government ADM systems cataloged by the Algorithm Tips database in an effort to describe the various ways government algorithms might be understood and inform downstream uses of such an algorithmic catalog. We describe the challenge of government algorithm accountability, the Algorithm Tips database and method for conducting a thematic analysis, and the themes of topics and issues, levels of sophistication, interfaces, and utilities of U.S. government algorithms that emerge. Through these themes, we contribute several different descriptions of government algorithm use across the U.S. and at federal, state, and local levels which can inform stakeholders such as journalists, members of civil society, or government policymakers 

This research shows that members of different ideological groups in the United States can use different search terms when looking for information about political candidates, but that difference is not enough to yield divergent search results on Google. Search engines are central in information seeking during elections, and have important implications for the distribution of information and, by extension, for democratic society. Using a method involving surveys, qualitative coding, and quantitative analysis of search terms and search results, we show that the sources of information that are returned by Google for both liberal and conservative search terms are strongly correlated. We collected search terms from people with different ideological positions about Senate candidates in the 2018 midterm election from the two main parties in the U.S., in three large and politically distinct states: California, Ohio, and Texas. We then used those search terms to scrape web results and analyze them. Our analysis shows that, in terms of the differences arising from individual search term choices, Google results exhibit a mainstreaming effect that partially neutralizes differentiation of search behaviors, by providing a set of common results, even to dissimilar searches. Based on this analysis, this article offers two main contributions: first, in the development of a method for determining group-level differences based on search input bias; and second, in demonstrating how search engines respond to diverse information seeking behavior and whether that may have implications for public discourse. 

This paper presents an algorithm audit of the Google Top Stories box, a prominent component of search engine results and powerful driver of traffic to news publishers. As such, it is important in shaping user attention towards news outlets and topics. By analyzing the number of appearances of news article links we contribute a series of novel analyses that provide an in-depth characterization of news source diversity and its implications for attention via Google search. We present results indicating a considerable degree of source concentration (with variation among search terms), a slight exaggeration in the ideological skew of news in comparison to a baseline, and a quantification of how the presentation of items translates into traffic and attention for publishers. We contribute insights that underscore the power that Google wields in exposing users to diverse news information, and raise important questions and opportunities for future work on algorithmic news curation.