This article summarizes two initiatives for artificial intelligence (AI) underway in the Canadian public service: public consultation and collaboration in compiling an algorithmic impact assessment, and a symposium on AI and human rights held by Global Affairs Canada. The findings contextualize the national consultations on digital and data transformation and future steps for more inclusive AI governance in Canada.Cet article offre une synthèse de deux initiatives sur l’intelligence artificielle (IA) en cours dans la fonction publique canadienne : la consultation et collaboration du public dans la compilation d’une évaluation d’impact algorithmique et un symposium sur l’IA et les droits de la personne organisée par Affaires mondiales Canada. Les conclusions permettent de donner un contexte aux consultations nationales sur la transformation du numérique et des données et les mesures à prendre pour une gouvernance en intelligence artificielle plus inclusive au Canada.

Congressional candidates regularly turn their frustration into posts on Facebook, fueling extreme partisanship and “echo-chamber” dialogue with their negative sentiment. In this research, we provide new evidence demonstrating the power of that negative sentiment to elicit more user engagement on Facebook across various metrics, illustrating how congressional candidates’ use of negativity corresponds with greater negativity in public responses. To fully comprehend the impact of these online political messages, we use a dictionary-based computational approach to catalog the tone of US House of Representatives candidates’ messages on Facebook and the user responses they elicit during the 2020 election. This research speaks to the power of elite rhetoric to shape political climates and pairs candidate strategies with user responses—contributing new insights into the mechanisms for voter engagement.

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How does the lack of institutional legislative and political power and influence in the House of Representatives shape politicians' rhetoric? In previous work, we found evidence that members of Congress in the minority party in the House and in the party opposing the president were more negative in the language that they used on Twitter. This pattern was even stronger when they were a part of the minority in a unified congress. In this project, we dive deeper into their negative tweets and outline different ways that they can use negative language, such as by attacking other politicians or branches of government or stating policy critiques, and theorize under what conditions we expect representatives to be more likely to use them. We offer a plan of how to leverage almost 2 million unique tweets made by representatives from 2013-2018 (soon to be brought up to date to 2020) to assess the impact of a representative's political power, or relative lack thereof, on her use of different methods of strategic negative sentiment in her tweets. Given the increasing contention between the two parties on-and offline, it is unlikely that the use of negative rhetoric and its potentially harmful impact on American government and congress will decrease in the near future. By better understanding what representatives say on Twitter, we can better understand the impacts of their public statements on the platform.

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