Natural language processing (NLP) tools have the potential to boost civic participation and enhance democratic processes because they can significantly increase governments' capacity to gather and analyze citizen opinions. However, their adoption in government remains limited, and harnessing their benefits while preventing unintended consequences remains a challenge. While prior work has focused on improving NLP performance, this work examines how different internal government stakeholders influence NLP tools' thoughtful adoption. We interviewed seven politicians (politically appointed officials as heads of government institutions) and thirteen public servants (career government employees who design and administrate policy interventions), inquiring how they choose whether and how to use NLP tools to support civic participation processes. The interviews suggest that policymakers across both groups focused on their needs for career advancement and the need to showcase the legitimacy and fairness of their work when considering NLP tool adoption and use. Because these needs vary between politicians and public servants, their preferred NLP features and tool designs also differ. Interestingly, despite their differing needs and opinions, neither group clearly identifies who should advocate for NLP adoption to enhance civic participation or address the unintended consequences of a poorly considered adoption. This lack of clarity in responsibility might have caused the governments' low adoption of NLP tools. We discuss how these findings reveal new insights for future HCI research. They inform the design of NLP tools for increasing civic participation efficiency and capacity, the design of other tools and methods that ensure thoughtful adoption of AI tools in government, and the design of NLP tools for collaborative use among users with different incentives and needs.

Binding precedents (S\'umulas Vinculantes) constitute a juridical instrument unique to the Brazilian legal system and whose objectives include the protection of the Federal Supreme Court against repetitive demands. Studies of the effectiveness of these instruments in decreasing the Court's exposure to similar cases, however, indicate that they tend to fail in such a direction, with some of the binding precedents seemingly creating new demands. We empirically assess the legal impact of five binding precedents, 11, 14, 17, 26 and 37, at the highest court level through their effects on the legal subjects they address. This analysis is only possible through the comparison of the Court's ruling about the precedents' themes before they are created, which means that these decisions should be detected through techniques of Similar Case Retrieval. The contributions of this article are therefore twofold: on the mathematical side, we compare the uses of different methods of Natural Language Processing -- TF-IDF, LSTM, BERT, and regex -- for Similar Case Retrieval, whereas on the legal side, we contrast the inefficiency of these binding precedents with a set of hypotheses that may justify their repeated usage. We observe that the deep learning models performed significantly worse in the specific Similar Case Retrieval task and that the reasons for binding precedents to fail in responding to repetitive demand are heterogeneous and case-dependent, making it impossible to single out a specific cause.

The deployment of AI systems for welfare benefit allocation allows for accelerated decision-making and faster provision of critical help, but has already led to an increase in unfair benefit denials and false fraud accusations. Collecting data in the US and the UK (N = 2449), we explore the public acceptability of such speed-accuracy trade-offs in populations of claimants and non-claimants. We observe a general willingness to trade off speed gains for modest accuracy losses, but this aggregate view masks notable divergences between claimants and non-claimants. Although welfare claimants comprise a relatively small proportion of the general population (e.g., 20% in the US representative sample), this vulnerable group is much less willing to accept AI deployed in welfare systems, raising concerns that solely using aggregate data for calibration could lead to policies misaligned with stakeholder preferences. Our study further uncovers asymmetric insights between claimants and non-claimants. The latter consistently overestimate claimant willingness to accept speed-accuracy trade-offs, even when financially incentivized for accurate perspective-taking. This suggests that policy decisions influenced by the dominant voice of non-claimants, however well-intentioned, may neglect the actual preferences of those directly affected by welfare AI systems. Our findings underline the need for stakeholder engagement and transparent communication in the design and deployment of these systems, particularly in contexts marked by power imbalances.

In the first four months of the Covid-19 pandemic, government leaders paid $100 million for management consultants at McKinsey to model the spread of the coronavirus and build online dashboards to project hospital capacity. It's unsurprising that leaders turned to McKinsey for help, given the notorious backwardness of government technology. Our everyday experience with online shopping and search only highlights the stark contrast between user-friendly interfaces and the frustrating inefficiencies of government websites--or worse yet, the ongoing need to visit a government office to submit forms in person. The 2016 animated movie Zootopia depicts literal sloths running the DMV, a scene that was guaranteed to get laughs given our low expectations of government responsiveness. More seriously, these doubts are reflected in the plummeting levels of public trust in government.

After ChatGPT burst on the scene last November, some government officials raced to prohibit its use. New York City, Los Angeles Unified, Seattle, and Baltimore School Districts either banned or blocked access to generative AI tools, fearing that ChatGPT, Bard, and other content generation sites could tempt students to cheat on assignments, induce rampant plagiarism, and impede critical thinking. This week, US Congress heard testimony from Sam Altman, CEO of OpenAI, and AI researcher Gary Marcus as it weighed whether and how to regulate the technology. In a rapid about-face, however, a few governments are now embracing a less fearful and more hands-on approach to AI. New York City Schools chancellor David Banks announced yesterday that NYC is reversing its ban because "the knee jerk fear and risk overlooked the potential of generative AI to support students and teachers, as well as the reality that our students are participating in and will work in a world where understanding generative AI is crucial."

The government is considering using the artificial intelligence chatbot ChatGPT in administrative work, officials said. The move is aimed at streamlining work such as preparing answers to parliamentary questions, reducing the burden on public servants. "We will pursue the possibility of using (ChatGPT) to reduce the workload of national public servants," economy minister Yasutoshi Nishimura said at a news conference Tuesday, citing the creation of responses to parliamentary questions as an example. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

John Kerry is an accomplished diplomat and a highly skilled public servant who has devoted his professional life to serving the American people and furthering the interests of his country on the international stage. Do you wish to discover his most notable accomplishments and credentials? In Massachusetts, Kerry started his career in public service as a prosecutor, where he worked to reduce crime and maintain public safety. He subsequently went on to serve for more than two decades as a senator in the US Senate, where he presided over the committee overseeing foreign relations and was a key player in establishing US foreign policy. He played a key role in crafting the Comprehensive Anti-Apartheid Act, which placed economic restrictions on the South African government in opposition to its apartheid system.

Transcript: How can AI positively transform the way we work, especially in the public sector? Over the past year and a half, we've seen a lot of different ways public servants are using AI to help them meet their mission and transform the constituent experience. AI can surface information insights more rapidly and at scale, and what's really interesting is that it can help agencies translate, "gov speak" to "people speak." AI can build that connective tissue between how people speak and how they actually live their lives and look for information, versus how government has organized it. Also, our public servants are burnt out.

They're called the Sampo Generation: South Koreans in their 20s and 30s who have given up (po) three (sam) of life's conventional rites of passage -- dating, marrying and having children. They've made these choices because of economic constraints and in the process have worsened South Korea's demographic imbalances. Last year, when the country registered more deaths than births for the first time in recent history, then-Vice Finance Minister Kim Yong-beom pronounced the milestone a "death cross." "I Live Alone" is one of South Korea's most popular reality TV shows. It follows the single lives of movie actors and K-pop singers engaging in mundane activities such as feeding their pets or eating ramen in the middle of the night -- all alone.

Donald Trump has a lot more to worry about than just Robert Mueller. That much has been clear since April, when details began to emerge from public court filings regarding the FBI raid on Trump's personal attorney, Michael Cohen, who pleaded guilty on Tuesday to a number of criminal charges, including some stemming from his work for Trump. Instead, it was carried out by FBI agents acting in coordination with Robert S. Khuzami, a deputy U.S. attorney in the Southern District of New York. Mueller had referred the Cohen case to Khuzami's office, but that was as far as his involvement apparently went. As I wrote at the time, the distribution of the investigation to a second office served to "potentially inoculate [it] from Trump's attacks against Mueller and potential meddling in the broader Russia investigation." Samuel W. Buell, the former lead Enron prosecutor, told me that would make it much more difficult to kill the investigation with a Saturday Night Massacre–style firing spree.