School of Information

Silicon Valley in California and the Hsinchu-Taipei region of Taiwan are among the most frequently cited ‘miracles’of the information technology era. The dominant accounts of these successes treat them in isolation, focusing either on free markets, multinationals or the state. This paper argues that the dynamism of these regional economies is attributable to their increasing interdependencies. A community of US-educated Taiwanese engineers has coordinated a decentralized process of reciprocal industrial upgrading by transferring capital, skill, and know-how and by facilitating collaboration between specialist producers in the two regions. This case underscores the significance of technical communities and their institutions in diffusing ideas and organizing production at the global as well as the local level.

As generative artificial intelligence (AI) continues its ballistic trajectory, everything from text to audio, image, and video generation continues to improve at mimicking human-generated content. Through a series of perceptual studies, we report on the realism of AI-generated voices in terms of identity matching and naturalness. We find human participants cannot consistently identify recordings of AI-generated voices. Specifically, participants perceived the identity of an AI-generated voice to be the same as its real counterpart approximately [Formula: see text] of the time, and correctly identified a voice as AI generated only about [Formula: see text] of the time.

Checklist of writings by Philip Olin Keeney (1891-1962).

Numerous studies have reported an increase in hate speech on X (formerly Twitter) in the months immediately following Elon Musk's acquisition of the platform on October 27th, 2022; relatedly, despite Musk's pledge to "defeat the spam bots," a recent study reported no substantial change in the concentration of inauthentic accounts. However, it is not known whether any of these trends endured. We address this by examining material posted on X from the beginning of 2022 through June 2023, the period that includes Musk's full tenure as CEO. We find that the increase in hate speech just before Musk bought X persisted until at least May of 2023, with the weekly rate of hate speech being approximately 50% higher than the months preceding his purchase, although this increase cannot be directly attributed to any policy at X. The increase is seen across multiple dimensions of hate, including racism, homophobia, and transphobia. Moreover, there is a doubling of hate post "likes," indicating increased engagement with hate posts. In addition to measuring hate speech, we also measure the presence of inauthentic accounts on the platform; these accounts are often used in spam and malicious information campaigns. We find no reduction (and a possible increase) in activity by these users after Musk purchased X, which could point to further negative outcomes, such as the potential for scams, interference in elections, or harm to public health campaigns. Overall, the long-term increase in hate speech, and the prevalence of potentially inauthentic accounts, are concerning, as these factors can undermine safe and democratic online environments, and increase the risk of offline harms.

With drastic changes to abortion policy, the months following the Dobbs leak and subsequent decision in 2022 were a uniquely uncertain and difficult time for abortion access in the United States. To understand experiences of challenges to abortion access during that time, we used a hybrid inductive and deductive thematic coding approach to analyse descriptions of barriers and their impacts shared in an abortion subreddit (r/abortion). A simple random sample of 10% of posts was obtained from those shared from 02 May 2022 through 23 December 2022; comments were purposively sampled during the coding process. In this sample of submissions (n = 523 posts, 88 comments), people described structural barriers identified in past research, including state abortion bans and gestational limits, high costs, limited appointment availability, and long travel required. Posters also commonly described known social barriers, including limited social support and abortion stigma. Several impactful barriers not well-described in past research emerged inductively, including wait time for receiving mail-ordered abortion medication, low credibility of online ordering platforms, and concerns about legal risks of accessing abortion or related medical care. The most common consequences of experiencing barriers were adverse mental health outcomes, delayed access to care, and being compelled to self-manage their abortion because of access barriers. This analysis provides timely insights into the experiences and impacts of abortion access barriers in a group of people with a range of engagement with clinical abortion care, lived experiences, and points in their abortion processes, with public health implications for mental health and abortion access.

Personal mobility data from mobile phones and other sensors are increasingly used to inform policymaking during pandemics, natural disasters, and other humanitarian crises. However, even aggregated mobility traces can reveal private information about individual movements to potentially malicious actors. This paper develops and tests an approach for releasing private mobility data, which provides formal guarantees over the privacy of the underlying subjects. Specifically, we (1) introduce an algorithm for constructing differentially private mobility matrices and derive privacy and accuracy bounds on this algorithm; (2) use real-world data from mobile phone operators in Afghanistan and Rwanda to show how this algorithm can enable the use of private mobility data in two high-stakes policy decisions: pandemic response and the distribution of humanitarian aid; and (3) discuss practical decisions that need to be made when implementing this approach, such as how to optimally balance privacy and accuracy. Taken together, these results can help enable the responsible use of private mobility data in humanitarian response.

Machine learning (ML)-driven diagnosis systems are particularly relevant in pediatrics given the well-documented impact of early-life health conditions on later-life outcomes. Yet, early identification of diseases and their subsequent impact on length of hospital stay for this age group has so far remained uncharacterized, likely because access to relevant health data is severely limited. Thanks to a confidential data use agreement with the California Department of Health Care Access and Information, we introduce Ped-BERT: a state-of-the-art deep learning model that accurately predicts the likelihood of 100+ conditions and the length of stay in a pediatric patients next medical visit. We link mother-specific pre- and postnatal period health information to pediatric patient hospital discharge and emergency room visits. Our data set comprises 513.9K mother-baby pairs and contains medical diagnosis codes, length of stay, as well as temporal and spatial pediatric patient characteristics, such as age and residency zip code at the time of visit. Following the popular bidirectional encoder representations from the transformers (BERT) approach, we pre-train Ped-BERT via the masked language modeling objective to learn embedding features for the diagnosis codes contained in our data. We then continue to fine-tune our model to accurately predict primary diagnosis outcomes and length of stay for a pediatric patients next visit, given the history of previous visits and, optionally, the mothers pre- and postnatal health information. We find that Ped-BERT generally outperforms contemporary and state-of-the-art classifiers when trained with minimum features. We also find that incorporating mother health attributes leads to significant improvements in model performance overall and across all patient subgroups in our data. Our most successful Ped-BERT model configuration achieves an area under the receiver operator curve (ROC AUC) of 0.927 and an average precision score (APS) of 0.408 for the diagnosis prediction task, and a ROC AUC of 0.855 and APS of 0.815 for the length of hospital stay task. Further, we examine Ped-BERTs fairness by determining whether prediction errors are evenly distributed across various subgroups of mother-baby demographics and health characteristics, or if certain subgroups exhibit a higher susceptibility to prediction errors.

Purpose: The purpose of this paper is to provide a new and useful formulation of relevance. Design/methodology/approach: This paper is formulated as a conceptual argument. It makes the case for the utility of considering relevance to be function of use in creative processes. Findings: There are several corollaries to formulating relevance as a function of use. These include the idea that objects by themselves cannot be relevant since use assumes interaction; the affordances of objects and how they are perceived can affect what becomes relevant but are not in themselves relevant; relevance is not an essential characteristic of objects; relevance is transient; potential relevance (what might be relevant in the future) can be distinguished from what is relevant in use and from what has been relevant in the past. Originality/value: The paper shows that its new formulation of relevance brings improved conceptual and terminological clarity to the discourse about relevance in information science. It demonstrates that how relevance is articulated conceptually is important as its conceptualization can affect the ways that users are able to make use of information systems and, by extension, how information systems can facilitate or disable the co-production of creative outcomes. The paper also usefully expands investigative opportunities by suggesting relevance and creativity are interrelated.