Chenyan Jia and Taeyoung Lee

While algorithms and crowdsourcing have been increasingly used to debunk or label misinformation on social media, such tasks might be most effective when performed by professional fact checkers or journalists. Drawing on a national survey (N = 1,003), we found that U.S. adults evaluated fact-checking labels created by professional fact checkers as more effective than labels by algorithms and other users. News media labels were perceived as more effective than user labels but not statistically different from labels by fact checkers and algorithms. There was no significant difference between labels created by users and algorithms. These findings have implications for platforms and fact-checking practitioners, underscoring the importance of journalistic professionalism in fact-checking.​ Learn More >>

Briony Swire-Thompson and Skyler Johnson

Although cancer might seem like a niche subject, we argue that it is a model topic for misinformation researchers, and an ideal area of application given its importance for society. We first discuss the prevalence of cancer misinformation online and how it has the potential to cause harm. We next examine the financial incentives for those who profit from disinformation dissemination, how people with cancer are a uniquely vulnerable population, and why trust in science and medical professionals is particularly relevant to this topic. We finally discuss how belief in cancer misinformation has clear objective consequences and can be measured with treatment adherence and health outcomes such as mortality. In sum, cancer misinformation could assist the characterization of misinformation beliefs and be used to develop tools to combat misinformation in general.​ Learn More >>

Sangwon Lee, S. Mo Jones-Jang, Myojung Chung, Nuri Kim, and Jihyang Choi

Since its public launch, ChatGPT has gained the world’s attention, demonstrating the immense potential of artificial intelligence (AI). To explore factors influencing the adoption of ChatGPT, we ran structural equation modelling to test the unified theory of acceptance and use of technology model while incorporating relative risk (vs. benefit) perception and emotional factors into its original form to gain a better understanding of the process. The findings revealed that in addition to individuals’ technology-specific perceptions (i.e., performance expectancy, effort expectancy, social influence, and facilitating conditions), relative risk perception and emotional factors play significant roles in predicting favourable attitude and behaviour intentions towards ChatGPT. Learn More >>

Saiph Savage, Gabriela Avila, Norma Elva Chávez, and Martha Garcia-Murillo

In this chapter we provide an overview of the impact that AI is having in national security and the new opportunities and challenges that are emerging in the space. For this purpose, we first provide a history of technology for national security. Next, we describe how AI is being used in various national security contexts and the key actors involved in the creation and development of military AI. We go on to discuss how to design AI for the military, especially for national security. We finish by presenting an agenda for AI for national security, highlighting key areas that bear looking into across the globe. Learn More >>

Benjamin A. Miller, Kevin Chan & Tina Eliassi-Rad

Vertex classification using graph convolutional networks is susceptible to targeted poisoning attacks, in which both graph structure and node attributes can be changed in an attempt to misclassify a target node. This vulnerability decreases users’ confidence in the learning method and can prevent adoption in high-stakes contexts. Defenses have been proposed, focused on filtering edges before creating the model or aggregating information from neighbors more robustly. This paper considers an alternative: we investigate the ability to exploit network phenomena in the training data selection process to improve classifier robustness. We propose two alternative methods of selecting training data: (1) to select the highest-degree nodes and (2) to select nodes with many connections to the test data. In four real datasets, we show that changing the training set often results in far more perturbations required for a successful attack on the graph structure; often a factor of 2 over the random training baseline. We also run a simulation study in which we demonstrate conditions under which the proposed methods outperform random selection, finding that they improve performance most when homophily is higher, clustering coefficient is higher, node degrees are more homogeneous, and attributes are less informative. In addition, we show that the methods are effective when applied to adaptive attacks, alleviating concerns about generalizability.

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Elettra Bietti

Today’s digital technologies are transforming the quantification, allocation and monetization of human time and attention. Motivated by a variety of technical and social pressures, the average American spends more than eight hours a day consuming digital media on their computer or phone. Social media overuse has been held responsible for a teenage mental health crisis, a rise in teen suicides and a more general degradation of collective attention processes essential in a political economy and democracy. In the midst of the current attentional crisis, existing bodies of law such as privacy, antitrust and free speech fail to assist us in grappling with concerns about technology overuse, addiction, technology-mediated attention disorders and the pervasive degradation of our individual and collective attention. It is tempting to reduce these disorders to problems of individual choice, delegating solutions to market-based tools or the exercise of individual data protection and speech rights. Instead, the answer requires moving past simplistic views of the market as a self-correcting device guided by individual preferences, and of data within them.

This paper focuses on the role of data in producing a progressive thinning, stretching and erosion of human attention. It argues that understanding the relation between datafication and attention can pave the way toward better law and policy in this area.

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 John Wihbey and Garrett Morrow

Based on representative national samples of ~1,000 respondents per country, we assess how people in three countries, the United Kingdom, the United States, and Canada, view the use of new artificial intelligence (AI) technologies such as large language models by social media companies for the purposes of content moderation. We find that about half of survey respondents across the three countries indicate that it would be acceptable for company chatbots to start public conversations with users who appear to violate rules or platform community guidelines. Persons who have more regular experiences with consumer-facing chatbots are less likely to be worried in general about the use of these technologies on social media. However, the vast majority of persons (80%+) surveyed across all three countries worry that if companies deploy chatbots supported by generative AI and engage in conversations with users, the chatbots may not understand context, may ruin the social experience of connecting with other humans, and may make flawed decisions. This study raises questions about a potential future where humans and machines interact a great deal more as common actors on the same technical surfaces, such as social media platforms.

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Brian Ball, Alexandros Koliousis, Amil Mohanan & Mike Peacey

In this paper, we situate our computational approach to philosophy relative to other digital humanities and computational social science practices, based on reflections stemming from our research on the PolyGraphs project in social epistemology. We begin by describing PolyGraphs. An interdisciplinary project funded by the Academies (BA, RS, and RAEng) and the Leverhulme Trust, it uses philosophical simulations (Mayo-Wilson and Zollman, 2021) to study how ignorance prevails in networks of inquiring rational agents. We deploy models developed in economics (Bala and Goyal, 1998), and refined in philosophy (O’Connor and Weatherall, 2018; Zollman, 2007), to simulate communities of agents engaged in inquiry, who generate evidence relevant to the topic of their investigation and share it with their neighbors, updating their beliefs on the evidence available to them.

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Nico Neumann , Catherine E. Tucker , Levi Kaplan , Alan Mislove,      Piotr Sapiezynski

Data brokers use black-box methods to profile and segment individuals for ad targeting, often with mixed success. We present evidence from 5 complementary field tests and 15 data brokers that differences in profiling accuracy and coverage for these attributes mainly depend on who is being profiled. Consumers who are better off—for example, those with higher incomes or living in affluent areas—are both more likely to be profiled and more likely to be profiled accurately. Occupational status (white-collar versus blue-collar jobs), race and ethnicity, gender, and household arrangements often affect the accuracy and likelihood of having profile information available, although this varies by country and whether we consider online or offline coverage of profile attributes. Our analyses suggest that successful consumer-background profiling can be linked to the scope of an individual’s digital footprint from how much time they spend online and the number of digital devices they own. Those who come from lower-income backgrounds have a narrower digital footprint, leading to a “data desert” for such individuals. Vendor characteristics, including differences in profiling methods, explain virtually none of the variation in profiling accuracy for our data, but explain variation in the likelihood of who is profiled. Vendor differences due to unique networks and partnerships also affect profiling outcomes indirectly due to differential access to individuals with different backgrounds. We discuss the implications of our findings for policy and marketing practice.

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Samsun Knight, Tsung-Yiou Hsieh, and Yakov Bart

We estimate the heterogeneous effects of TV advertising on revenues of physical retail stores and restaurants in the United States for 313 brands using a novel panel of store-level revenue data and a two-way fixed effects design. We find a mean revenue elasticity to TV advertising of 0.094 and a median elasticity of 0.044, along with a significant estimated S-curvature in the marginal effect of advertising. We document significant heterogeneity in estimated effective- ness across store-level characteristics, and in particular find that advertising is more effective for stores in denser areas and for stores in areas with higher numbers of competitor locations. We then use these heterogeneity estimates to construct optimal allocations of ad expenditure across DMAs and project that these counterfactual reallocations would increase returns on advertising by a median of 4.3% and a mean of 22.1%. This study advances recent research demonstrating that TV advertising is less effective than is generally assumed by highlighting the role of suboptimal geographic targeting and by quantifying how much realized effectiveness can understate advertising’s potential effect.

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Alyssa Hasegawa Smith, Adina Gitomer, and Brooke Foucault Welles

In this work, we seek to understand how hegemonic and subversive (counter-hegemonic) stories about gender and control are constructed across and between media platforms. To do so, we examine how American singer-songwriter Britney Spears is framed in the stories that tabloid journalists, Wikipedia editors, and Twitter users tell about her online. Using Spears’ portrayal as a case study, we hope to better understand how subversive stories come to prominence online, and how platform affordances and incentives can encourage or discourage their emergence. We draw upon previous work on the portrayal of women and mental illness in news and tabloid media, as well as work on narrative formation on Wikipedia. Using computational methods and critical readings of key articles, we find that Twitter, as a source of the #FreeBritney hashtag, continually supports counter-hegemonic narratives during periods of visibility, while both the tabloid publication TMZ and Wikipedia may lag in their adoption of the same.

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Stefan McCabe, Jon Green, Pranav Goel, and  David Lazer

Members of Congress increasingly rely on social media to communicate with their constituents and other members of the public in real time. However, despite their increased use, little is known about the composition of members’ audiences in these online spaces. We address these questions using a panel of Twitter users linked to their congressional district of residence through administrative data. We provide evidence that Twitter users who followed their own representative in the 115th, 116th, and 117th Congresses were generally older and more partisan, and live in wealthier areas of those districts, compared to those who did not. We further find that shared partisanship and shared membership in historically marginalized groups are associated with an increased probability of a constituent following their congressional representative. These results suggest that the efficiency of communication offered by social media reproduces, rather than alters, patterns of political polarization and class inequalities in representation observed offline.

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 Chenyan Jia, Martin J. Riedl, and Samuel Woo

Automated tools for parsing and communicating information have increasingly become associated with the production of journalistic content. To study this phenomenon and to explore the development of automated journalism across two locales at the cutting edge of technology, we leverage insights from in-depth interviews with news technologists from pioneering news organizations and Internet companies specialized in the construction of “news bot” technology in the United States and China, including The Associated Press, The New York Times, The Atlanta Journal-Constitution, BuzzFeed, Quartz, Xinhua Zhiyun, Southern Metropolis Daily, Toutiao, and Tencent. Based on these interviews, we document how the creation of automated journalism products is heavily dependent on the successful assembly of actor networks inside and outside organizations. While metrics for measuring the success of automated journalism are applied differently, they often center around the augmentation of existing reportorial activities and focus on replacing rote and mundane (human) work processes. Some of the biggest challenges in automated journalism lie in curating high-quality datasets and managing the associated high stakes of errors in a business defined by trust. Lastly, automated journalism can be seen as a form of experimentation, helping its protagonists to future-proof their respective organizations.

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Here we represent human lives in a way that shares structural similarity to language, and we exploit this similarity to adapt natural language processing techniques to examine the evolution and predictability of human lives based on detailed event sequences. We do this by drawing on a comprehensive registry dataset, which is available for Denmark across several years, and that includes information about life-events related to health, education, occupation, income, address and working hours, recorded with day-to-day resolution. We create embeddings of life-events in a single vector space, showing that this embedding space is robust and highly structured. Our models allow us to predict diverse outcomes ranging from early mortality to personality nuances, outperforming state-of-the-art models by a wide margin. Using methods for interpreting deep learning models, we probe the algorithm to understand the factors that enable our predictions. Our framework allows researchers to discover potential mechanisms that impact life outcomes as well as the associated possibilities for personalized interventions.

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Insight into one’s own cognitive abilities is one important aspect of metacognition. Whether this insight varies between groups when discerning true and false information has yet to be examined. We investigated whether demographics like political partisanship and age were associated with discernment ability, metacognitive efficiency, and response bias for true and false news. Participants rated the veracity of true and false news headlines and provided confidence ratings for each judgment. We found that Democrats and older adults were better at discerning true and false news than Republicans and younger adults. However, all demographic groups maintained good insight into their discernment ability. Although Republicans were less accurate than Democrats, they slightly outperformed Democrats in metacognitive efficiency when a politically equated item set was used. These results suggest that even when individuals mistake misinformation to be true, they are aware that they might be wrong.

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 Rahul Bhargava

Over the last two decades quantitative data representation has moved from a specialization of the sciences, economics, and statistics, to becoming commonplace in settings of democratic governance and community decision making. The dominant norms of those fields of origin are not connected to the governance and activism settings data is now used in, where practices emphasize empowerment, efficacy, and engagement. This has created ongoing harms and exclusion in a variety of well-documented settings. In this paper I critique the singular way of knowing embodied and charts and graphs, and apply the theories of epistemological pluralism and extended epistemology to argue for a larger toolbox of data representation. Through three concrete case studies of data representations created by activists I argue that social justice movements can embrace a broader set of approaches, practicing creative data representation as epistemological resistance. Through learning from these ongoing examples the fields of data literacy, open data, and data visualization can help create a broader toolbox for data representation. This is necessary to create a pluralistic practice of bringing people together around data in social justice settings.

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Parker Bach, Adina Gitomer, Melody Devries, Christina Walker, Deen Freelon, Julia Atienza-Barthelemy, Brooke Foucault Welles, Diana Deyoe, and Diana Zulli

Though a relative newcomer among social media platforms, social video-sharing platform TikTok is one of the largest social media platforms in the world, boasting over one billion monthly active users, which it garnered in just five years (Dellatto, 2021). While much of the early attention to the platform focused on more frivolous elements, such as its dances and challenges, the political weight of TikTok has become ever clearer. In the 2020 US election, Donald Trump’s plan to fill the 19,000-seat BOK Center in Tulsa was stymied by young activists who reserved tickets with no intention of attending, organized largely on TikTok (Bandy & Diakopoulos, 2020). In the years since, political discourse on TikTok has continued to emerge from everyday users and political campaigns alike (see Newman, 2022), even as TikTok itself has become an object of political contention: calls for banning the app in the United States–citing security concerns influenced by xenophobia, given the app’s Chinese ownership–began in the Trump presidency (Allyn, 2020) and have recently culminated in state- and federal-level bans on the app for government-owned devices in the U.S. (Berman, 2023). While some studies have navigated limited data access and the platform’s relative infancy to offer an examination of political TikTok (see Literat & Kligler-Vilenchik, 2019; Medina Serrano et al., 2020; Vijay & Gekker, 2021; Guinaudeau et al., 2022), there remains a significant need for more analysis and theorization of how TikTok can become both a site for political discourse and a feature caught up within political mobilization. This panel seeks to bring together emerging work that deals with political participation on TikTok, in order to share current wisdom and forge future research directions. The presented works specifically focus on the relationship between political participation on TikTok and political identity for three primary reasons. First, as a video-based and thus embodied platform (Raun, 2012), creator identity is more prominent and easily perceptible in the visual and auditory elements of TikTok videos than in the primarily text-based posts on platforms like Twitter and Facebook. Second, TikTok relies more heavily on its recommendation algorithm for content distribution than its competitors traditionally have (Kaye et al., 2022; Cotter et al., 2022; Zeng & Kaye, 2022; Zhang & Liu, 2021), leading to the creation of “refracted publics” (Abidin, 2021) or Gemeinschaft-style communities (Kaye et al., 2022) around users’ common interests, which may include and/or be heavily informed by identity. Third, TikTok has long prioritized and found success with Generation Z and younger users more broadly (Zeng et al., 2021; Vogels et al., 2022; Stahl & Literat, 2022), which has made generational identity extremely salient on the app, while also implicating political identity, as young people tend to hold political beliefs more cognizant and accepting of diverse identities than older generations (Parker et al., 2019). The papers in this panel consider a wide range of identity characteristics of TikTok users and how these identities shape and are shaped by political discourse on TikTok. Paper 1 builds on TikTok’s targeting of Gen Z, considering the identities of age and generation through a content analysis of political remix on TikTok to uncover how younger users use TikTok for political activism as compared to their older counterparts, and finding evidence that TikTok is a powerful site of collective action. Also building from TikTok’s appeal to GenZ, Paper 2 presents a digital ethnographic analysis of the Trad-Wife phenomena on TikTok, offering that TikTok quietly (and thus insidiously) offers space for the cultivation of Christian Nationalist, ‘gentle fascisms’ within GenZ women, often without mention of ‘politics’ at all. Paper 3 offers a computational content analysis of political posts on TikTok with a focus on the interactions between identity and partisanship, and particularly the ways in which creators of marginalized identities on the right act as identity entrepreneurs, offering conservative critiques of their identity groups in ways which find popularity among conservative audiences of hegemonic identities. Finally, Paper 4 looks at differences in how TikTok users respond to male and female politicians’ TikTok videos using a combination of computational and qualitative methods, with exploratory analysis suggesting that male politicians receive more neutral and positive comments than female politicians. By focusing on identity and political discourse on TikTok, we recognize the wide range of political activity occurring on a platform often denigrated as frivolous, and foreground the importance of identity characteristics to the technological and social shaping of these dialogues.

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Samsun Knight and  Yakov Bart

How has generative artificial intelligence affected the quality and quantity of user-generated content? Our analysis of restaurant reviews on Yelp.com and product reviews on Amazon.com shows that, contrary to prior lab evidence, use of generative AI in text generation is associated with significant declines in online content quality. These results are similar both in OLS and in two- period differences-in-differences estimation based on within-reviewer changes in AI use. We also find that use of generative AI is associated with increases in per-reviewer quantity of content, and document heterogeneity in the observed effect between expert and non-expert reviewers, with the strongest declines in quality associated with use by non-experts.

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Meg Heckman, Rahul Bhargavaa, and Emily Boardman Ndulue

This study uses a mix of traditional and computational content analysis to track digital news coverage of U.S. Vice President Kamala Harris during and immediately after the 2020 general elections. It’s well documented that female politicians, especially women of color, face biased coverage in the political press. In this paper, we explore how these dynamics play out in the modern hyper-polarized digital media landscape. Our corpus includes roughly 17,000 stories published online between August 2020 and April 2021 by news organizations we categorized along a binary axis of partisanship between Republican and Democrat. Our findings show that sexist, racialized coverage of Harris is most prevalent in news sources shared by mostly Republican voters. Coverage in news sources shared by registered Democratic voters, meanwhile, tended to treat Harris as a celebrity, often fixating on her wardrobe and personal life. We ponder implications for both gender equity in civic life and future feminist media scholarship.

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Avantika Velho, Pedro Cruz, David Banks-Richardson, Gabrielle Armin, Ying Zhang, Keisuke Inomura, and Katia Zolotovsky

Microbial plankton play a critical role in global biogeochemical cycling, and while a vast amount of genomics data has been collected from microbial communities throughout the ocean, there remains a gap in connecting this data to ecosystems and biogeochemical cycles. One key question is how different genotypes influence an organism’s environmental impact? Here, we scientists and designers have collaborated to develop an interactive visual tool that helps to better understand the relationships between microbial ecosystems and global biogeochemical cycling. 

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 Tomo Lazovich, Luca Belli, Aaron Gonzales, Amanda Bower, Uthaipon Tantipongpipat, Kristian Lum, Ferenc Huszár, and Rumman Chowdhury

The harmful impacts of algorithmic decision systems have recently come into focus, with many examples of machine learning (ML) models amplifying societal biases. In this paper, we propose adapting income inequality metrics from economics to complement existing model-level fairness metrics, which focus on intergroup differences of model performance. In particular, we evaluate their ability to measure disparities between exposures that individuals receive in a production recommendation system, the Twitter algorithmic timeline. We define desirable criteria for metrics to be used in an operational setting by ML practitioners. We characterize engagements with content on Twitter using these metrics and use the results to evaluate the metrics with respect to our criteria. We also show that we can use these metrics to identify content suggestion algorithms that contribute more strongly to skewed outcomes between users. Overall, we conclude that these metrics can be a useful tool for auditing algorithms in production settings.

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  Myojung Chung

While there has been a growing call for insights on algorithms given their impact on what people encounter on social media, it remains unknown how enhanced algorithmic knowledge serves as a countermeasure to problematic information flow. To fill this gap, this study aims to investigate how algorithmic knowledge predicts people’s attitudes and behaviors regarding misinformation through the lens of the third-person effect.

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David Gray Grant, Jeff Behrends, and   John Basl

The ongoing explosion of interest in artificial intelligence is fueled in part by recently developed techniques in machine learning. Those techniques allow automated systems to process huge amounts of data, utilizing mathematical methods that depart from traditional statistical approaches, and resulting in impressive advancements in our ability to make predictions and uncover correlations across a host of interesting domains. But as is now widely discussed, the way that those systems arrive at their outputs is often opaque, even to the experts who design and deploy them. Is it morally problematic to make use of opaque automated methods when making high-stakes decisions, like whether to issue a loan to an applicant, or whether to approve a parole request? Many scholars answer in the affirmative. However, there is no widely accepted explanation for why transparent systems are morally preferable to opaque systems. We argue that the use of automated decision-making systems sometimes violates duties of consideration that are owed by decision-makers to decision-subjects, duties that are both epistemic and practical in character. Violations of that kind generate a weighty consideration against the use of opaque decision systems. In the course of defending our approach, we show that it is able to address three major challenges sometimes leveled against attempts to defend the moral import of transparency in automated decision-making.

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  Elettra Bietti

Antitrust law is more contested than ever. The recent push by the Biden Administration to re-orient antitrust towards justice and fairness considerations is leading to public backlash, judicial resistance and piecemeal doctrinal developments. The methodological hegemony of welfare maximizing moves in antitrust makes it theoretically fragile and maladaptive to change. To bridge disagreements and overcome polarization, this Article revisits John Rawls’ foundational work on political and economic justice, arguing that it can facilitate consensus and inform the present and future of antitrust law.

John Rawls’ work on justice can help re-orient antitrust law in four ways. First, it provides a common language of justice that enables various “camps” of thinkers and activists find common ground on the relation between antitrust’s economic mission and its implications for political justice. Second, it helps recalibrate the balance between utilitarian moves that emphasize welfare maximization on the one hand and deontological or structural justice considerations on the other. Third, and related, Rawls guides our thinking on the relationship between justice, efficiency and welfare, showing us that attempts at separating antitrust law’s mission from its impact on liberty, equality and fairness violates basic moral intuitions. Fourth, Rawls’ late work on political economy, particularly notions of property-owning democracy and liberal democratic socialism, can help situate antitrust law within broader policy thinking on economic justice.

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Michelle N. Meyer,  John Basl,   David Choffnes,  Christo Wilson, and   David Lazer

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Annika Marie Schoene, John Ortega,  Silvio Amir, and Kenneth Church

This work starts with the TWISCO baseline, a benchmark of suicide-related content from Twitter. We find that hyper-parameter tuning can improve this baseline by 9%. We examined 576 combinations of hyper-parameters: learning rate, batch size, epochs and date range of training data. Reasonable settings of learning rate and batch size produce better results than poor settings. Date range is less conclusive. Balancing the date range of the training data to match the benchmark ought to improve performance, but the differences are relatively small. Optimal settings of learning rate and batch size are much better than poor settings, but optimal settings of date range are not that different from poor settings of date range. Finally, we end with concerns about reproducibility. Of the 576 experiments, 10% produced F1 performance above baseline. It is common practice in the literature to run many experiments and report the best, but doing so may be risky, especially given the sensitive nature of Suicide Ideation Detection.

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Embedding ethics modules within computer science courses has become a popular response to the growing recognition that computer science programs need to better equip their students to navigate the ethical dimensions of computing technologies such as artificial intelligence, machine learning, and big data analytics. However, the popularity of this approach has outpaced the evidence of its positive outcomes. To help close that gap, this empirical study reports positive results from Northeastern University’s program that embeds values analysis modules into computer science courses. The resulting data suggest that such modules have a positive effect on students’ moral attitudes and that students leave the modules believing they are more prepared to navigate the ethical dimensions they will likely face in their eventual careers. Importantly, these gains were accomplished at an institution without a philosophy doctoral program, suggesting this strategy can be effectively employed by a wider range of institutions than many have thought.

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Brain-computer interfaces (BCI) are an important mode of alternative and augmentative communication for many people. Unlike keyboards, many BCI systems do not display even the 26 letters of English at one time, let alone all the symbols in more complex systems. Using language models to make character-level predictions, therefore, can greatly speed up BCI typing (Ghosh and Kristensson, 2017). While most existing BCI systems employ character n-gram models or no LM at all, this paper adapts several wordpiece-level Transformer LMs to make character predictions and evaluates them on typing tasks. GPT-2 fares best on clean text, but different LMs react differently to noisy histories. We further analyze the effect of character positions in a word and context lengths. 

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Internet-of-Things (IoT) devices are ubiquitous, but little attention has been paid to how they may incorporate dark patterns despite consumer protections and privacy concerns arising from their unique access to intimate spaces and always-on capabilities. This paper conducts a systematic investigation of dark patterns in 57 popular, diverse smart home devices. We update manual interaction and annotation methods for the IoT context, then analyze dark pattern frequency across device types, manufacturers, and interaction modalities. We find that dark patterns are pervasive in IoT experiences, but manifest in diverse ways across device traits. Speakers, doorbells, and camera devices contain the most dark patterns, with manufacturers of such devices (Amazon and Google) having the most dark patterns compared to other vendors. We investigate how this distribution impacts the potential for consumer exposure to dark patterns, discuss broader implications for key stakeholders like designers and regulators, and identify opportunities for future dark patterns study..

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In 2021, the U.S. Hispanic population totaled 62.5 million people, 68% of whom spoke Spanish in their homes. To date, it is unclear which political advertisers address this audience in their preferred language, and whether they do so differently than for English-speaking audiences. In this work, we study differences between political Facebook ads in English and Spanish during 2020, the latest U.S. presidential election. Political advertisers spent $ 1.48 B in English, but only $ 28.8 M in Spanish, disproportionately little compared to the share of Spanish speakers in the population. We further find a lower proportion of election-related advertisers (which additionally are more liberal-leaning than in the English set), and a higher proportion of government agencies in the set of Spanish ads. We perform multilingual topic classification, finding that the most common ad topics in English were also present in Spanish, but to a different extent, and with a different composition of advertisers. Thus, Spanish speakers are served different types of ads from different types of advertisers than English speakers, and in lower amounts; these results raise the question of whether political communication through Facebook ads may be inequitable and effectively disadvantaging the sizeable minority of Spanish speakers in the U.S. population.

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The powers and possibilities of artificial intelligence (AI) are boundless, as computer scientists and philosophers formalize novel cognitive and moral capabilities, progressively expanding the boundaries of our imaginations. This monumental technological revolution will disrupt the global order and social hierarchies, and generate winners and losers among those who steer the telos of AI to either protect vested interests or uphold social justice. Our greatest challenge as a species in wielding—or yielding to—these new intelligent systems will be to stipulate the establishment of ethical governance structures which prioritize and protect freedom and fairness. What’s at stake is nothing less than the future of humanity—one in which software code and ethical codes fight for supremacy to shape our lives.

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