854 results on '"Levy, Karen"'
Search Results
2. Title Page, Copyright
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Levy, Karen
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- 2023
3. Bibliography
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Levy, Karen
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- 2023
4. Index
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Levy, Karen
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5. Notes
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Levy, Karen
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- 2023
6. Appendix B: Notes on the Organization of the Trucking Industry
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Levy, Karen
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- 2023
7. 8. Technology, Enforcement, and Apparent Order
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Levy, Karen
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- 2023
8. Appendix A: Studying Surveillance
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Levy, Karen
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- 2023
9. 5. Computers in the Coop
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Levy, Karen
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- 2023
10. 6. Beating the Box: How Truckers Resist Being Monitored
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Levy, Karen
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- 2023
11. 3. Tired Truckers and the Rise of Electronic Surveillance
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Levy, Karen
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- 2023
12. 4. The Business of Trucker Surveillance
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Levy, Karen
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- 2023
13. 7. RoboTruckers: The Double Threat of AI for Low-Wage Work
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Levy, Karen
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- 2023
14. 2. If the Wheel Ain't Turnin', You Ain't Earnin': Trucker Politics, Economics, and Culture
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Levy, Karen
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- 2023
15. 1. Introduction
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Levy, Karen
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- 2023
16. Cover
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Levy, Karen
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- 2023
17. "Automation is a Myth" by Luke Munn (review)
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Levy, Karen
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- 2023
18. Participation in the age of foundation models
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Suresh, Harini, Tseng, Emily, Young, Meg, Gray, Mary L., Pierson, Emma, and Levy, Karen
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Computer Science - Computers and Society ,Computer Science - Artificial Intelligence ,Computer Science - Human-Computer Interaction ,Computer Science - Machine Learning - Abstract
Growing interest and investment in the capabilities of foundation models has positioned such systems to impact a wide array of public services. Alongside these opportunities is the risk that these systems reify existing power imbalances and cause disproportionate harm to marginalized communities. Participatory approaches hold promise to instead lend agency and decision-making power to marginalized stakeholders. But existing approaches in participatory AI/ML are typically deeply grounded in context - how do we apply these approaches to foundation models, which are, by design, disconnected from context? Our paper interrogates this question. First, we examine existing attempts at incorporating participation into foundation models. We highlight the tension between participation and scale, demonstrating that it is intractable for impacted communities to meaningfully shape a foundation model that is intended to be universally applicable. In response, we develop a blueprint for participatory foundation models that identifies more local, application-oriented opportunities for meaningful participation. In addition to the "foundation" layer, our framework proposes the "subfloor'' layer, in which stakeholders develop shared technical infrastructure, norms and governance for a grounded domain, and the "surface'' layer, in which affected communities shape the use of a foundation model for a specific downstream task. The intermediate "subfloor'' layer scopes the range of potential harms to consider, and affords communities more concrete avenues for deliberation and intervention. At the same time, it avoids duplicative effort by scaling input across relevant use cases. Through three case studies in clinical care, financial services, and journalism, we illustrate how this multi-layer model can create more meaningful opportunities for participation than solely intervening at the foundation layer., Comment: 13 pages, 2 figures. Appeared at FAccT '24
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- 2024
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19. Use large language models to promote equity
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Pierson, Emma, Shanmugam, Divya, Movva, Rajiv, Kleinberg, Jon, Agrawal, Monica, Dredze, Mark, Ferryman, Kadija, Gichoya, Judy Wawira, Jurafsky, Dan, Koh, Pang Wei, Levy, Karen, Mullainathan, Sendhil, Obermeyer, Ziad, Suresh, Harini, and Vafa, Keyon
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Computer Science - Computers and Society - Abstract
Advances in large language models (LLMs) have driven an explosion of interest about their societal impacts. Much of the discourse around how they will impact social equity has been cautionary or negative, focusing on questions like "how might LLMs be biased and how would we mitigate those biases?" This is a vital discussion: the ways in which AI generally, and LLMs specifically, can entrench biases have been well-documented. But equally vital, and much less discussed, is the more opportunity-focused counterpoint: "what promising applications do LLMs enable that could promote equity?" If LLMs are to enable a more equitable world, it is not enough just to play defense against their biases and failure modes. We must also go on offense, applying them positively to equity-enhancing use cases to increase opportunities for underserved groups and reduce societal discrimination. There are many choices which determine the impact of AI, and a fundamental choice very early in the pipeline is the problems we choose to apply it to. If we focus only later in the pipeline -- making LLMs marginally more fair as they facilitate use cases which intrinsically entrench power -- we will miss an important opportunity to guide them to equitable impacts. Here, we highlight the emerging potential of LLMs to promote equity by presenting four newly possible, promising research directions, while keeping risks and cautionary points in clear view.
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- 2023
20. Dosimetric calibration of an anatomically specific ultra-high dose rate electron irradiation platform for preclinical FLASH radiobiology experiments
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Wang, Jinghui, Melemenidis, Stavros, Manjappa, Rakesh, Viswanathan, Vignesh, Ashraf, Ramish M., Levy, Karen, Skinner, Lawrie, Soto, Luis A., Chow, Stephanie, Lau, Brianna, Ko, Ryan B., Graves, Edward E., Yu, Amy S., Bush, Karl K., Surucu, Murat, Rankin, Erinn B., Loo Jr, Billy W., Schüler, Emil, and Maxim, Peter G.
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Physics - Medical Physics - Abstract
We characterized the dosimetric properties of a clinical linear accelerator configured to deliver ultra-high dose rate (UHDR) irradiation to mice and cell-culture FLASH radiobiology experiments. UHDR electron beams were controlled by a microcontroller and relay interfaced with the respiratory gating system. We produced beam collimators with indexed stereotactic mouse positioning devices to provide anatomically specific preclinical treatments. Treatment delivery was monitored directly with an ionization chamber, and charge measurements were correlated with radiochromic film at the entry surface of the mice. The setup for conventional (CONV) dose rate irradiation was similar but the source-to-surface distance was longer. Monte Carlo simulations and film dosimetry were used to characterize beam properties and dose distributions. The mean electron beam energies before the flattening filter were 18.8 MeV (UHDR) and 17.7 MeV (CONV), with corresponding values at the mouse surface of 17.2 MeV and 16.2 MeV. The charges measured with an external ion chamber were linearly correlated with the mouse entrance dose. Use of relay gating for pulse control initially led to a delivery failure rate of 20% ($+/-$ 1 pulse); adjustments to account for the linac latency improved this rate to <1/20. Beam field sizes for two anatomically specific mouse collimators (4x4 $cm^2$ for whole-abdomen and 1.5x1.5 $cm^2$ for unilateral lung irradiation) were accurate within <5% and had low radiation leakage (<4%). Normalizing the dose at the center of the mouse (~0.75 cm depth) produced UHDR and CONV doses to the irradiated volumes with >95% agreement. We successfully configured a clinical linear accelerator for increased output and developed a robust preclinical platform for anatomically specific irradiation, with highly accurate and precise temporal and spatial dose delivery, for both CONV and UHDR applications., Comment: Jinghui Wang and Stavros Melemenidis are co-first authors, and Emil Sch\"uler and Peter G. Maxim are co-senior/co-corresponding authors
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- 2023
21. Strategic Evaluation: Subjects, Evaluators, and Society
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Laufer, Benjamin, Kleinberg, Jon, Levy, Karen, and Nissenbaum, Helen
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Computer Science - Computers and Society ,Computer Science - Computer Science and Game Theory ,Computer Science - Machine Learning - Abstract
A broad current application of algorithms is in formal and quantitative measures of murky concepts -- like merit -- to make decisions. When people strategically respond to these sorts of evaluations in order to gain favorable decision outcomes, their behavior can be subjected to moral judgments. They may be described as 'gaming the system' or 'cheating,' or (in other cases) investing 'honest effort' or 'improving.' Machine learning literature on strategic behavior has tried to describe these dynamics by emphasizing the efforts expended by decision subjects hoping to obtain a more favorable assessment -- some works offer ways to preempt or prevent such manipulations, some differentiate 'gaming' from 'improvement' behavior, while others aim to measure the effort burden or disparate effects of classification systems. We begin from a different starting point: that the design of an evaluation itself can be understood as furthering goals held by the evaluator which may be misaligned with broader societal goals. To develop the idea that evaluation represents a strategic interaction in which both the evaluator and the subject of their evaluation are operating out of self-interest, we put forward a model that represents the process of evaluation using three interacting agents: a decision subject, an evaluator, and society, representing a bundle of values and oversight mechanisms. We highlight our model's applicability to a number of social systems where one or two players strategically undermine the others' interests to advance their own. Treating evaluators as themselves strategic allows us to re-cast the scrutiny directed at decision subjects, towards the incentives that underpin institutional designs of evaluations. The moral standing of strategic behaviors often depend on the moral standing of the evaluations and incentives that provoke such behaviors., Comment: 12 pages, 2 figures, EAAMO 2023
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- 2023
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22. On the Actionability of Outcome Prediction
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Liu, Lydia T., Barocas, Solon, Kleinberg, Jon, and Levy, Karen
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Computers and Society - Abstract
Predicting future outcomes is a prevalent application of machine learning in social impact domains. Examples range from predicting student success in education to predicting disease risk in healthcare. Practitioners recognize that the ultimate goal is not just to predict but to act effectively. Increasing evidence suggests that relying on outcome predictions for downstream interventions may not have desired results. In most domains there exists a multitude of possible interventions for each individual, making the challenge of taking effective action more acute. Even when causal mechanisms connecting the individual's latent states to outcomes is well understood, in any given instance (a specific student or patient), practitioners still need to infer -- from budgeted measurements of latent states -- which of many possible interventions will be most effective for this individual. With this in mind, we ask: when are accurate predictors of outcomes helpful for identifying the most suitable intervention? Through a simple model encompassing actions, latent states, and measurements, we demonstrate that pure outcome prediction rarely results in the most effective policy for taking actions, even when combined with other measurements. We find that except in cases where there is a single decisive action for improving the outcome, outcome prediction never maximizes "action value", the utility of taking actions. Making measurements of actionable latent states, where specific actions lead to desired outcomes, considerably enhances the action value compared to outcome prediction, and the degree of improvement depends on action costs and the outcome model. This analysis emphasizes the need to go beyond generic outcome prediction in interventional settings by incorporating knowledge of plausible actions and latent states., Comment: 14 pages, 3 figures
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- 2023
23. Digital Security and Reproductive Rights: Lessons for Feminist Cyberlaw
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Meister, Michela, primary and Levy, Karen, additional
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- 2024
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24. Informational Diversity and Affinity Bias in Team Growth Dynamics
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Heidari, Hoda, Barocas, Solon, Kleinberg, Jon, and Levy, Karen
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Computer Science - Computer Science and Game Theory ,Computer Science - Artificial Intelligence ,Computer Science - Computers and Society ,Computer Science - Machine Learning ,Economics - General Economics - Abstract
Prior work has provided strong evidence that, within organizational settings, teams that bring a diversity of information and perspectives to a task are more effective than teams that do not. If this form of informational diversity confers performance advantages, why do we often see largely homogeneous teams in practice? One canonical argument is that the benefits of informational diversity are in tension with affinity bias. To better understand the impact of this tension on the makeup of teams, we analyze a sequential model of team formation in which individuals care about their team's performance (captured in terms of accurately predicting some future outcome based on a set of features) but experience a cost as a result of interacting with teammates who use different approaches to the prediction task. Our analysis of this simple model reveals a set of subtle behaviors that team-growth dynamics can exhibit: (i) from certain initial team compositions, they can make progress toward better performance but then get stuck partway to optimally diverse teams; while (ii) from other initial compositions, they can also move away from this optimal balance as the majority group tries to crowd out the opinions of the minority. The initial composition of the team can determine whether the dynamics will move toward or away from performance optimality, painting a path-dependent picture of inefficiencies in team compositions. Our results formalize a fundamental limitation of utility-based motivations to drive informational diversity in organizations and hint at interventions that may improve informational diversity and performance simultaneously.
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- 2023
25. Proactive Moderation of Online Discussions: Existing Practices and the Potential for Algorithmic Support
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Schluger, Charlotte, Chang, Jonathan P., Danescu-Niculescu-Mizil, Cristian, and Levy, Karen
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Computer Science - Computers and Society ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language ,Computer Science - Human-Computer Interaction ,Physics - Physics and Society - Abstract
To address the widespread problem of uncivil behavior, many online discussion platforms employ human moderators to take action against objectionable content, such as removing it or placing sanctions on its authors. This reactive paradigm of taking action against already-posted antisocial content is currently the most common form of moderation, and has accordingly underpinned many recent efforts at introducing automation into the moderation process. Comparatively less work has been done to understand other moderation paradigms -- such as proactively discouraging the emergence of antisocial behavior rather than reacting to it -- and the role algorithmic support can play in these paradigms. In this work, we investigate such a proactive framework for moderation in a case study of a collaborative setting: Wikipedia Talk Pages. We employ a mixed methods approach, combining qualitative and design components for a holistic analysis. Through interviews with moderators, we find that despite a lack of technical and social support, moderators already engage in a number of proactive moderation behaviors, such as preemptively intervening in conversations to keep them on track. Further, we explore how automation could assist with this existing proactive moderation workflow by building a prototype tool, presenting it to moderators, and examining how the assistance it provides might fit into their workflow. The resulting feedback uncovers both strengths and drawbacks of the prototype tool and suggests concrete steps towards further developing such assisting technology so it can most effectively support moderators in their existing proactive moderation workflow., Comment: 27 pages, 3 figures. More info at https://www.cs.cornell.edu/~cristian/Proactive_Moderation.html
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- 2022
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26. Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report
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Littman, Michael L., Ajunwa, Ifeoma, Berger, Guy, Boutilier, Craig, Currie, Morgan, Doshi-Velez, Finale, Hadfield, Gillian, Horowitz, Michael C., Isbell, Charles, Kitano, Hiroaki, Levy, Karen, Lyons, Terah, Mitchell, Melanie, Shah, Julie, Sloman, Steven, Vallor, Shannon, and Walsh, Toby
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Computer Science - Artificial Intelligence - Abstract
In September 2021, the "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the second report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. It was written by a panel of 17 study authors, each of whom is deeply rooted in AI research, chaired by Michael Littman of Brown University. The report, entitled "Gathering Strength, Gathering Storms," answers a set of 14 questions probing critical areas of AI development addressing the major risks and dangers of AI, its effects on society, its public perception and the future of the field. The report concludes that AI has made a major leap from the lab to people's lives in recent years, which increases the urgency to understand its potential negative effects. The questions were developed by the AI100 Standing Committee, chaired by Peter Stone of the University of Texas at Austin, consisting of a group of AI leaders with expertise in computer science, sociology, ethics, economics, and other disciplines., Comment: 82 pages, https://ai100.stanford.edu/gathering-strength-gathering-storms-one-hundred-year-study-artificial-intelligence-ai100-2021-study
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- 2022
27. Fast or Accurate? Governing Conflicting Goals in Highly Autonomous Vehicles
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Cooper, A. Feder and Levy, Karen
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Computer Science - Computers and Society - Abstract
The tremendous excitement around the deployment of autonomous vehicles (AVs) comes from their purported promise. In addition to decreasing accidents, AVs are projected to usher in a new era of equity in human autonomy by providing affordable, accessible, and widespread mobility for disabled, elderly, and low-income populations. However, to realize this promise, it is necessary to ensure that AVs are safe for deployment, and to contend with the risks AV technology poses, which threaten to eclipse its benefits. In this Article, we focus on an aspect of AV engineering currently unexamined in the legal literature, but with critical implications for safety, accountability, liability, and power. Specifically, we explain how understanding the fundamental engineering trade-off between accuracy and speed in AVs is critical for policymakers to regulate the uncertainty and risk inherent in AV systems. We discuss how understanding the trade-off will help create tools that will enable policymakers to assess how the trade-off is being implemented. Such tools will facilitate opportunities for developing concrete, ex ante AV safety standards and conclusive mechanisms for ex post determination of accountability after accidents occur. This will shift the balance of power from manufacturers to the public by facilitating effective regulation, reducing barriers to tort recovery, and ensuring that public values like safety and accountability are appropriately balanced., Comment: Vol. 20, pp. 249-277
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- 2022
28. An Uncommon Task: Participatory Design in Legal AI
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Delgado, Fernando, Barocas, Solon, and Levy, Karen
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Computer Science - Computers and Society ,Computer Science - Artificial Intelligence ,Computer Science - Human-Computer Interaction ,Computer Science - Information Retrieval - Abstract
Despite growing calls for participation in AI design, there are to date few empirical studies of what these processes look like and how they can be structured for meaningful engagement with domain experts. In this paper, we examine a notable yet understudied AI design process in the legal domain that took place over a decade ago, the impact of which still informs legal automation efforts today. Specifically, we examine the design and evaluation activities that took place from 2006 to 2011 within the TeXT Retrieval Conference's (TREC) Legal Track, a computational research venue hosted by the National Institute of Standards and Technologies. The Legal Track of TREC is notable in the history of AI research and practice because it relied on a range of participatory approaches to facilitate the design and evaluation of new computational techniques--in this case, for automating attorney document review for civil litigation matters. Drawing on archival research and interviews with coordinators of the Legal Track of TREC, our analysis reveals how an interactive simulation methodology allowed computer scientists and lawyers to become co-designers and helped bridge the chasm between computational research and real-world, high-stakes litigation practice. In analyzing this case from the recent past, our aim is to empirically ground contemporary critiques of AI development and evaluation and the calls for greater participation as a means to address them.
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- 2022
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29. Global Health Impacts for Economic Models of Climate Change: A Systematic Review and Meta-Analysis
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Cromar, Kevin R, Anenberg, Susan C, Balmes, John R, Fawcett, Allen A, Ghazipura, Marya, Gohlke, Julia M, Hashizume, Masahiro, Howard, Peter, Lavigne, Eric, Levy, Karen, Madrigano, Jaime, Martinich, Jeremy A, Mordecai, Erin A, Rice, Mary B, Saha, Shubhayu, Scovronick, Noah C, Sekercioglu, Fatih, Svendsen, Erik R, Zaitchik, Benjamin F, and Ewart, Gary
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Biomedical and Clinical Sciences ,Cardiovascular Medicine and Haematology ,Clinical Sciences ,Climate-Related Exposures and Conditions ,Health and social care services research ,8.2 Health and welfare economics ,Climate Action ,Good Health and Well Being ,Air Pollutants ,Air Pollution ,Climate Change ,Global Health ,Greenhouse Gases ,Humans ,Models ,Economic ,climate change ,economic models ,social cost of greenhouse gases ,mortality ,temperature ,Cardiovascular medicine and haematology ,Clinical sciences - Abstract
Rationale: Avoiding excess health damages attributable to climate change is a primary motivator for policy interventions to reduce greenhouse gas emissions. However, the health benefits of climate mitigation, as included in the policy assessment process, have been estimated without much input from health experts. Objectives: In accordance with recommendations from the National Academies in a 2017 report on approaches to update the social cost of greenhouse gases (SC-GHG), an expert panel of 26 health researchers and climate economists gathered for a virtual technical workshop in May 2021 to conduct a systematic review and meta-analysis and recommend improvements to the estimation of health impacts in economic-climate models. Methods: Regionally resolved effect estimates of unit increases in temperature on net all-cause mortality risk were generated through random-effects pooling of studies identified through a systematic review. Results: Effect estimates and associated uncertainties varied by global region, but net increases in mortality risk associated with increased average annual temperatures (ranging from 0.1% to 1.1% per 1°C) were estimated for all global regions. Key recommendations for the development and utilization of health damage modules were provided by the expert panel and included the following: not relying on individual methodologies in estimating health damages; incorporating a broader range of cause-specific mortality impacts; improving the climate parameters available in economic models; accounting for socioeconomic trajectories and adaptation factors when estimating health damages; and carefully considering how air pollution impacts should be incorporated in economic-climate models. Conclusions: This work provides an example of how subject-matter experts can work alongside climate economists in making continued improvements to SC-GHG estimates.
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- 2022
30. Characterizing Behaviors Associated with Enteric Pathogen Exposure among Infants in Rural Ecuador through Structured Observations
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Sosa-Moreno, Andrea, Lee, Gwenyth O, Van Engen, Amanda, Sun, Kelly, Uruchima, Jessica, Kwong, Laura H, Ludwig-Borycz, Elizabeth, Caruso, Bethany A, Cevallos, William, Levy, Karen, and Eisenberg, Joseph NS
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Pediatric ,Infectious Diseases ,Clinical Research ,Rural Health ,2.2 Factors relating to the physical environment ,Aetiology ,Medical and Health Sciences ,Tropical Medicine - Abstract
The relative importance of environmental pathways that results in enteropathogen transmission may vary by context. However, measurement of contact events between individuals and the environment remains a challenge, especially for infants and young children who may use their mouth and hands to explore their environment. Using a mixed-method approach, we combined 1) semistructured observations to characterize key behaviors associated with enteric pathogen exposure and 2) structured observations using Livetrak, a customized software application, to quantify the frequency and duration of contacts events among infants in rural Ecuador. After developing and iteratively piloting the structured observation instrument, we loaded the final list of prompts onto a LiveTrak pallet to assess environmental exposures of 6-month infants (N = 19) enrolled in a prospective cohort study of diarrheal disease. Here we provide a detailed account of the lessons learned. For example, in our field site, 1) most mothers reported washing their hands after diaper changes (14/18, 77.8%); however only a third (4/11, 36.4%) were observed washing their hands; 2) the observers noted that animal ownership differed from observed animal exposure because animals owned by neighboring households were reported during the observation; and 3) using Livetrak, we found that infants frequently mouthed their hands (median = 1.9 episodes/hour, median duration: 1.6 min) and mouthed surroundings objects (1.8 episodes/hour, 1.9 min). Structured observations that track events in real time, can complement environmental sampling, quantitative survey data and qualitative interviews. Customizing these observations enabled us to quantify enteric exposures most relevant to our rural Ecuadorian context.
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- 2022
31. Algorithmic Auditing and Social Justice: Lessons from the History of Audit Studies
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Vecchione, Briana, Barocas, Solon, and Levy, Karen
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Computer Science - Computers and Society ,K.4.0 ,K.4.1 ,K.4.2 - Abstract
Algorithmic audits have been embraced as tools to investigate the functioning and consequences of sociotechnical systems. Though the term is used somewhat loosely in the algorithmic context and encompasses a variety of methods, it maintains a close connection to audit studies in the social sciences--which have, for decades, used experimental methods to measure the prevalence of discrimination across domains like housing and employment. In the social sciences, audit studies originated in a strong tradition of social justice and participatory action, often involving collaboration between researchers and communities; but scholars have argued that, over time, social science audits have become somewhat distanced from these original goals and priorities. We draw from this history in order to highlight difficult tensions that have shaped the development of social science audits, and to assess their implications in the context of algorithmic auditing. In doing so, we put forth considerations to assist in the development of robust and engaged assessments of sociotechnical systems that draw from auditing's roots in racial equity and social justice.
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- 2021
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32. Publisher Correction: Artificial intelligence in communication impacts language and social relationships
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Hohenstein, Jess, Kizilcec, Rene F., DiFranzo, Dominic, Aghajari, Zhila, Mieczkowski, Hannah, Levy, Karen, Naaman, Mor, Hancock, Jeffrey, and Jung, Malte F.
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- 2023
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33. Artificial intelligence in communication impacts language and social relationships
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Hohenstein, Jess, Kizilcec, Rene F., DiFranzo, Dominic, Aghajari, Zhila, Mieczkowski, Hannah, Levy, Karen, Naaman, Mor, Hancock, Jeffrey, and Jung, Malte F.
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- 2023
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34. Algorithms and Decision-Making in the Public Sector
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Levy, Karen, Chasalow, Kyla, and Riley, Sarah
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Computer Science - Computers and Society - Abstract
This article surveys the use of algorithmic systems to support decision-making in the public sector. Governments adopt, procure, and use algorithmic systems to support their functions within several contexts -- including criminal justice, education, and benefits provision -- with important consequences for accountability, privacy, social inequity, and public participation in decision-making. We explore the social implications of municipal algorithmic systems across a variety of stages, including problem formulation, technology acquisition, deployment, and evaluation. We highlight several open questions that require further empirical research.
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- 2021
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35. Computer Vision and Conflicting Values: Describing People with Automated Alt Text
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Hanley, Margot, Barocas, Solon, Levy, Karen, Azenkot, Shiri, and Nissenbaum, Helen
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Computer Science - Computers and Society ,Computer Science - Artificial Intelligence ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Scholars have recently drawn attention to a range of controversial issues posed by the use of computer vision for automatically generating descriptions of people in images. Despite these concerns, automated image description has become an important tool to ensure equitable access to information for blind and low vision people. In this paper, we investigate the ethical dilemmas faced by companies that have adopted the use of computer vision for producing alt text: textual descriptions of images for blind and low vision people, We use Facebook's automatic alt text tool as our primary case study. First, we analyze the policies that Facebook has adopted with respect to identity categories, such as race, gender, age, etc., and the company's decisions about whether to present these terms in alt text. We then describe an alternative -- and manual -- approach practiced in the museum community, focusing on how museums determine what to include in alt text descriptions of cultural artifacts. We compare these policies, using notable points of contrast to develop an analytic framework that characterizes the particular apprehensions behind these policy choices. We conclude by considering two strategies that seem to sidestep some of these concerns, finding that there are no easy ways to avoid the normative dilemmas posed by the use of computer vision to automate alt text.
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- 2021
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36. On Modeling Human Perceptions of Allocation Policies with Uncertain Outcomes
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Heidari, Hoda, Barocas, Solon, Kleinberg, Jon, and Levy, Karen
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Computer Science - Computers and Society - Abstract
Many policies allocate harms or benefits that are uncertain in nature: they produce distributions over the population in which individuals have different probabilities of incurring harm or benefit. Comparing different policies thus involves a comparison of their corresponding probability distributions, and we observe that in many instances the policies selected in practice are hard to explain by preferences based only on the expected value of the total harm or benefit they produce. In cases where the expected value analysis is not a sufficient explanatory framework, what would be a reasonable model for societal preferences over these distributions? Here we investigate explanations based on the framework of probability weighting from the behavioral sciences, which over several decades has identified systematic biases in how people perceive probabilities. We show that probability weighting can be used to make predictions about preferences over probabilistic distributions of harm and benefit that function quite differently from expected-value analysis, and in a number of cases provide potential explanations for policy preferences that appear hard to motivate by other means. In particular, we identify optimal policies for minimizing perceived total harm and maximizing perceived total benefit that take the distorting effects of probability weighting into account, and we discuss a number of real-world policies that resemble such allocational strategies. Our analysis does not provide specific recommendations for policy choices, but is instead fundamentally interpretive in nature, seeking to describe observed phenomena in policy choices.
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- 2021
37. Artificial intelligence in communication impacts language and social relationships
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Hohenstein, Jess, DiFranzo, Dominic, Kizilcec, Rene F., Aghajari, Zhila, Mieczkowski, Hannah, Levy, Karen, Naaman, Mor, Hancock, Jeff, and Jung, Malte
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Computer Science - Human-Computer Interaction ,Computer Science - Artificial Intelligence ,H.5.m - Abstract
Artificial intelligence (AI) is now widely used to facilitate social interaction, but its impact on social relationships and communication is not well understood. We study the social consequences of one of the most pervasive AI applications: algorithmic response suggestions ("smart replies"). Two randomized experiments (n = 1036) provide evidence that a commercially-deployed AI changes how people interact with and perceive one another in pro-social and anti-social ways. We find that using algorithmic responses increases communication efficiency, use of positive emotional language, and positive evaluations by communication partners. However, consistent with common assumptions about the negative implications of AI, people are evaluated more negatively if they are suspected to be using algorithmic responses. Thus, even though AI can increase communication efficiency and improve interpersonal perceptions, it risks changing users' language production and continues to be viewed negatively., Comment: 11 pages, 6 figures
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- 2021
38. Representativeness in Statistics, Politics, and Machine Learning
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Chasalow, Kyla and Levy, Karen
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Computer Science - Computers and Society - Abstract
Representativeness is a foundational yet slippery concept. Though familiar at first blush, it lacks a single precise meaning. Instead, meanings range from typical or characteristic, to a proportionate match between sample and population, to a more general sense of accuracy, generalizability, coverage, or inclusiveness. Moreover, the concept has long been contested. In statistics, debates about the merits and methods of selecting a representative sample date back to the late 19th century; in politics, debates about the value of likeness as a logic of political representation are older still. Today, as the concept crops up in the study of fairness and accountability in machine learning, we need to carefully consider the term's meanings in order to communicate clearly and account for their normative implications. In this paper, we ask what representativeness means, how it is mobilized socially, and what values and ideals it communicates or confronts. We trace the concept's history in statistics and discuss normative tensions concerning its relationship to likeness, exclusion, authority, and aspiration. We draw on these analyses to think through how representativeness is used in FAccT debates, with emphasis on data, shift, participation, and power., Comment: Accepted to ACM FAccT 2021 https://facctconference.org/2021/acceptedpapers.html
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- 2021
39. Accuracy-Efficiency Trade-Offs and Accountability in Distributed ML Systems
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Cooper, A. Feder, Levy, Karen, and De Sa, Christopher
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Computer Science - Computers and Society ,Computer Science - Machine Learning - Abstract
Trade-offs between accuracy and efficiency pervade law, public health, and other non-computing domains, which have developed policies to guide how to balance the two in conditions of uncertainty. While computer science also commonly studies accuracy-efficiency trade-offs, their policy implications remain poorly examined. Drawing on risk assessment practices in the US, we argue that, since examining these trade-offs has been useful for guiding governance in other domains, we need to similarly reckon with these trade-offs in governing computer systems. We focus our analysis on distributed machine learning systems. Understanding the policy implications in this area is particularly urgent because such systems, which include autonomous vehicles, tend to be high-stakes and safety-critical. We 1) describe how the trade-off takes shape for these systems, 2) highlight gaps between existing US risk assessment standards and what these systems require to be properly assessed, and 3) make specific calls to action to facilitate accountability when hypothetical risks concerning the accuracy-efficiency trade-off become realized as accidents in the real world. We close by discussing how such accountability mechanisms encourage more just, transparent governance aligned with public values.
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- 2020
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40. Privacy threats in intimate relationships
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Levy, Karen and Schneier, Bruce
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Computer Science - Computers and Society - Abstract
This article provides an overview of intimate threats: a class of privacy threats that can arise within our families, romantic partnerships, close friendships, and caregiving relationships. Many common assumptions about privacy are upended in the context of these relationships, and many otherwise effective protective measures fail when applied to intimate threats. Those closest to us know the answers to our secret questions, have access to our devices, and can exercise coercive power over us. We survey a range of intimate relationships and describe their common features. Based on these features, we explore implications for both technical privacy design and policy, and offer design recommendations for ameliorating intimate privacy risks.
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- 2020
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41. Roles for Computing in Social Change
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Abebe, Rediet, Barocas, Solon, Kleinberg, Jon, Levy, Karen, Raghavan, Manish, and Robinson, David G.
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Computer Science - Computers and Society - Abstract
A recent normative turn in computer science has brought concerns about fairness, bias, and accountability to the core of the field. Yet recent scholarship has warned that much of this technical work treats problematic features of the status quo as fixed, and fails to address deeper patterns of injustice and inequality. While acknowledging these critiques, we posit that computational research has valuable roles to play in addressing social problems -- roles whose value can be recognized even from a perspective that aspires toward fundamental social change. In this paper, we articulate four such roles, through an analysis that considers the opportunities as well as the significant risks inherent in such work. Computing research can serve as a diagnostic, helping us to understand and measure social problems with precision and clarity. As a formalizer, computing shapes how social problems are explicitly defined --- changing how those problems, and possible responses to them, are understood. Computing serves as rebuttal when it illuminates the boundaries of what is possible through technical means. And computing acts as synecdoche when it makes long-standing social problems newly salient in the public eye. We offer these paths forward as modalities that leverage the particular strengths of computational work in the service of social change, without overclaiming computing's capacity to solve social problems on its own.
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- 2019
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42. Distribution of blaCTX-M-gene variants in E. coli from different origins in Ecuador
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Valenzuela, Xavier, Hedman, Hayden, Villagomez, Alma, Cardenas, Paul, Eisenberg, Joseph N.S., Levy, Karen, Zhang, Lixin, and Trueba, Gabriel
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- 2023
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43. Opportunities to Interrupt Transmission of Enteropathogens of Poultry Origin in Maputo, Mozambique: A Transmission Model Analysis
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Shioda, Kayoko, Brouwer, Andrew F., Lamar, Frederica, Mucache, Hermogenes N., Levy, Karen, and Freeman, Matthew C.
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Medical research -- Health aspects ,Medicine, Experimental -- Health aspects ,Meat -- Health aspects -- Contamination ,Diarrhea -- Health aspects ,Poultry industry -- Health aspects ,Infection -- Health aspects ,Disease transmission -- Health aspects ,Food contamination -- Health aspects ,Environmental issues ,Health ,World Health Organization - Abstract
Background: The burden of diarrheal diseases remains high among children in low- income countries. Enteropathogens are challenging to control because they are transmitted via multiple pathways. Chickens are an important animal protein source, but live chickens and their products are often highly contaminated with enteropathogens. Objectives: We conducted this study to a) understand the contribution of multiple transmission pathways to the force of infection of Campylobacter spp. and nontyphoidal Salmonella spp., b) quantify the potential impact of reducing each pathway on human infection, and c) quantify hypothesized pathway reduction from the context of Maputo, Mozambique. Methods: We developed transmission models for Campylobacter and Salmonella that captured person-to-person, water- to- person, food-to-person, soil-to-person, animal-to-person, and all-other-sources-to-person in an urban, low-income setting in Mozambique. We calibrated these models using prevalence data from Maputo, Mozambique and estimates of attributable fraction of transmission pathways for the region. We simulated the prevalence of human infection after reducing transmission through each pathway. Results: Simulation results indicated that if foodborne transmission were reduced by 90%, the prevalence of Campylobacter and Salmonella infection would decline by [52.2%; 95% credible interval (CrI): 39.7, 63.8] and (46.9%; 95% CrI: 39, 55.4), respectively. Interruption of any other pathway did not have a substantial impact. Combined with survey and microbiology data, if contamination of broiler chicken meat at informal markets in Maputo could be reduced by 90%, the total infection of Campylobacter and Salmonella could be reduced by 21% (16-26%) and 12% (10- 13%), respectively. Discussion: Our transmission models showed that the foodborne transmission has to be reduced to control enteropathogen infections in our study site, and likely in other similar contexts, but mitigation of this transmission pathway has not received sufficient attention. Our model can serve as a tool to identify effective mitigation opportunities to control zoonotic enteropathogens. https://doi.org/10.1289/EHP12314, Introduction Diarrheal diseases are a substantial source of childhood morbidity and mortality. (1) In contexts where enteropathogen transmission is high, repeated asymptomatic infections also contributed to undernutrition and growth faltering [...]
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- 2023
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44. Quantifying Enteropathogen Contamination along Chicken Value Chains in Maputo, Mozambique: A Multidisciplinary and Mixed-Methods Approach to Identifying High Exposure Settings
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Lamar, Frederica, Mucache, Hermogenes N., Mondlane-Milisse, Amelia, Jesser, Kelsey J., Victor, Courtney, Fafetine, Jose M., Saide, Joaquim Angelo Osvaldo, Fevre, Eric M., Caruso, Bethany A., Freeman, Matthew C., and Levy, Karen
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Food -- Safety and security measures ,Meat -- Health aspects -- Methods -- Contamination ,Poultry industry -- International economic relations ,Infection -- Methods -- Health aspects ,Environmental issues ,Health ,World Health Organization - Abstract
Background: Small-scale poultry production is widespread and increasing in low- and middle-income countries (LMICs). Exposure to enteropathogens in poultry feces increases the hazard of human infection and related sequela, and the burden of disease due to enteric infection in children Objectives: To improve the understanding of potential exposures to enteropathogens carried by chickens, we used mixed methods to map and quantify microbial hazards along production value chains among broiler, layer, and indigenous chickens in Maputo, Mozambique. Methods: To map and describe the value chains, we conducted 77 interviews with key informants working in locations where chickens and related products are sold, raised, and butchered. To quantify microbial hazards, we collected chicken carcasses (n = 75) and fecal samples (n = 136) from chickens along the value chain and assayed them by qPCR for the chicken- associated bacterial enteropathogens C. jejuni/coli and Salmonella spp. Results: We identified critical hazard points along the chicken value chains and identified management and food hygiene practices that contribute to potential exposures to chicken-sourced enteropathogens. We detected C. jejuni/coli in 84 (76%) of fecal samples and 52 (84%) of carcass rinses and Salmonella spp. in 13 (11%) of fecal samples and 16 (21%) of carcass rinses. Prevalence and level of contamination increased as chickens progressed along the value chain, from no contamination of broiler chicken feces at the start of the value chain to 100% contamination of carcasses with C. jejuni/coli at informal markets. Few hazard mitigation strategies were found in the informal sector. Discussion: High prevalence and concentration of C. jejuni/coli and Salmonella spp. contamination along chicken value chains suggests a high potential for exposure to these enteropathogens associated with chicken production and marketing processes in the informal sector in our study setting. We identified critical control points, such as the carcass rinse step and storage of raw chicken meat, that could be intervened in to mitigate risk, but regulation and enforcement pose challenges. This mixed-methods approach can also provide a model to understand animal value chains, sanitary risks, and associated exposures in other settings. https://doi.org/10.1289/EHP11761, Introduction Foodborne illness results in an estimated 420,000 deaths annually, 30% of which occur in children Annual global production of feces from animals (primarily cattle, chickens, and sheep) is estimated [...]
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- 2023
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45. Population genomics of diarrheagenic Escherichia coli uncovers high connectivity between urban and rural communities in Ecuador
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Rothstein, Andrew P., Jesser, Kelsey J., Feistel, Dorian J., Konstantinidis, Konstantinos T., Trueba, Gabriel, and Levy, Karen
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- 2023
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46. Understanding the Impact of Rainfall on Diarrhea: Testing the Concentration-Dilution Hypothesis Using a Systematic Review and Meta-Analysis.
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Kraay, Alicia NM, Man, Olivia, Levy, Morgan C, Levy, Karen, Ionides, Edward, and Eisenberg, Joseph NS
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Diarrhea ,Water Microbiology ,Rain ,Environmental Exposure ,Environmental Sciences ,Medical and Health Sciences ,Toxicology - Abstract
BackgroundProjected increases in extreme weather may change relationships between rain-related climate exposures and diarrheal disease. Whether rainfall increases or decreases diarrhea rates is unclear based on prior literature. The concentration-dilution hypothesis suggests that these conflicting results are explained by the background level of rain: Rainfall following dry periods can flush pathogens into surface water, increasing diarrhea incidence, whereas rainfall following wet periods can dilute pathogen concentrations in surface water, thereby decreasing diarrhea incidence.ObjectivesIn this analysis, we explored the extent to which the concentration-dilution hypothesis is supported by published literature.MethodsTo this end, we conducted a systematic search for articles assessing the relationship between rain, extreme rain, flood, drought, and season (rainy vs. dry) and diarrheal illness.ResultsA total of 111 articles met our inclusion criteria. Overall, the literature largely supports the concentration-dilution hypothesis. In particular, extreme rain was associated with increased diarrhea when it followed a dry period [incidence rate ratio (IRR)=1.26; 95% confidence interval (CI): 1.05, 1.51], with a tendency toward an inverse association for extreme rain following wet periods, albeit nonsignificant, with one of four relevant studies showing a significant inverse association (IRR=0.911; 95% CI: 0.771, 1.08). Incidences of bacterial and parasitic diarrhea were more common during rainy seasons, providing pathogen-specific support for a concentration mechanism, but rotavirus diarrhea showed the opposite association. Information on timing of cases within the rainy season (e.g., early vs. late) was lacking, limiting further analysis. We did not find a linear association between nonextreme rain exposures and diarrheal disease, but several studies found a nonlinear association with low and high rain both being associated with diarrhea.DiscussionOur meta-analysis suggests that the effect of rainfall depends on the antecedent conditions. Future studies should use standard, clearly defined exposure variables to strengthen understanding of the relationship between rainfall and diarrheal illness. https://doi.org/10.1289/EHP6181.
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- 2020
47. Measuring Environmental Exposure to Enteric Pathogens in Low-Income Settings: Review and Recommendations of an Interdisciplinary Working Group
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Goddard, Frederick GB, Ban, Radu, Barr, Dana Boyd, Brown, Joe, Cannon, Jennifer, Colford, John M, Eisenberg, Joseph NS, Ercumen, Ayse, Petach, Helen, Freeman, Matthew C, Levy, Karen, Luby, Stephen P, Moe, Christine, Pickering, Amy J, Sarnat, Jeremy A, Stewart, Jill, Thomas, Evan, Taniuchi, Mami, and Clasen, Thomas
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Foodborne Illness ,Prevention ,Infectious Diseases ,Pediatric ,2.2 Factors relating to the physical environment ,Aetiology ,Infection ,Good Health and Well Being ,Clean Water and Sanitation ,Child ,Child ,Preschool ,Environmental Exposure ,Feces ,Humans ,Hygiene ,Poverty ,Sanitation ,Environmental Sciences - Abstract
Infections with enteric pathogens impose a heavy disease burden, especially among young children in low-income countries. Recent findings from randomized controlled trials of water, sanitation, and hygiene interventions have raised questions about current methods for assessing environmental exposure to enteric pathogens. Approaches for estimating sources and doses of exposure suffer from a number of shortcomings, including reliance on imperfect indicators of fecal contamination instead of actual pathogens and estimating exposure indirectly from imprecise measurements of pathogens in the environment and human interaction therewith. These shortcomings limit the potential for effective surveillance of exposures, identification of important sources and modes of transmission, and evaluation of the effectiveness of interventions. In this review, we summarize current and emerging approaches used to characterize enteric pathogen hazards in different environmental media as well as human interaction with those media (external measures of exposure), and review methods that measure human infection with enteric pathogens as a proxy for past exposure (internal measures of exposure). We draw from lessons learned in other areas of environmental health to highlight how external and internal measures of exposure can be used to more comprehensively assess exposure. We conclude by recommending strategies for advancing enteric pathogen exposure assessments.
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- 2020
48. Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices
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Raghavan, Manish, Barocas, Solon, Kleinberg, Jon, and Levy, Karen
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Computer Science - Computers and Society ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
There has been rapidly growing interest in the use of algorithms in hiring, especially as a means to address or mitigate bias. Yet, to date, little is known about how these methods are used in practice. How are algorithmic assessments built, validated, and examined for bias? In this work, we document and analyze the claims and practices of companies offering algorithms for employment assessment. In particular, we identify vendors of algorithmic pre-employment assessments (i.e., algorithms to screen candidates), document what they have disclosed about their development and validation procedures, and evaluate their practices, focusing particularly on efforts to detect and mitigate bias. Our analysis considers both technical and legal perspectives. Technically, we consider the various choices vendors make regarding data collection and prediction targets, and explore the risks and trade-offs that these choices pose. We also discuss how algorithmic de-biasing techniques interface with, and create challenges for, antidiscrimination law.
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- 2019
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49. A Qualitative Study of Food Choice in Urban Coastal Esmeraldas, Ecuador
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Uruchima, Jessica, Renehan, Cala, Castro, Nancy, Cevallos, William, Levy, Karen, Eisenberg, Joseph NS., and Lee, Gwenyth O.
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- 2023
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50. Measurement in the study of human exposure to animal feces: A systematic review and audit
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Ballard, April M., Laramee, Nicholas, Haardörfer, Regine, Freeman, Matthew C., Levy, Karen, and Caruso, Bethany A.
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- 2023
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