arXiv subjects

Elissa M. Redmiles

Publications and source records attributed to Elissa M. Redmiles.

28 records · Page 2Linked to original sources

arXiv2021

"I need a better description'': An Investigation Into User Expectations For Differential Privacy

Despite recent widespread deployment of differential privacy, relatively little is known about what users think of differential privacy. In this work, we seek to explore users' privacy expectations related to differential privacy. Specifically, we investigate (1) whether users care about the protections afforded by differential privacy, and (2) whether they are therefore more willing to share their data with differentially private systems. Further, we attempt to understand (3) users' privacy expectations of the differentially private systems they may encounter in practice and (4) their willingness to share data in such systems. To answer these questions, we use a series of rigorously conducted surveys (n=2424). We find that users care about the kinds of information leaks against which differential privacy protects and are more willing to share their private information when the risks of these leaks are less likely to happen. Additionally, we find that the ways in which differential privacy is described in-the-wild haphazardly set users' privacy expectations, which can be misleading depending on the deployment. We synthesize our results into a framework for understanding a user's willingness to share information with differentially private systems, which takes into account the interaction between the user's prior privacy concerns and how differential privacy is described.

cs.CY↗

arXiv2021

How we browse: Measurement and analysis of digital behavior

Accurately analyzing and modeling online browsing behavior play a key role in understanding users and technology interactions. In this work, we design and conduct a user study to collect browsing data from 31 participants continuously for 14 days and self-reported browsing patterns. We combine self-reports and observational data to provide an up-to-date measurement study of online browsing behavior. We use these data to empirically address the following questions: (1) Do structural patterns of browsing differ across demographic groups and types of web use?, (2) Do people have correct perceptions of their behavior online?, and (3) Do people change their browsing behavior if they are aware of being observed? In response to these questions, we find significant differences in level of activity based on user age, but not based on race or gender. We also find that users have significantly different behavior on Security Concerns websites, which may enable new behavioral methods for automatic detection of security concerns online. We find that users significantly overestimate the time they spend online, but have relatively accurate perceptions of how they spend their time online. We find no significant changes in behavior over the course of the study, which may indicate that observation had no effect on behavior, or that users were consciously aware of being observed throughout the study

cs.CY↗

arXiv2021

Characterizing the Online Learning Landscape: What and How People Learn Online

Hundreds of millions of people learn something new online every day. Simultaneously, the study of online education has blossomed within the human computer interaction community, with new systems, experiments, and observations creating and exploring previously undiscovered online learning environments. In this study we endeavor to characterize this entire landscape of online learning experiences using a national survey of 2260 US adults who are balanced to match the demographics of the U.S. We examine the online learning resources that they consult, and we analyze the subjects that they pursue using those resources. Furthermore, we compare both formal and informal online learning experiences on a larger scale than has ever been done before, to our knowledge, to better understand which subjects people are seeking for intensive study. We find that there is a core set of online learning experiences that are central to other experiences and these are shared among the majority of people who learn online. We conclude by showing how looking outside of these core online learning experiences can reveal opportunities for innovation in online education.

cs.HC↗

arXiv2020

User Concerns & Tradeoffs in Technology-Facilitated Contact Tracing

The COVID19 pandemic spread across the world in late 2019 and early 2020. As the pandemic spread, technologists joined forces with public health officials to develop apps to support COVID19 response. Yet, for these technological solutions to benefit public health, users must be willing to adopt these apps.This paper details the potential inputs to a user's decision to adopt a COVID19 contact-tracing app or other technology and empirically validates the relevance of these inputs via both the literature and a demographically-representative survey of 1,000 Americans.

cs.CR↗

arXiv2020

Designing Indicators to Combat Fake Media

The growth of misinformation technology necessitates the need to identify fake videos. One approach to preventing the consumption of these fake videos is provenance which allows the user to authenticate media content to its original source. This research designs and investigates the use of provenance indicators to help users identify fake videos. We first interview users regarding their experiences with different misinformation modes (text, image, video) to guide the design of indicators within users' existing perspectives. Then, we conduct a participatory design study to develop and design fake video indicators. Finally, we evaluate participant-designed indicators via both expert evaluations and quantitative surveys with a large group of end-users. Our results provide concrete design guidelines for the emerging issue of fake videos. Our findings also raise concerns regarding users' tendency to overgeneralize from misinformation warning messages, suggesting the need for further research on warning design in the ongoing fight against misinformation.

cs.HC↗

arXiv2020

How good is good enough for COVID19 apps? The influence of benefits, accuracy, and privacy on willingness to adopt

A growing number of contact tracing apps are being developed to complement manual contact tracing. A key question is whether users will be willing to adopt these contact tracing apps. In this work, we survey over 4,500 Americans to evaluate (1) the effect of both accuracy and privacy concerns on reported willingness to install COVID19 contact tracing apps and (2) how different groups of users weight accuracy vs. privacy. Drawing on our findings from these first two research questions, we (3) quantitatively model how the amount of public health benefit (reduction in infection rate), amount of individual benefit (true-positive detection of exposures to COVID), and degree of privacy risk in a hypothetical contact tracing app may influence American's willingness to install. Our work takes a descriptive ethics approach toward offering implications for the development of policy and app designs related to COVID19.

cs.CY↗

arXiv2019

"Should I Worry?" A Cross-Cultural Examination of Account Security Incident Response

Digital security technology is able to identify and prevent many threats to users accounts. However, some threats remain that, to provide reliable security, require human intervention: e.g., through users paying attention to warning messages or completing secondary authentication procedures. While prior work has broadly explored people's mental models of digital security threats, we know little about users' precise, in-the-moment response process to in-the-wild threats. In this work, we conduct a series of qualitative interviews (n=67) with users who had recently experienced suspicious login incidents on their real Facebook accounts in order to explore this process of account security incident response. We find a common process across participants from five countries -- with differing online and offline cultures -- allowing us to identify areas for future technical development to best support user security. We provide additional insights on the unique nature of incident-response information seeking, known attacker threat models, and lessons learned from a large, cross-cultural qualitative study of digital security.

cs.CR↗

arXiv2018

On Microtargeting Socially Divisive Ads: A Case Study of Russia-Linked Ad Campaigns on Facebook

Targeted advertising is meant to improve the efficiency of matching advertisers to their customers. However, targeted advertising can also be abused by malicious advertisers to efficiently reach people susceptible to false stories, stoke grievances, and incite social conflict. Since targeted ads are not seen by non-targeted and non-vulnerable people, malicious ads are likely to go unreported and their effects undetected. This work examines a specific case of malicious advertising, exploring the extent to which political ads from the Russian Intelligence Research Agency (IRA) run prior to 2016 U.S. elections exploited Facebook's targeted advertising infrastructure to efficiently target ads on divisive or polarizing topics (e.g., immigration, race-based policing) at vulnerable sub-populations. In particular, we do the following: (a) We conduct U.S. census-representative surveys to characterize how users with different political ideologies report, approve, and perceive truth in the content of the IRA ads. Our surveys show that many ads are "divisive": they elicit very different reactions from people belonging to different socially salient groups. (b) We characterize how these divisive ads are targeted to sub-populations that feel particularly aggrieved by the status quo. Our findings support existing calls for greater transparency of content and targeting of political ads. (c) We particularly focus on how the Facebook ad API facilitates such targeting. We show how the enormous amount of personal data Facebook aggregates about users and makes available to advertisers enables such malicious targeting.

cs.SI↗

arXiv2018

Dancing Pigs or Externalities? Measuring the Rationality of Security Decisions

Accurately modeling human decision-making in security is critical to thinking about when, why, and how to recommend that users adopt certain secure behaviors. In this work, we conduct behavioral economics experiments to model the rationality of end-user security decision-making in a realistic online experimental system simulating a bank account. We ask participants to make a financially impactful security choice, in the face of transparent risks of account compromise and benefits offered by an optional security behavior (two-factor authentication). We measure the cost and utility of adopting the security behavior via measurements of time spent executing the behavior and estimates of the participant's wage. We find that more than 50% of our participants made rational (e.g., utility optimal) decisions, and we find that participants are more likely to behave rationally in the face of higher risk. Additionally, we find that users' decisions can be modeled well as a function of past behavior (anchoring effects), knowledge of costs, and to a lesser extent, users' awareness of risks and context (R2=0.61). We also find evidence of endowment effects, as seen in other areas of economic and psychological decision-science literature, in our digital-security setting. Finally, using our data, we show theoretically that a "one-size-fits"-all emphasis on security can lead to market losses, but that adoption by a subset of users with higher risks or lower costs can lead to market gains.

cs.GT↗

arXiv2018

Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction

As algorithms are increasingly used to make important decisions that affect human lives, ranging from social benefit assignment to predicting risk of criminal recidivism, concerns have been raised about the fairness of algorithmic decision making. Most prior works on algorithmic fairness normatively prescribe how fair decisions ought to be made. In contrast, here, we descriptively survey users for how they perceive and reason about fairness in algorithmic decision making. A key contribution of this work is the framework we propose to understand why people perceive certain features as fair or unfair to be used in algorithms. Our framework identifies eight properties of features, such as relevance, volitionality and reliability, as latent considerations that inform people's moral judgments about the fairness of feature use in decision-making algorithms. We validate our framework through a series of scenario-based surveys with 576 people. We find that, based on a person's assessment of the eight latent properties of a feature in our exemplar scenario, we can accurately (> 85%) predict if the person will judge the use of the feature as fair. Our findings have important implications. At a high-level, we show that people's unfairness concerns are multi-dimensional and argue that future studies need to address unfairness concerns beyond discrimination. At a low-level, we find considerable disagreements in people's fairness judgments. We identify root causes of the disagreements, and note possible pathways to resolve them.

stat.ML↗