Online targeted ads are those shown only to certain users based on interests, demographics, or behaviors. Because targeted ads raise many privacy concerns, many platforms provide ad transparency systems (ATSs) to inform users about this practice. To better understand what current ATSs are communicating to users—and how—we first taxonomized the design and content of 22 of the most popular English-language websites' ATSs as presented to users in the United States. We found substantial differences across ATSs in both the prevalence of transparency-enhancing features (e.g., whether they show users what has been inferred about them) and the presentation of information (e.g., the terminology used, where settings are located). Across all platforms, however, we observed consistent ambiguity about what data is used to target ads and the actual impact of altering settings. To gauge how these different design choices impact users, we conducted an online user study in which 198 participants used their own account to explore the ATS of one of eight representative platforms. We found that many of the questions participants hoped the ATS would answer remained unanswered after exploring the ATS. More broadly, participants found current ATSs simultaneously complex and lacking key details. We pinpoint ATS design decisions that best support users.

Current algorithmic fairness tools focus on auditing completed models, neglecting the potential downstream impacts of iterative decisions about cleaning data and training machine learning models. In response, we developed Retrograde, a JupyterLab environment extension for Python that generates real-time, contextual notifications for data scientists about decisions they are making regarding protected classes, proxy variables, missing data, and demographic differences in model performance. Our novel framework uses automated code analysis to trace data provenance in JupyterLab, enabling these notifications. In a between-subjects online experiment, 51 data scientists constructed loan-decision models with Retrograde providing notifications continuously throughout the process, only at the end, or never. Retrograde’s notifications successfully nudged participants to account for missing data, avoid using protected classes as predictors, minimize demographic differences in model performance, and exhibit healthy skepticism about their models.

Home Internet of Things (IoT) devices can be difficult for users to secure. Prior work has suggested measuring these devices' network behaviors and using these characterizations to create allowlists of permitted endpoints. Unfortunately, previous studies have typically been conducted in controlled lab settings, with one or two devices per product. In this paper, we examine whether popular home IoT products' network behaviors generalize via both in-lab experiments of 24 devices and a large, crowdsourced dataset of IoT devices in the wild. We find that observing traffic from one device in one lab is often insufficient to fully characterize an IoT product's network behaviors. For example, specifying which endpoints a device may contact based on initial measurements in our lab led 25% of products to stop functioning later, and even more when using a VPN. We then used the crowdsourced dataset to better understand this traffic's heterogeneity and pinpoint how to create more generalizable allowlists. We identified causes of failure, such as regionalization, CDN usage, third-party integrations, and API changes. Finally, we used the crowdsourced data in numerous configurations to specify which endpoints each product in our lab could contact. We found that domain-level allowlists enabled the majority of devices to function in our lab using data collected years in the past. For the remaining devices, we characterize how to mitigate the failures observed and pave the way to creating more generalizable allowlists.

Goals of objectivity and neutrality drive the usage of algorithmic systems, however most efforts have produced similar or more harm than their human counterparts. Post-hoc analyses of these flawed systems often reveal systemic issues underlying the data and flawed assumptions in preparation stages that affect the models produced. To date, the algorithmic fairness community has had a myopic focus on optimizing and evaluating algorithmic systems at static decision points and mathematical definitions of fairness, neglecting efforts towards critically understanding the data being used prior to modeling. My research aims to support data workers’ sociotechnical understanding of data by creating new interfaces and methods to elicit and utilize their prior knowledge and open information (such as census data), as means to discover and augment harmful patterns in data.

Jamar Sullivan Jr., Will Brackenbury, Andrew McNutt, Kevin Bryson, Kwam Byll, Yuxin Chen, Michael Littman, Chenhao Tan, Blase Ur. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.