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The 2022 settlement between Meta and the U.S. Department of Justice to resolve allegations of discriminatory advertising resulted is a first-of-its-kind change to Meta's ad delivery system aimed to address algorithmic discrimination in its housing ad delivery. In this work, we explore direct and indirect effects of both the settlement's choice of terms and the Variance Reduction System (VRS) implemented by Meta on the actual reduction in discrimination. \newline We first show that the settlement terms allow for an implementation that does not meaningfully improve access to opportunities for individuals. The settlement measures impact of ad delivery in terms of impressions, instead of unique individuals reached by an ad; it allows the platform to level down access, reducing disparities by decreasing the overall access to opportunities; and it allows the platform to selectively apply VRS to only small advertisers. \newline We then conduct experiments to evaluate VRS with real-world ads, and show that while VRS does reduce variance, it also raises advertiser costs (measured per-individuals-reached), therefore decreasing user exposure to opportunity ads for a given ad budget. VRS thus \emph{passes the cost of decreasing variance to advertisers}. \newline Finally, we explore an alternative approach to achieve the settlement goals, that is significantly more intuitive and transparent than VRS. We show our approach outperforms VRS by both increasing ad exposure for users from \emph{all} groups and reducing cost to advertisers, thus demonstrating that the increase in cost to advertisers when implementing the settlement is not inevitable. \newline Our methodologies use a black-box approach that relies on capabilities available to any regular advertiser, rather than on privileged access to data, allowing others to reproduce or extend our work. 

Auditing social-media algorithms has become a focus of public-interest research and policymaking to ensure their fairness across demographic groups such as race, age, and gender in consequential domains such as the presentation of employment opportunities. However, such demographic attributes are often unavailable to auditors and platforms. When demographics data is unavailable, auditors commonly \emph{infer} them from other available information. In this work, we study the effects of inference error on auditing for bias in one prominent application: \emph{black-box} audit of ad delivery using \emph{paired ads}. We show that inference error, if not accounted for, causes auditing to falsely miss skew that exists. We then propose a way to mitigate the inference error when evaluating skew in ad delivery algorithms. Our method works by adjusting for expected error due to demographic inference, and it makes skew detection more sensitive when attributes must be inferred. Because inference is increasingly used for auditing, our results provide an important addition to the auditing toolbox to promote correct audits of ad delivery algorithms for bias. While the impact of attribute inference on accuracy has been studied in other domains, our work is the first to consider it for black-box evaluation of ad delivery bias, when only aggregate data is available to the auditor. 

Measuring Internet outages is important to allow ISPs to improve their services, users to choose providers by reliability, and governments to understand the reliability of their infrastructure. Today's active outage detection provides good accuracy with tight temporal and spatial precision (around 10 minutes and IPv4 /24 blocks), but cannot see behind firewalls or into IPv6. Systems using passive methods can see behind firewalls, but usually, relax spatial or temporal precision, reporting on whole countries or ASes at 5 minute precision, or /24 IPv4 blocks with 25 minute precision. We propose Durbin, a new approach to passive outage detection that \emph{adapts spatial and temporal precision} to each network they study, thus providing good accuracy and wide coverage with the best possible spatial and temporal precision. Durbin observes data from Internet services or network telescopes. Durbin studies /24 blocks to provide fine spatial precision, and we show it provides good accuracy even for short outages (5 minutes) in 600k blocks with frequent data sources. To retain accuracy for the 400k blocks with less activity, Durbin uses a coarser temporal precision of 25 minutes. Including short outages is important: omitting short outages underestimates overall outage duration by 15\%, because 5\% of all blocks have at least one short outage. Finally, passive data allows Durbin to report this results for outage detection in IPv6 for 15k /48 blocks. Durbin's use of per-block adaptivity is the key to providing good accuracy and broad coverage across a diverse Internet. 

Time synchronization is of paramount importance on the Internet, with the Network Time Protocol (NTP) serving as the primary synchronization protocol. The NTP Pool, a volunteer-driven initiative launched two decades ago, facilitates connections between clients and NTP servers. Our analysis of root DNS queries reveals that the NTP Pool has consistently been the most popular time service. We further investigate the DNS component (GeoDNS) of the NTP Pool, which is responsible for mapping clients to servers. Our findings indicate that the current algorithm is heavily skewed, leading to the emergence of time monopolies for entire countries. For instance, clients in the US are served by 551 NTP servers, while clients in Cameroon and Nigeria are served by only one and two servers, respectively, out of the 4k+ servers available in the NTP Pool. We examine the underlying assumption behind GeoDNS for these mappings and discover that time servers located far away can still provide accurate clock time information to clients. We have shared our findings with the NTP Pool operators, who acknowledge them and plan to revise their algorithm to enhance security. 

IP anycast is a commonly used method to associate users with services provided across multiple sites, and if properly used, it can provide efficient access with low latency. However, prior work has shown that \emph{polarization} can occur in global anycast services, where some users of that service are routed to an anycast site on another continent, adding 100\,ms or more latency compared to a nearby site. This paper describes the causes of polarization in real-world anycast and shows how to observe polarization in third-party anycast services. We use these methods to look for polarization and its causes in 7986 known anycast prefixes. We find that polarization occurs in more than a quarter of anycast prefixes, and identify incomplete connectivity to Tier-1 transit providers and route leakage by regional ISPs as common problems. Finally, working with a commercial CDN, we show how small routing changes can often address polarization, improving latency for 40\% of clients, by up to 54\%.