Poor broadband access undermines civic and economic life, a challenge exacerbated by the fact that millions of Americans still lack access to reliable high-speed broadband connectivity. Federal broadband funding initiatives such as the Connect America Fund (CAF), the Rural Digital Opportunity Fund (RDOF), and the Broadband Equity, Access, and Deployment (BEAD) seek to address these gaps, but their success relies on the accuracy of broadband availability and affordability data. This data is often based on self-reported ISP information that overstates coverage and speeds. Such inaccuracies risk misallocating funds, leaving unserved and underserved communities without high-speed Internet. In this work, we present BQT+, an ML-based data collection platform that queries ISP web interfaces by inputting residential street addresses and extracting the returned data on service availability, quality, and pricing. BQT+ has been applied in policy evaluation studies, including an independent assessment of the state of broadband availability, quality (available speed tiers), and affordability in areas expected to benefit from the $42.45 billion BEAD program.

Residential fixed broadband internet access in the US remains inequitable, despite significant taxpayer investment. This paper evaluates the efficacy of the Connect America Fund (CAF), which subsidizes new broadband monopolies in underserved areas to provide internet access comparable to that in urban regions. CAF's oversight relies heavily on self-reported data from internet service providers (ISPs). Unfortunately, the reliability of this self-reported data has always been open to question. We use the broadband-plan querying tool (BQT) to create a novel dataset that complements ISP-reported information with ISP-advertised broadband plan details from publicly accessible websites for 537k residential addresses across 15 states. Our analysis reveals significant discrepancies, with a serviceability rate of only 55.45%, indicating that a significant fraction of addresses certified as served are still unserved. Furthermore, we observe a compliance rate of only 33.03%, indicating that a significant fraction of served addresses receive download speeds that are non-compliant with the FCC's 10 Mbps threshold for CAF-served addresses. Although we observe that CAF-served addresses occasionally receive higher download speeds than their monopoly-served neighbors, overall, the CAF program has largely failed to achieve its intended goal, leaving many targeted rural communities with inadequate or no broadband connectivity.

Digital equity in Internet access is often measured along three axes: availability, affordability, and adoption. Most prior work focuses on availability; the other two aspects have received less attention. In this paper, we study broadband affordability in the US by focusing on the nature of broadband plans offered by major ISPs. To this end, we develop a broadband plan querying tool (BQT) that obtains broadband plans (upload/download speed and price) offered by seven major wireline US ISPs for any street address in the US. We then use this tool to curate a dataset, querying broadband plans for over 837 k street addresses in thirty cities for these ISPs. We use a plan's carriage value, defined as the Mbps of a user's traffic that an ISP carries for one dollar, to compare plans. Our analysis provides us with the following new insights: (1) ISP plans vary inter-city. Specifically, up to 60% of the census block groups in a city can receive low carriage value plans from an ISP; (2) ISP plans intra-city are spatially clustered, and the carriage value can vary as much as 600% within a city; (3) Cable-based ISPs offer up to 30% higher carriage value to users when they are competing with fiber-based ISPs in a block group compared to when they are operating alone or in conjunction with a DSL-based ISP; and (4) Fiber deployments, which have better carriage values, are associated with higher average income block groups. While we hope our tool, dataset, and analysis in their current form are helpful for policymakers at different levels (city, county, state), they are only a small step toward quantifying digital inequity. We conclude with recommendations to further advance our understanding of broadband affordability.