There are two kinds of problems with rumour over WhatsApp: one is disinformation and the other is incitement to violence. Both are serious in their own ways, but for different reasons. In this essay, I explain why the rumours leading to the lynchings are more appropriately treated as incitement to violence. I also discuss what significance WhatsApp has in this context, and whether the changes made by WhatsApp in reaction to the public criticism and government pressure are likely to put a stop to the lynchings. Finally, I discuss the potential human rights consequences of WhatsApp’s willingness to co-operate with the Indian government.
Large language models (LLMs) are increasingly deployed in artificial intelligence (AI) governance analysis across national and international organisations. There is, however, growing evidence that such models produce significantly less accurate responses for countries that are underrepresented in their training data-a pattern described in existing literature as geographic bias. Existing studies examining this phenomenon are subject to three methodological limitations that together undermine their findings: (1) reliance on proprietary systems whose weights are not publicly released, which prevents independent replication; (2) evaluation of model knowledge about years that fall after data collection for model training had concluded, leading to geographic ignorance in addition to the natural limits of each model's knowledge; and (3) use of coarse binary response classification that cannot distinguish models' confident fabrication (HF) from their honest acknowledgement of uncertainty. This study addresses all three limitations by benchmarking four open-weight frontier language models against the Global AI Dataset v2 (GAID v2), a verified ground-truth database of 24,453 indicators across 227 countries published on Harvard Dataverse in January 2026. A total of 18 indicators, mapped to the eight thematic dimensions of the IEEE IRAI 2026 framework, are selected from GAID v2, yielding approximately 2,990 country-metric-year observations across six evaluation years (within the period of 2010-2023). Model responses are classified using a five-category scheme that distinguishes (a) verified accuracy (VA), (b) HF, (c) honest refusal (HR), (d) qualitative hedging (QH), and (e) misattribution (MF). Geographic disparities in accuracy are estimated through mixed-effects logistic regression and difference-in-differences (DiD) analysis.
The five Regional Internet Registries (RIRs) provide the critical function of IP address resource del egation and registration. The accuracy of registration data directly impacts Internet operation, management, security, and optimization. In addition, the scarcity of IP addresses has brought into focus conflicts between RIR policy and IP registration ownership and use. The tension between a free-market based approach to address allocation versus policies to promote fairness and regional equity has resulted in court litigation that threatens the very existence of the RIR system. We develop WHEREIS, a measurement-based approach to geolocate delegated IPv4 and IPv6 prefixes at an RIR-region granularity and systematically study where addresses are used post-allocation and the extent to which registration information is accurate. We define a taxonomy of registration ``geo-consistency'' that compares a prefix's measured geolocation to the allocating RIR's coverage region as well as the registered organization's location. While in aggregate over 98% of the prefixes we examine are consistent with our geolocation inferences, there is substantial variation across RIRs and we focus on AFRINIC as a case study. IPv6 registrations are no more consistent than IPv4, suggesting that structural, rather than technical, issues play an important role in allocations. We solicit additional information on inconsistent prefixes from network operators, IP leasing providers, and collaborate with three RIRs to obtain validation. We further show that the inconsistencies we discover manifest in three commercial geolocation databases. By improving the transparency around post-allocation prefix use, we hope to improve applications that use IP registration data and inform ongoing discussions over in-region address use and policy.
Autonomous Systems (ASes) rely on interconnection agreements to exchange traffic and ensure global Internet connectivity. Establishing mutually beneficial peering relationships offers significant cost and performance advantages, yet requires agreement based on compatibility and perceived value. While existing research focuses on how ASes select potential peers, the complementary question remains unaddressed: how can an AS strategically modify its own infrastructure to maximize its attractiveness as a peering partner? We present a causal inference framework to answer this question, estimating the impact of infrastructure investments at Internet Exchange Points (IXPs) on an AS’s peering desirability. Using the T-learner meta-algorithm with publicly available data, we quantify treatment effects for three infrastructure decisions: joining IXPs, upgrading port capacities, and leaving IXPs. Our analysis reveals substantial heterogeneity in treatment effects across network types and contexts. Joining an IXP yields the highest returns but with significant variability, while port upgrades provide smaller yet more consistent benefits. Leaving an IXP uniformly degrades peering prospects. These findings demonstrate that effective interconnection strategies must be personalized to network characteristics and local IXP environments rather than following one-size-fits-all recommendations. We demonstrate practical applicability through a case study that shows how operators can evaluate specific investment decisions and predict their impacts on individual peering relationships.
Artificial intelligence (AI) control protocols assume that trusted large language model (LLM) monitors reliably assess proposed actions across all deployment contexts. This paper tests that assumption in the geographic dimension. We audit Claude Opus 4.6-the monitor specified in Apart Research's AI Control Hackathon Track 3 benchmark-for systematic gaps in its factual knowledge of the global AI landscape. We develop the AI Control Knowledge Framework (ACKF), a six-dimension thematic scheme, and operationalise it with 17 verified indicators drawn from the Global AI Dataset v2 (GAID v2): 24,453 indicators across 227 countries published on Harvard Dataverse. A five-category response classification scheme distinguishes verifiable fabrication (VF) from honest refusal (HR); logistic regression with country-clustered standard errors combined with difference-in-differences (DiD) estimation quantifies geographic disparities in monitor accuracy across 2,820 country-metric-year observations. Contrary to our initial hypothesis, Claude Opus 4.6 produces higher fabrication rates for Global North queries than for Global South counterparts-a pattern consistent with a partial-knowledge mechanism in which the model attempts answers more frequently for Global North contexts but commits to incorrect values. This fabrication profile constitutes an exploitable vulnerability, where an adversarial AI system could frame harmful actions in governance or public attitude terms to reduce the probability of detection. This study provides the first cross-national, multi-domain audit of an AI control monitor's geographic knowledge gaps, with direct implications for the design of control protocols.
