Large language models (LLMs) are being increasingly deployed in healthcare, influencing diagnostic reasoning and clinical workflows. However, evidence of clinician engagement with these systems, how they prompt, constrain, and verify output, remains scarce, particularly in low- and middle-income countries (LMICs). We conducted a mixed-methods study with physicians in Pakistan: (1) logging their interactions while they solved expert-designed clinical vignettes with optional LLM assistance, and (2) interviewing 12 participants about generative-AI-supported diagnosis. Findings highlight diverse prompting strategies from role assignment to cautious scaffolding, with consistent insistence on human oversight. Interviews reveal pragmatic enthusiasm for LLMs as a “second brain” in resource-constrained settings, tempered by skepticism about reliability, privacy, and patient trust. This study contributes evidence of physician-LLM interaction patterns in an LMIC context, a taxonomy of prompting strategies and oversight mechanisms, and design implications for responsible AI integration in healthcare workflows.

Today's Web remains too expensive for many Internet users, especially in developing regions. Unfortunately, the rising complexity of the Web makes affordability an even bigger concern as it stands to limit users' access to Internet services. We propose a novel framework and a fairness metric for rethinking Web architecture for affordability and inclusion. Our proposed framework systematically adapts Web complexity based on geographic variations in mobile broadband prices and income levels. We conduct a cross-country analysis of 99 countries, showing that our framework can better balance affordability and webpage quality while preserving user privacy. To adapt Web complexity, our framework solves an optimization problem to produce webpages that maximize page quality while reducing the webpage to a given target size.

Public service websites act as official gateways to services provided by governments. Many of these websites are essential for citizens to receive reliable information and online government services. However, the lack of affordability of mobile broadband services in many developing countries and the rising complexity of websites create barriers for citizens in accessing these government websites. This paper presents the first large-scale analysis of the affordability of public service websites in developing countries. We do this by collecting a corpus of 1900 public service websites, including public websites from nine developing countries and for comparison websites from nine developed countries. Our investigation is driven by website complexity analysis as well as evaluation through a recently proposed affordability index. Our analysis reveals that, in general, public service websites in developing countries do not meet the affordability target set by the UN’s Broadband Commission. However, we show that several countries can be brought within or closer to the affordability target by implementing webpage optimizations to reduce page sizes. We also discuss policy interventions that can help make access to public service website more affordable.

With the advancement in natural-sounding speech production models, it is becoming important to develop models that can detect spoofed audios. Synthesized speech models do not explicitly account for all factors affecting speech production, such as the shape, size and structure of a speaker's vocal tract. In this paper, we hypothesize that due to practical limitations of audio corpora (including size, distribution, and balance of variables like gender, age, and accents), there exist certain phonemes that synthesized models are not able to replicate as well as the human articulation system and such phonemes differ in their spectral characteristics from bonafide speech. To discover such phonemes and quantify their effectiveness in distinguishing between spoofed and bonafide speech, we use a deep learning model with self-attention, and analyze the attention weights of the trained model. We use the ASVSpoof2019 dataset for our analysis and find that the attention mechanism picks most on fricatives: /S/,/SH/, nasals: /M/,/N/, vowels: /Y/, and stops: /D/. Furthermore, we obtain 7.54% EER on train and 11.98% on dev data when using only the top-16 most attended phonemes from input audio, better than when any other phoneme classes are used.

Today's Web remains too expensive for many Internet users, especially in developing regions. Unfortunately, the rising complexity of the Web makes affordability an even bigger concern as it stands to limit users' access to Internet services. We propose a novel framework and fairness metric for rethinking Web architecture for affordability and inclusion. Our framework provides systematic guidelines for adapting Web complexity based on geographic variations in mobile broadband prices and income levels. Preliminary evaluation shows the resulting architecture can achieve a better balance between Web quality and affordability while preserving user privacy.

Emerging countries predominantly rely on room-level air conditioning units (window ACs, space heaters, ceiling fans) for thermal comfort. These distributed units have manual, decentralized control leading to suboptimal energy usage for two reasons: excessive setpoints by individuals and inability to interleave different conditioning units for energy savings. We propose a novel inverted HVAC approach: cheaply retrofitting these distributed units with “on-off” control and providing centralized control augmented with room and environmental sensors. Our binary control approach exploits an understanding of device consumption characteristics and factors this into the control algorithms to reduce consumption. We implement this approach as H awadaar in a prototype 180ft 2 room to evaluate its efficacy over a 7-month period experiencing both hot and cold climates. Through a post analysis, we show that our on-off algorithms are not far from a theoretically optimal approach based on a priori information that precisely knows the optimal control points to minimize consumption. We collect enough evidence to plausibly scale our empirical evaluation, demonstrating countrywide benefits: with just 20% market penetration, H awadaar can save up to 6% of electricity per capita in residential and commercial sectors—resulting in a substantial countrywide impact.