Representational bias in large language models (LLMs) has predominantly been measured through single-response interactions and has focused on Global North-centric identities like race and gender. We expand on that research by conducting a systematic audit of GPT -4 Turbo to reveal how deeply encoded representational biases are and how they extend to less-explored dimensions of identity. We prompt GPT - 4 Turbo to generate over 7,200 stories about significant life events (such as weddings) in India, using prompts designed to encourage diversity to varying extents. Comparing the diversity of religious and caste representation in the outputs against the actual population distribution in India as recorded in census data, we quantify the presence and "stickiness" of representational bias in the LLM for religion and caste. We find that GPT -4 responses consistently overrepresent culturally dominant groups far beyond their statistical representation, despite prompts intended to encourage representational diversity. Our findings also suggest that representational bias in LLMs has a winner-take-all quality that is more biased than the likely distribution bias in their training data, and repeated prompt-based nudges have limited and inconsistent efficacy in dislodging these biases. These results suggest that diversifying training data alone may not be sufficient to correct LLM bias, highlighting the need for more fundamental changes in model development.

Air pollution kills 7 million people annually. The brick kiln sector significantly contributes to economic development but also accounts for 8-14\% of air pollution in India. Policymakers have implemented compliance measures to regulate brick kilns. Emission inventories are critical for air quality modeling and source apportionment studies. However, the largely unorganized nature of the brick kiln sector necessitates labor-intensive survey efforts for monitoring. Recent efforts by air quality researchers have relied on manual annotation of brick kilns using satellite imagery to build emission inventories, but this approach lacks scalability. Machine-learning-based object detection methods have shown promise for detecting brick kilns; however, previous studies often rely on costly high-resolution imagery and fail to integrate with governmental policies. In this work, we developed a scalable machine-learning pipeline that detected and classified 30638 brick kilns across five states in the Indo-Gangetic Plain using free, moderate-resolution satellite imagery from Planet Labs. Our detections have a high correlation with on-ground surveys. We performed automated compliance analysis based on government policies. In the Delhi airshed, stricter policy enforcement has led to the adoption of efficient brick kiln technologies. This study highlights the need for inclusive policies that balance environmental sustainability with the livelihoods of workers.

With billions of people facing moderate or severe food insecurity, the resilience of the global food supply will be of increasing concern due to the effects of climate change and geopolitical events. In this paper we describe a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system. While we feel that the methods are adaptable to other regions of the world, we focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population. We present a quantitative analysis of deep learning domain adaptation methods for wheat farm identification based on curated remote sensing data from France. We model climate change impacts on crop yields using the existing crop yield modeling tool WOFOST and we identify key drivers of crop simulation error using a longitudinal penalized functional regression. A description of a system dynamics model of the food distribution system in India is also presented, along with results of food insecurity identification based on seeding this model with the predicted crop yields.

NITI Aayog, the policy think tank of the Government of India, is looking at expanding its "Medicines from the Sky" project, which uses unmanned aerial systems for the delivery of vaccines in remote areas, to the North-Eastern parts of the country. It is also exploring use of emerging technologies including artificial intelligence (AI) in medical diagnostics. NITI Aayog, in collaboration with the Government of Telangana and the World Economic Forum (WEF), launched the'Medicines from the Sky' project on piloting the use of unmanned aerial systems for the delivery of vaccines in remote areas. These drone trials are focused on laying the groundwork for a drone delivery network that will improve access to vital healthcare supplies for remote and vulnerable communities. The scope includes deliveries of MMR (maternal mortality rate), flu and C-19 vaccines.

We have entered an era of a pandemic that has shaken the world with major impact to medical systems, economics and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lack of data collection and reporting makes any such modelling attempts unreliable. Hence we need to re-look at the situation with the latest data sources and most comprehensive forecasting models. Deep learning models such as recurrent neural networks are well suited for modelling temporal sequences. In this paper, prominent recurrent neural networks, in particular \textit{long short term memory} (LSTMs) networks, bidirectional LSTM, and encoder-decoder LSTM models for multi-step (short-term) forecasting the spread of COVID-infections among selected states in India. We select states with COVID-19 hotpots in terms of the rate of infections and compare with states where infections have been contained or reached their peak and provide two months ahead forecast that shows that cases will slowly decline. Our results show that long-term forecasts are promising which motivates the application of the method in other countries or areas. We note that although we made some progress in forecasting, the challenges in modelling remain due to data and difficulty in capturing factors such as population density, travel logistics, and social aspects such culture and lifestyle.

In the course of the two years, we have seen a consistent increment in the percentage of adoption of AI in India. Given the Indian government's ongoing focus on building up a plan for artificial intelligence, it is recommended to apply strengths (deep analysis of AI applications and implications) to determine (a) the state of AI innovation in India, and (b) strategic insights to help India survive and thrive in a global market with the help of AI initiatives. Advances in artificial intelligence and data analytics are pushing development in numerous parts of the world. China, for instance, has committed $150 billion towards its objective of turning into a world chief by 2030. And while the United States government is putting just $1.1 billion in non-classified AI research, its private sector is burning through billions in fields from finance and healthcare to retail and defense.

NEW DELHI/MUMBAI, INDIA – When artist Rachita Taneja heads out to protest in New Delhi, she covers her face with a pollution mask, a hoodie or a scarf to reduce the risk of being identified by police facial recognition software. Police in the Indian capital and the northern state of Uttar Pradesh -- both hotbeds of dissent -- have used the technology during protests that have raged since mid-December against a new citizenship law that critics say marginalizes Muslims. Activists are worried about insufficient regulation around the new technology, amid what they say is a crackdown on dissent under Prime Minister Narendra Modi, whose Hindu nationalist agenda has gathered pace since his re-election in May. "I do not know what they are going to do with my data," said Taneja, 28, who created a popular online cartoon about cheap ways for protesters to hide their faces. "We need to protect ourselves, given how this government cracks down."

Staqu launched an AI powered Video wall which will analyse CCTV footage from 70 prisons of UP. The video analytics platform called JARVIS will check for frisking, unauthorised access, crowd analysis, violence, and intrusion detection. The startup has already worked with the police forces of Rajasthan, Punjab, Uttar Pradesh, Uttarakhand and Telangana. Indian Artificial Intelligence and facial recognition startup Staqu which has partnered with the police on several occasions - has now brought about a'Video Wall' that will analyse movement in the prisons of Uttar Pradesh. The AI-powered Video wall will cover and analyse CCTV footage from 70 prisons of UP.

NEW DELHI: India on Friday sought to add meat to its strategic partnership with Germany by wooing industries to invest in defence corridors of Tamil Nadu and Uttar Pradesh. At their biennial summit in New Delhi, India and Germany also sought to give momentum to revive stalled negotiations for free-trade agreement with the European Union. Proposed in 2007, the negotiations hit a roadblock in 2013 when the two sides arrived at an impasse on tariffs and market access. Disagreements on standards and practices exacerbated the situation and negotiations were shelved for five years. Germany has been an advocate of the deal and welcomed the resumption of negotiations last year.

Something is happening in Bhaumau - a rural village in India's populous state of Uttar Pradesh where parents spend most of their time working in agriculture and as day labourers. Children, with no guidance from adults, are forming learning groups and with nothing more than a tablet computer preloaded with educational videos, stories and games, they are learning English and conducting science experiments. In the first three months of playing with the tablets there has been, according to the project's monitoring data, an 11% increase in pupils' core academic skills such as reading in children's mother tongue, reading and speaking in English, and science. Maybe even more important, children are figuring out how to navigate the digital world to find out answers to their questions and are more confident about speaking up. This is a radically different approach to using technology to advance learning.