Abstract

The global rise of football analytics has rapidly transformed how clubs make strategic decisions (morgulev2018sports,). However, in India, the adoption of analytics remains constrained by institutional resistance, infrastructural limitations, and cultural barriers—challenges that grassroots innovation and low-cost data solutions have the potential to overcome. Despite the increasing popularity of the Indian Super League (scroll2020isl,; chatterjee2021isl,), resource scarcity and fragmented governance continue to hinder the widespread adoption and impact of analytics. This mixed-methods study explores how informal, decentralized analytics communities—comprising amateur analysts and Twitter-based “data sleuths”—navigate these constraints through peer mentorship and grassroots innovation. Drawing on extensive digital ethnography, participant observation, and interviews, the study illustrates how these informal networks mitigate data scarcity, limited digital infrastructure, and institutional indifference while fostering skill development and professional growth. Building on these insights, the paper proposes HCI interventions such as decentralized knowledge platforms to facilitate structured, cross-border peer mentorship and low-cost data solutions—including AI-assisted player tracking and mobile analytics dashboards—rooted in principles of frugal innovation (shauloff2023ai,). These interventions aim to bridge the data divide, support inclusive technical engagement in sport, and enhance analytics-driven decision-making in resource-constrained environments. This paper contributes to HCIxB’s focus on cross-border collaboration by highlighting how community-driven technological adaptation in the Global South can foster meaningful participation, skill-building, and long-term sustainability through informal learning networks and scalable, context-sensitive tools (vashistha2017cve,; nawaz2021farming,).

Abstract

In recent years, widespread internet adoption and the growth in user base of various social media platforms have led to an increase in the prolifera tion of extreme speech online. While traditional language models have demon strated proficiency in distinguishing between neutral text and non-neutral text (i.e. extreme speech), categorizing the diverse types of extreme speech presents significant challenges [1][2]. The task of extreme speech classification is par ticularly nuanced, as it requires a deep understanding of socio-cultural contexts to accurately interpret the intent of the language used by the speaker. Even hu man annotators often disagree on the appropriate classification of such content, emphasizing the complex and subjective nature of this task [3]. The use of hu man moderators also presents a scaling issue, necessitating the need for auto mated systems for extreme speech classification. The recent launch of ChatGPT has drawn global attention to the potential applications of Large Language Models (LLMs) across a diverse variety of tasks. Trained on vast and diverse corpora, and demonstrating the ability to effectively capture and encode contex tual information, LLMs emerge as highly promising tools for tackling this spe cific task of extreme speech classification. In this paper, we leverage the Indian subset of the extreme speech dataset from [3] to develop an effective classifica tion framework using LLMs. We evaluate open-source Llama models against closed-source OpenAI models, finding that while pre-trained LLMs show mod erate efficacy, fine-tuning with domain-specific data significantly enhances per formance, highlighting their adaptability to linguistic and contextual nuances. Although GPT-based models outperform Llama models in zero-shot settings, the performance gap disappears after fine-tuning.

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Abstract

This paper contributes to the expanding research on toxicity in online games, exploring the relationship between game design and player behaviours in India. Using an ethnographic approach and in-depth interviews with gamers, we capture firsthand accounts of toxic gaming experiences, delving into game design, game mechanics, and player interactions intersecting to shape player behaviours. While previous research on toxicity in gaming environments has largely focused on the role of game design, aspects of gender and, to a lesser extent, race, primarily within the context of the Global North, our study shifts the focus to India and, by extension, the Global South. We seek to uncover broader dimensions of gaming toxicity that go beyond design and gender. Our analysis examines the underlying factors contributing to the prevalence and persistence of toxic behaviours in Indian gaming environments by highlighting the influence of cultural and contextual elements in producing toxic behaviours. Expanding the scope of research to include these overlooked aspects, our study aims to provide a more comprehensive understanding of the complexities of toxicity in online gaming spaces within the Indian context.

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From an study of food delivery workers, we examine the precarity and consolidation of working conditions driven by the platform economy in the context of Urban India. The study considers the apparent threats in ‘gigging’, informalisation of labour and the obvious need to reconceptualise worker well-being, risks, stress and the eventuality of workplace and worker alignment in food delivery labour.

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The evolving landscape of gig labor, particularly within food delivery platforms, has been influenced by a shift towards algorithmic management, emphasizing performance metrics as a daily burden to be carried through delivery work. Through a study of Indian food delivery agents, our paper suggests an emerging gamification culture between delivery platforms and workers. Agents gradually yet consistently adapt to stringent algorithmic management to overcome everyday work's precarity. Despite the challenges of performance pressure on food delivery labor, we capture the daily rhythms and routines of delivery work punctuated by flashes of agenting, revealing small capacities to make choices and influence work outcomes. By employing a qualitative research framework, we uncover the mechanisms through which delivery platforms manipulate labor, simultaneously exploring delivery agents' tactics to extract agency and exploit the platforms' rules. We develop the notion of 'agency’ to disentangle the idea of `gamification by agents’ as a socio-technical derivative governing food delivery agents in urban India. The findings highlight the potential of platform work empowering, albeit with limits, delivery agents with agency and decision-making authority.

Abstract

Anti-discrimination represents a critical area of study for researchers, policymakers, and designers of technical systems. Drawing on AI fairness and inclusion studies research, we analyse algorithmic bias and their propensity to reproduce bias [Selbst et al., 2019] on two types of platforms- a gaming environment and a dating platform. On the gaming platform we conduct research on toxicity to comprehend its manifestations, diverse interpretations of toxicity, the conditions fostering toxic gaming, specifically affecting gendered identities. We also investigate if toxic behaviours are normalised within gaming domains, adversely impacting gendered perceptions and experiences. For the second platform, we offer results from an experiment on the Bumble dating platform to identify and address the presence of discrimination in the matching algorithms the platform pushes to its users in the form of profiles for potential dates in the real world. Forms of Interactions of user discovery pose a significant risk of sexual violence and other forms of harm for diverse communities of gender on dating platforms. By reporting experiences of non-binary individuals using dating platforms we contribute to a largely unexplored research field. This study not only highlights the discrepancies and potential biases in dating algorithms but also serves as a catalyst to foster more inclusive design principles, thereby acknowledging the diverse needs across genders and communities.

Abstract

AI is about learning algorithms and huge amounts of data and are drivers of economic growth -- what does this mean for the field of development studies? Can we re-orient to twin AI studies and development theory and practice to generate how development challenges are identified and researched? To do this a good grasp is needed of AI internal mechanisms and outcomes in addressing development issues -- this argument will be developed through a case study of the ADAS [Advanced Driver Assistance System] deployment in India. Over and above discussing the ADAS we bring an anthropological lens to understand the social context that surrounds the system. Focusing on bus drivers, we offer findings from a qualitative and ethnographic study of drivers in a collaborative effort to achieve road safety by deploying AI-driven technology and empowering stakeholders in the transport industry in India especially, bus drivers as critical actors in the city transport network.

Abstract

We study bias and discrimination in the context of Bumble, an online dating platform in India. Drawing on research in AI fairness and inclusion studies we analyze algorithmic bias and their propensity to reproduce bias. We conducted an experiment to identify and address the presence of bias in the matching algorithms Bumble pushes to its users in the form of profiles for potential dates in the real world. Dating apps like Bumble utilize algorithms that learn from user data to make recommendations. Even if the algorithm does not have intentions or consciousness, it is a system created and maintained by humans. We attribute moral agency of such systems to be compositely derived from algorithmic mediations, the design and utilization of these platforms. Developers, designers, and operators of dating platforms thus have a moral obligation to mitigate biases in the algorithms to create inclusive platforms that affirm diverse social identities.