The Polyfoni Social Sciences for AI Fellowship represents a significant opportunity for researchers to bridge the gap between social science expertise and the rapidly advancing field of artificial intelligence. This fellowship is designed for individuals with a background in social sciences and practical fieldwork experience, particularly in regions where AI technologies are being implemented. The program offers a unique four-month, part-time research collaboration where fellows will remotely conduct a cultural audit of a real AI product within their area of expertise. This initiative aims to foster a deeper understanding of how AI systems interact with diverse cultural contexts, ensuring that these powerful tools are developed and deployed responsibly.
The core of the fellowship revolves around Polyfoni’s Cultural Audit Method. This structured approach guides fellows in empirically analyzing AI products. It involves mapping the assumptions embedded within an AI system, identifying where these assumptions break down in real-world contexts, assessing the severity of these failures, and finally, proposing actionable recommendations for improvement. The fellowship provides fellows with the necessary training in this method, alongside dual mentorship from both academic collaborators and AI industry professionals. This blend of academic rigor and practical industry insight is intended to equip fellows with the skills to critically evaluate AI and contribute to its ethical development.
Fellows will be encouraged to choose an AI product for their audit that is currently in use in a region where they possess genuine contextual knowledge. This emphasizes the value of existing fieldwork experience as a primary data source. The program is not about starting from scratch but rather about applying established social science understanding through a defined methodology. Potential audit targets include AI chatbots used in healthcare in East Africa, AI tools for ocean management in Southeast Asia, agricultural AI applications in West Africa, or large language models deployed in markets where English is not the dominant language. These examples highlight the broad applicability of the fellowship’s focus across various sectors and geographical regions.
The benefits of participating in the Polyfoni Social Sciences for AI Fellowship are multifaceted. Beyond the invaluable training and mentorship, fellows gain access to online masterclasses and monthly group sessions, supported by the AI Mission Lab. A key outcome of the fellowship is the publication of the fellow’s research report on Polyfoni’s website and in the Making AI Listen open-access publication. This provides fellows with a platform to share their findings and contribute to the broader discourse on AI ethics and governance. The fellowship is open to a wide range of disciplinary backgrounds, including anthropology, sociology, Science and Technology Studies (STS), development studies, political science, humanities, and area studies. While there are no strict degree requirements, a strong track record of fieldwork experience in a Global South context and a demonstrated interest in technology careers are highly valued. Prior experience with AI is not a prerequisite, making the fellowship accessible to those looking to transition their social science skills into the AI domain.
Understanding the Polyfoni Social Sciences for AI Fellowship
The Polyfoni Social Sciences for AI Fellowship is a unique program designed to integrate social science perspectives into the development and evaluation of artificial intelligence. It recognizes that AI systems, while technologically advanced, are created by humans and deployed within complex human societies. Therefore, understanding the social, cultural, and ethical implications of AI is paramount. The fellowship specifically targets individuals who can bring their deep understanding of human behavior, cultural contexts, and societal structures to bear on the challenges posed by AI.
The program’s structure emphasizes a practical, research-oriented approach. Fellows are not expected to be AI developers but rather critical observers and analysts. They are tasked with examining existing AI products through the lens of social science, identifying how these products reflect, reinforce, or challenge existing societal norms and assumptions. This critical examination is crucial for ensuring that AI technologies serve humanity equitably and ethically. The fellowship’s commitment to fieldwork experience underscores the importance of grounded, real-world data in understanding the impact of AI.
The Cultural Audit Method: A Deep Dive
At the heart of the Polyfoni Fellowship lies its distinctive Cultural Audit Method. This methodology provides a systematic framework for social scientists to analyze AI products. It moves beyond a purely technical assessment to explore the cultural assumptions embedded within the AI and its performance in diverse contexts.
Assumption Mapping
The first step in the Cultural Audit Method is assumption mapping. This involves identifying the underlying beliefs, values, and expectations that the developers of an AI product have implicitly or explicitly built into the system. These assumptions can relate to user behavior, cultural norms, language, and even the desired outcomes of the AI’s function. For example, a health AI chatbot might assume a certain level of health literacy or a specific cultural understanding of illness and treatment. Mapping these assumptions helps to reveal potential biases or blind spots in the AI’s design.
Contextual Failure Identification
Once assumptions are mapped, the next phase is contextual failure identification. This step focuses on observing how the AI product performs in its real-world deployment, particularly in regions or communities that may differ significantly from the developers’ assumed context. Failures occur when the AI’s embedded assumptions do not align with the actual context, leading to misunderstandings, incorrect outputs, or unintended consequences. For instance, a translation AI might fail when encountering regional dialects or culturally specific idioms that were not part of its training data. Fieldwork experience is invaluable here, as fellows can draw upon their direct knowledge of local contexts to pinpoint these failures.
Severity Assessment
Following the identification of failures, fellows conduct a severity assessment. This involves evaluating the impact and significance of each identified failure. Some failures might be minor inconveniences, while others could have serious consequences, such as misdiagnosing a medical condition, providing harmful advice, or perpetuating discrimination. The assessment considers factors like the potential harm to users, the impact on the AI’s intended function, and the broader societal implications. This step helps prioritize which issues require the most urgent attention.
Recommendations
The final stage of the Cultural Audit Method is developing recommendations. Based on the assumption mapping, failure identification, and severity assessment, fellows propose concrete steps that product teams can take to improve the AI system. These recommendations might involve refining algorithms, updating training data, redesigning user interfaces, or providing clearer contextual information. The goal is to offer practical, actionable advice that can lead to more responsible and culturally sensitive AI development. The output is a report tailored for a product team, making the research accessible and useful for industry professionals.
Fellowship Details and Structure
The Polyfoni Social Sciences for AI Fellowship is structured as a part-time, remote research collaboration. This flexible format allows fellows to balance their research commitments with other professional or personal responsibilities. The fellowship runs for four months, typically from November to March, providing a focused period for in-depth research and analysis.
Remote Collaboration and Fieldwork
The remote nature of the fellowship is a key feature, enabling participation from anywhere in the world. This is particularly beneficial for social scientists based in regions where AI products are being deployed, allowing them to conduct their audits within their familiar environments. The fellowship explicitly states that fellows’ existing fieldwork experience serves as their primary data source. This approach recognizes the immense value of local knowledge and lived experience in understanding the complex interplay between AI and society. Fellows are encouraged to apply their existing contextual understanding through the structured Cultural Audit Method, rather than needing to gather entirely new data from scratch.
Mentorship and Training
A cornerstone of the fellowship is the dual mentorship provided to each fellow. Participants receive guidance from both an academic collaborator and an AI industry professional. This pairing ensures that fellows benefit from both theoretical insights and practical, industry-relevant perspectives. The academic mentor can offer support in research design, theoretical framing, and scholarly writing, while the industry mentor can provide context on AI development processes, product management, and the challenges of implementing social science findings in commercial settings.
In addition to mentorship, fellows receive training in Polyfoni’s Cultural Audit Method. This training is crucial for equipping them with the specific skills and framework needed to conduct their audits effectively. The fellowship also includes online masterclasses and monthly group sessions, often supported by partners like the AI Mission Lab. These sessions offer opportunities for learning, discussion, and peer exchange, fostering a sense of community among the fellows and exposing them to broader discussions in the AI and social science space.
Project Scope and Output
The audit target for each fellow must be a real AI product deployed in a region where the fellow has genuine contextual knowledge. This requirement ensures that the research is grounded and relevant. Polyfoni supervisors propose overarching audit themes that align with their research priorities, but fellows have the autonomy to choose and shape their specific project in close collaboration with their mentors. This collaborative approach allows for both alignment with Polyfoni’s goals and the pursuit of individual research interests.
The primary output of the fellowship is a research report written specifically for a product team. This format emphasizes the practical application of the research, aiming to provide actionable insights that can directly inform the development and improvement of AI products. The reports are then published on Polyfoni’s Substack, under the fellow’s name, ensuring recognition for their work and contributing to public knowledge about AI’s social impact. This publication strategy also includes potential inclusion in Making AI Listen, an open-access publication, further amplifying the reach of the fellows’ research.
Eligibility and Application Process
The Polyfoni Social Sciences for AI Fellowship is designed to be inclusive, welcoming individuals from a diverse range of backgrounds who are passionate about contributing to the responsible development of AI. The program seeks to foster a multidisciplinary cohort, recognizing that a variety of perspectives are essential for understanding the complex challenges of AI.
Who Can Apply?
The fellowship is open to anyone interested in using their social science training to influence how AI products are built, evaluated, and governed. Polyfoni actively seeks candidates from a broad spectrum of disciplines, including but not limited to anthropology, sociology, Science and Technology Studies (STS), development studies, political science, humanities, and area studies. There are no strict academic degree requirements. However, the program anticipates that the most successful applicants will possess significant fieldwork experience, particularly in Global South contexts, and will demonstrate a clear interest in pursuing careers related to technology and AI. Crucially, prior experience with AI is not a prerequisite, making the fellowship an accessible entry point for social scientists looking to engage with this field.
Key Selection Criteria
When evaluating applications, Polyfoni looks for several key attributes:
- Regional Expertise with Evidence of Fieldwork: Applicants must demonstrate deep contextual knowledge of a specific place, community, or sector where AI is currently being deployed. This expertise should be supported by concrete evidence of fieldwork. Priority regions for this cohort include East Africa, South Asia, Southeast Asia, Latin America, and China, reflecting a focus on diverse global contexts.
- Clear Motivation: A strong application will articulate a specific reason for wanting to work at the intersection of social science and AI. Applicants are expected to propose a concrete idea for which AI product they would audit. The fellowship requires a commitment of approximately five hours per week, and applicants should be prepared to dedicate this time to their audit project.
- English Proficiency: While not strictly an academic requirement, comfort and proficiency in working in English are essential, as it is the primary language of the fellowship. Proficiency in additional languages is considered an advantage, especially if relevant to the applicant’s region of expertise.
Polyfoni also explicitly states a commitment to selecting for diversity across both region and discipline. This ensures that the cohort of fellows represents a wide array of perspectives and experiences, enriching the collaborative learning environment.
The Application Steps
The application process for the Polyfoni Fellowship is divided into two main steps:
- Step 1: Initial Application: The process begins with submitting a short application that includes a statement of motivation. This statement has a maximum word count of 500 words and is where applicants articulate their interest, proposed audit topic, and relevant experience. Candidates who are successful in this initial screening will be contacted for the next stage, which involves a review of their curriculum vitae (CV).
- Step 2: Interview and Research Discussion: Final candidates will be invited for an interview. This interview is not just a standard Q&A; it includes a research discussion with the Polyfoni team. This allows the team to delve deeper into the applicant’s research ideas, assess their understanding of the fellowship’s goals, and gauge their potential to contribute to the program.
Following these steps, Polyfoni carefully matches fellows with mentors. This matching process considers the alignment of regional expertise (the fellow’s fieldwork region and the mentor’s company market) and sector alignment (e.g., health, fintech, content moderation). This thoughtful pairing aims to maximize the benefit of the mentorship for each fellow.
The Impact and Future of Social Sciences in AI
The Polyfoni Social Sciences for AI Fellowship is more than just a research program; it is a vital initiative contributing to the broader conversation about the responsible development and deployment of artificial intelligence. As AI technologies become increasingly integrated into nearly every aspect of modern life, the need for social science perspectives has never been greater.
Bridging the Gap Between Technology and Society
AI systems are not neutral tools. They are designed, trained, and deployed within specific social and cultural contexts, and they inevitably shape those contexts in return. Without a deep understanding of human behavior, societal structures, and cultural nuances, AI can inadvertently perpetuate biases, exacerbate inequalities, and lead to unintended negative consequences. Social scientists, with their expertise in these areas, are uniquely positioned to identify and address these challenges. The Polyfoni Fellowship provides a structured pathway for these experts to engage directly with the AI industry, bringing their critical insights to bear on the design and governance of these powerful technologies.
The fellowship’s focus on cultural audits is particularly important. It moves beyond simply identifying technical flaws to understanding how AI products function (or fail to function) within diverse human environments. This approach recognizes that what works in one cultural context may not work in another, and that AI systems must be sensitive to local norms, values, and practices. By conducting these audits, fellows contribute to a more nuanced understanding of AI’s real-world impact, moving away from a one-size-fits-all approach to technology development.
Contributing to Ethical AI Development
Ethical AI development is a growing concern for researchers, policymakers, and the public alike. Issues such as algorithmic bias, data privacy, transparency, and accountability are at the forefront of discussions about AI’s future. Social scientists play a crucial role in these conversations by providing empirical evidence and theoretical frameworks to understand the ethical dimensions of AI. The research produced through the Polyfoni Fellowship directly contributes to this effort. By publishing their findings, fellows help to raise awareness, inform best practices, and advocate for more responsible AI governance.
The fellowship’s emphasis on producing reports for product teams is a strategic choice. It aims to bridge the gap between academic research and industry practice, ensuring that the insights gained from social science analysis are accessible and actionable for those who are actually building AI products. This direct engagement with industry professionals is essential for driving meaningful change in how AI is developed and deployed.
The Future Landscape
The collaboration between social sciences and AI is likely to become even more critical in the coming years. As AI systems become more sophisticated and pervasive, the need for interdisciplinary approaches will only grow. Fellowships like Polyfoni are paving the way for a future where AI is developed not just with technical prowess, but with a deep understanding of its human and societal implications. This ensures that AI can be a force for good, contributing to societal progress in a way that is equitable, inclusive, and beneficial for all. The ongoing research and dialogue fostered by such initiatives are essential for navigating the complex ethical and social challenges that lie ahead in the age of artificial intelligence.
Examples of Audit Targets and Their Significance
The Polyfoni Social Sciences for AI Fellowship encourages fellows to select AI products that are actively deployed in regions where they have deep contextual knowledge. This focus on real-world application and local expertise is what makes the fellowship’s research so impactful. The examples provided illustrate the diverse range of AI applications and the critical need for social science scrutiny across various sectors and geographies.
Health AI Chatbots in East Africa
Consider a health AI chatbot being used in East Africa. Such a tool might be designed to provide basic medical advice, answer questions about symptoms, or guide users on when to seek professional help. However, its effectiveness and safety depend heavily on its understanding of local health practices, common diseases, cultural beliefs surrounding illness, and access to healthcare facilities. A social scientist with fieldwork experience in East Africa might identify failures related to the chatbot’s assumptions about user literacy, its inability to recognize culturally specific symptoms, or its recommendations that are impractical given the local healthcare infrastructure. For instance, a chatbot might recommend immediate hospital visits, which is not feasible for someone living in a remote rural area with limited transportation. The cultural audit would reveal how the chatbot’s design reflects assumptions from a different healthcare context and how these assumptions lead to potentially harmful or ineffective advice in East Africa.
Ocean AI Tools in Southeast Asia
In Southeast Asia, AI tools are increasingly being used for ocean management, such as monitoring marine ecosystems, predicting fishing yields, or tracking illegal fishing activities. These tools often rely on data and algorithms that may not fully account for the diverse local knowledge of fishing communities, traditional fishing practices, or the complex socio-economic realities of coastal populations. A fellow with experience in Southeast Asia might audit an AI tool that, for example, predicts fish stocks. The audit could uncover that the AI’s models are based on Western scientific data and do not incorporate the nuanced, generational knowledge of local fishermen about specific fish behaviors, breeding grounds, or the impact of local environmental factors that are not captured by standard sensors. This could lead to inaccurate predictions, affecting livelihoods and potentially leading to overfishing or misallocation of resources. The cultural audit would highlight how the AI’s assumptions about data sources and ecological understanding fail to integrate vital local expertise.
Agricultural AI Tools in West Africa
Agricultural AI tools are being developed to assist farmers in West Africa with tasks like crop monitoring, pest detection, and yield optimization. These tools often assume a certain level of technological access, farming practices, and land ownership structures. A social scientist with fieldwork experience in West Africa might audit an AI-powered pest detection app. The audit could reveal that the app’s algorithms are trained on data from large-scale industrial farms and do not recognize indigenous crops or local pest variations common in smallholder farming systems prevalent in the region. Furthermore, the app might assume consistent internet connectivity or access to smartphones, which are not universally available. The cultural audit would expose how the AI’s design assumptions about farming methods, technology access, and agricultural diversity lead to its ineffectiveness or even detriment for local farmers.
Large Language Models (LLMs) in Non-English Dominant Markets
The deployment of Large Language Models (LLMs) in markets where English is not the primary language presents another significant area for cultural audits. While LLMs are powerful, their performance can vary dramatically across languages and cultures. An LLM used for customer service, content generation, or information retrieval in a non-English dominant market might exhibit biases or generate outputs that are culturally inappropriate, grammatically awkward, or simply nonsensical. A fellow with expertise in such a market could audit an LLM used for customer support. The audit might uncover that the LLM struggles with local idioms, politeness conventions, or cultural references, leading to frustrating or offensive interactions for users. It might also reveal that the LLM’s underlying assumptions about conversational flow or information seeking behavior are rooted in Western communication styles. The audit would demonstrate how the LLM’s training data and design principles fail to capture the linguistic and cultural richness of the target market.
These examples underscore the critical role of social scientists in ensuring that AI technologies are developed and deployed in a manner that is sensitive to the diverse contexts in which they operate. The Polyfoni Fellowship provides a vital platform for this work, empowering researchers to contribute their expertise to the creation of more equitable and effective AI systems worldwide.
Conclusion
The Polyfoni Social Sciences for AI Fellowship stands as a crucial initiative at the intersection of technology and society. By equipping social scientists with the tools and framework to conduct cultural audits of AI products, the fellowship fosters a deeper understanding of how these technologies interact with diverse human contexts. The program’s emphasis on fieldwork experience, dual mentorship, and practical output ensures that research is grounded, relevant, and actionable for AI developers. As AI continues to shape our world, the insights gained from such interdisciplinary collaborations are indispensable for promoting ethical development, mitigating unintended consequences, and ensuring that AI serves humanity in a responsible and equitable manner. The fellowship not only empowers individual researchers but also contributes significantly to the broader goal of building AI that is truly aligned with human values and societal needs.