Indie game developers are often defined as game developers who are typically not employed by or affiliated with tech giants or large gaming companies/publishers. Although people may decide to "go indie" for various purposes, indie game development has become a crucial part of the global gaming culture. However, this community is now facing unprecedented tensions as generative AI technologies are shifting how games can be designed, produced, and experienced. Through a qualitative analysis of 3,091 online posts and comments from subreddits and Facebook groups for indie game developers, we offer an in-depth investigation of how indie game developers perceive and envision the multifaceted role of generative AI in their creative practices. Our empirical investigation reveals that generative AI both promotes and harms indie game developers' endeavors to innovate the traditional game production model, which further influences the nature and workflow of creativity in game development. We also propose three principles for designing future generative AI technologies to improve indie developers' work while mitigating potential risks, harm, and negative impacts of AI. We hope that this study can help design and develop future generative AI technologies to foster and sustain more democratic and inclusive practices in game development rather than replacing human creators.

We investigate the potential for nationality biases in natural language processing (NLP) models using human evaluation methods. Biased NLP models can perpetuate stereotypes and lead to algorithmic discrimination, posing a significant challenge to the fairness and justice of AI systems. Our study employs a two-step mixed-methods approach that includes both quantitative and qualitative analysis to identify and understand the impact of nationality bias in a text generation model. Through our human-centered quantitative analysis, we measure the extent of nationality bias in articles generated by AI sources. We then conduct open-ended interviews with participants, performing qualitative coding and thematic analysis to understand the implications of these biases on human readers. Our findings reveal that biased NLP models tend to replicate and amplify existing societal biases, which can translate to harm if used in a sociotechnical setting. The qualitative analysis from our interviews offers insights into the experience readers have when encountering such articles, highlighting the potential to shift a reader’s perception of a country. These findings emphasize the critical role of public perception in shaping AI’s impact on society and the need to correct biases in AI systems.

Pranav Narayanan Venkit, Sanjana Gautam, Ruchi Panchanadikar, Ting-Hao Huang, Shomir Wilson. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.