Two papers at the CHI conference this year.
(2021) Algorithmic Food Justice: Co-Designing More-than-Human Blockchain Futures for the Food Commons, Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, New York, NY, USA: Association for Computing Machinery, pdf, doi:10.1145/3411764.3445655
Abstract
The relationships that constitute the global industrial food system tend towards two dominant values that are creating unsustainable social and environmental inequalities. The first is a human-centered perspective on food that privileges humans over all other species. The second is a view of food as a commodity to be traded for maximum economic value, rewarding a small number of shareholders. We present work that explores the unique algorithmic affordances of blockchain to create new types of value exchange and governance in the food system. We describe a project that used roleplay with urban agricultural communities to co-design blockchain-based food futures and explore the conditions for creating a thriving multispecies food commons. We discuss how the project helped rethink algorithmic food justice by reconfiguring more-than-human values and reconfiguring food as more-than-human commons. We also discuss some of the challenges and tensions arising from these explorations.
The relationships that constitute the global industrial food system tend towards two dominant values that are creating unsustainable social and environmental inequalities. The first is a human-centered perspective on food that privileges humans over all other species. The second is a view of food as a commodity to be traded for maximum economic value, rewarding a small number of shareholders. We present work that explores the unique algorithmic affordances of blockchain to create new types of value exchange and governance in the food system. We describe a project that used roleplay with urban agricultural communities to co-design blockchain-based food futures and explore the conditions for creating a thriving multispecies food commons. We discuss how the project helped rethink algorithmic food justice by reconfiguring more-than-human values and reconfiguring food as more-than-human commons. We also discuss some of the challenges and tensions arising from these explorations.
(2021) Social Sensemaking with AI: Designing an Open-Ended AI Experience with a Blind Child, Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, New York, NY, USA: Association for Computing Machinery, pdf, doi:10.1145/3411764.3445290
Abstract
AI technologies are often used to aid people in performing discrete tasks with well-defined goals (e.g., recognising faces in images). Emerging technologies that provide continuous, real-time information enable more open-ended AI experiences. In partnership with a blind child, we explore the challenges and opportunities of designing human-AI interaction for a system intended to support social sensemaking. Adopting a research-through-design perspective, we reflect upon working with the uncertain capabilities of AI systems in the design of this experience. We contribute: (i) a concrete example of an open-ended AI system that enabled a blind child to extend his own capabilities; (ii) an illustration of the delta between imagined and actual use, highlighting how capabilities derive from the human-AI interaction and not the AI system alone; and (iii) a discussion of design choices to craft an ongoing human-AI interaction that addresses the challenge of uncertain outputs of AI systems.
AI technologies are often used to aid people in performing discrete tasks with well-defined goals (e.g., recognising faces in images). Emerging technologies that provide continuous, real-time information enable more open-ended AI experiences. In partnership with a blind child, we explore the challenges and opportunities of designing human-AI interaction for a system intended to support social sensemaking. Adopting a research-through-design perspective, we reflect upon working with the uncertain capabilities of AI systems in the design of this experience. We contribute: (i) a concrete example of an open-ended AI system that enabled a blind child to extend his own capabilities; (ii) an illustration of the delta between imagined and actual use, highlighting how capabilities derive from the human-AI interaction and not the AI system alone; and (iii) a discussion of design choices to craft an ongoing human-AI interaction that addresses the challenge of uncertain outputs of AI systems.