Kingsley, Sara
A Cognitive Science perspective for learning how to design meaningful user experiences and human-centered technology
Kingsley, Sara
Misinterpreted or misleading in cognitive science, human-computer interaction (HCI) and stories or facts are known to "go viral" and to increase the natural-language processing (NLP) to consider how analogical likelihood for incivility [11]. Referred to as "misinformation" reasoning (AR) could help inform the design of communication or "disinformation," the phenomenon is, in part, a product of and learning technologies, as well as online communities (exploiting) analogical reasoning and normal cognitive processes and digital platforms. First, analogical reasoning (AR) is [3, 19]. Problematically, digital platforms are efficient defined, and use-cases of AR in the computing sciences are mechanisms for spreading rumors, participating in misinterpretations, presented. The concept of schema is introduced, along with and for misconstruing fact-sharing as opinion [16].
SECure: A Social and Environmental Certificate for AI Systems
Gupta, Abhishek, Lanteigne, Camylle, Kingsley, Sara
In a world increasingly dominated by AI applications, an understudied aspect is the carbon and social footprint of these power-hungry algorithms that require copious computation and a trove of data for training and prediction. While profitable in the short-term, these practices are unsustainable and socially extractive from both a data-use and energy-use perspective. This work proposes an ESG-inspired framework combining socio-technical measures to build eco-socially responsible AI systems. The framework has four pillars: compute-efficient machine learning, federated learning, data sovereignty, and a LEEDesque certificate. Compute-efficient machine learning is the use of compressed network architectures that show marginal decreases in accuracy. Federated learning augments the first pillar's impact through the use of techniques that distribute computational loads across idle capacity on devices. This is paired with the third pillar of data sovereignty to ensure the privacy of user data via techniques like use-based privacy and differential privacy. The final pillar ties all these factors together and certifies products and services in a standardized manner on their environmental and social impacts, allowing consumers to align their purchase with their values.