13 citations · 25 across the 6 of their papers we have counts for
6 papers
Toward a Theory of Value in AI Alignment
Andrew Smart, Shazeda Ahmed, Jackie Kay +3
Can AI systems be aligned to human values? The popularization of large language models (LLMs) and multi-modal foundation models has seen a rise in harms spanning from toxic speech…
How Tech Workers Contend with Hazards of Humanlikeness in Generative AI
Mark Díaz, Renee Shelby, Eric Corbett +1
Generative AI's humanlike qualities are driving its rapid adoption in professional domains. However, this anthropomorphic appeal raises concerns from HCI and responsible AI scholar…
Socially Responsible Data for Large Multilingual Language Models
Andrew Smart, Ben Hutchinson, Lameck Mbangula Amugongo +12
Large Language Models (LLMs) have rapidly increased in size and apparent capabilities in the last three years, but their training data is largely English text. There is growing int…
Beyond Model Interpretability: Socio-Structural Explanations in Machine Learning
Andrew Smart, Atoosa Kasirzadeh
What is it to interpret the outputs of an opaque machine learning model. One approach is to develop interpretable machine learning techniques. These techniques aim to show how mach…
Discipline and Label: A WEIRD Genealogy and Social Theory of Data Annotation
Andrew Smart, Ding Wang, Ellis Monk +4
Data annotation remains the sine qua non of machine learning and AI. Recent empirical work on data annotation has begun to highlight the importance of rater diversity for fairness,…
Assessing LLMs for Moral Value Pluralism
Noam Benkler, Drisana Mosaphir, Scott Friedman +2
The fields of AI current lacks methods to quantitatively assess and potentially alter the moral values inherent in the output of large language models (LLMs). However, decades of s…