65 citations · 145 across the 28 of their papers we have counts for
8 papers · 1 filter
Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction
Arjun Subramonian, Levent Sagun, Yizhou Sun
Graph neural network (GNN) link prediction is increasingly deployed in citation, collaboration, and online social networks to recommend academic literature, collaborators, and frie…
Bound by the Bounty: Collaboratively Shaping Evaluation Processes for Queer AI Harms
Organizers of QueerInAI, Nathan Dennler, Anaelia Ovalle +11
Bias evaluation benchmarks and dataset and model documentation have emerged as central processes for assessing the biases and harms of artificial intelligence (AI) systems. However…
Weisfeiler and Leman Go Measurement Modeling: Probing the Validity of the WL Test
Arjun Subramonian, Adina Williams, Maximilian Nickel +2
The expressive power of graph neural networks is usually measured by comparing how many pairs of graphs or nodes an architecture can possibly distinguish as non-isomorphic to those…
Evaluating the Social Impact of Generative AI Systems in Systems and Society
Irene Solaiman, Zeerak Talat, William Agnew +28
Generative AI systems across modalities, ranging from text (including code), image, audio, and video, have broad social impacts, but there is no official standard for means of eval…
It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance
Arjun Subramonian, Xingdi Yuan, Hal Daumé +1
Progress in NLP is increasingly measured through benchmarks; hence, contextualizing progress requires understanding when and why practitioners may disagree about the validity of be…
Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness
Anaelia Ovalle, Arjun Subramonian, Vagrant Gautam +2
Intersectionality is a critical framework that, through inquiry and praxis, allows us to examine how social inequalities persist through domains of structure and discipline. Given…