11 citations · 21 across the 5 of their papers we have counts for
6 papers
Looking at Creative ML Blindspots with a Sociological Lens
Katharina Burgdorf, Negar Rostamzadeh, Ramya Srinivasan +1
How can researchers from the creative ML/AI community and sociology of culture engage in fruitful collaboration? How do researchers from both fields think (differently) about creat…
Quantifying Confounding Bias in Generative Art: A Case Study
Ramya Srinivasan, Kanji Uchino
In recent years, AI generated art has become very popular. From generating art works in the style of famous artists like Paul Cezanne and Claude Monet to simulating styles of art m…
Biases in Generative Art -- A Causal Look from the Lens of Art History
Ramya Srinivasan, Kanji Uchino
With rapid progress in artificial intelligence (AI), popularity of generative art has grown substantially. From creating paintings to generating novel art styles, AI based generati…
Generating User-friendly Explanations for Loan Denials using GANs
Ramya Srinivasan, Ajay Chander, Pouya Pezeshkpour
Financial decisions impact our lives, and thus everyone from the regulator to the consumer is interested in fair, sound, and explainable decisions. There is increasing competitive…
Crowdsourcing in the Absence of Ground Truth -- A Case Study
Ramya Srinivasan, Ajay Chander
Crowdsourcing information constitutes an important aspect of human-in-the-loop learning for researchers across multiple disciplines such as AI, HCI, and social science. While using…
Creation of User Friendly Datasets: Insights from a Case Study concerning Explanations of Loan Denials
Ajay Chander, Ramya Srinivasan
Most explainable AI (XAI) techniques are concerned with the design of algorithms to explain the AI's decision. However, the data that is used to train these algorithms may contain…