xai4debugging.github.io - eXplainable AI approaches for debugging and diagnosis. | Workshop @ NeurIPS2021 14 December

Description: Workshop @ NeurIPS2021 14 December

Example domain paragraphs

Recently, artificial intelligence has seen the explosion of deep learning models, which are able to reach super-human performance in several tasks, finding application in many domains. These performance improvements, however, come at a cost: DL models are uninterpretable black boxes, where one feeds an input and obtains an output without understanding the motivations behind that prediction or decision.

To address this problem, two research areas are particularly active: the eXplainable AI (XAI) field and the visual analytics community. The eXplainable XAI field tries to address such problems by proposing algorithmic methods that can explain, at least partially, the behavior of these networks. Their works also try to define the limits of interpretability, the definition of valid metrics, the study of users, and the study of the effectiveness of these solutions. Conversely, visual analytics systems target u

The workshop aims at advancing the discourse by collecting novel methods and discussing challenges, issues, and goals around the usage of XAI approaches to debug and improve current deep learning models. To achieve this goal, the workshop aims at bringing researchers and practitioners from both fields, strengthening their collaboration. In particular, we narrow the XAI focus to the specific case in which developers or researchers need to debug their models and diagnose system behaviors. Therefore we start f

Links to xai4debugging.github.io (7)

himalakkaraju.github.io Hima Lakkaraju
shivanshpatel35.github.io Shivansh Patel
arjunakula.com Arjun Akula ( Arjun Reddy Akula ) - UCLA
vinitra.github.io Vinitra Swamy
yamunak.com Yamuna Krishnamurthy
yamsgithub.github.io Yamuna Krishnamurthy
agarwalsiddhant10.github.io Siddhant Agarwal