REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research

5 May 2022

Papers citing "REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research"

5 / 5 papers shown

Title
Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation Maria Eriksson Erasmo Purificato Arman Noroozian Joao Vinagre Guillaume Chaslot Emilia Gomez David Fernandez Llorca ELM 135 1 0 10 Feb 2025
Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review Rock Yuren Pang Hope Schroeder Kynnedy Simone Smith Solon Barocas Ziang Xiao Emily Tseng Danielle Bragg 77 3 0 22 Jan 2025
Fine-Grained Appropriate Reliance: Human-AI Collaboration with a Multi-Step Transparent Decision Workflow for Complex Task Decomposition Gaole He Patrick Hemmer Michael Vossing Max Schemmer U. Gadiraju 45 0 0 19 Jan 2025
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations N. McGreivy Ammar Hakim AI4CE 34 42 0 09 Jul 2024
From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gap Tianqi Kou 44 0 0 19 Apr 2024