The importance of transparency and reproducibility in artificial intelligence research

02/28/2020
by   Benjamin Haibe-Kains, et al.
0

In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines its scientific value. We identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al and provide solutions with implications for the broader field.

READ FULL TEXT
research
09/04/2019

What can the brain teach us about building artificial intelligence?

This paper is the preprint of an invited commentary on Lake et al's Beha...
research
06/02/2023

Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables

In recent years, the community of 'explainable artificial intelligence' ...
research
10/08/2016

Deep Convolutional Networks as Models of Generalization and Blending Within Visual Creativity

We examine two recent artificial intelligence (AI) based deep learning a...
research
01/17/2018

Innateness, AlphaZero, and Artificial Intelligence

The concept of innateness is rarely discussed in the context of artifici...
research
03/01/2023

On Kenn's Rule of Combination Applied to Breast Cancer Precision Therapy

This short technical note points out an erroneous claim about a new rule...
research
09/10/2021

Open-World Active Learning with Stacking Ensemble for Self-Driving Cars

The environments, in which autonomous cars act, are high-risky, dynamic,...

Please sign up or login with your details

Forgot password? Click here to reset