Length-biased data analysis and survival modeling have become pivotal in accurately interpreting time-to-event data, particularly in epidemiology and clinical research. Traditional survival analyses ...

Artificial Intelligence (A.I.) systems can inherit biases from the data they are trained on, reflecting human cognitive biases such as implicit bias, data bias, algorithmic bias, and sampling bias.

Your Artstor image groups were copied to Workspace. The Artstor website will be retired on Aug 1st. Diversity and Distributions Vol. 30, No. 6, June 2024 Causes and effects of sampling bias on m ...

In this special guest feature, Sinan Ozdemir, Director of Data Science at Directly, points out how algorithmic bias has been one of the most talked-about issues in AI for years, yet it remains one of ...

Matthieu Jonglez at Progress explores the dangers of data bias and ways to mitigate it In our quest to comprehend the world around us, humans instinctively seek patterns when faced with insufficient ...

Machine learning methods have emerged as promising tools to predict antimicrobial resistance (AMR) and uncover resistance determinants from genomic data. This study shows that sampling biases driven ...

This guest post from Alegion explores the reality of machine learning bias and how to mitigate its impact on AI systems. Artificial intelligence (AI) isn’t perfect. It exists as a combination of ...