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 ...

Biased sampling occurs frequently in economics, epidemiology, and medical studies either by design or due to data collecting mechanism. Failing to take into account the sampling bias usually leads to ...

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 ...

Ravishankar, Pavan, Qingyu Mo, Edward McFowland III, and Daniel B. Neill. "Provable Detection of Propagating Sampling Bias in Prediction Models." Proceedings of the AAAI Conference on Artificial ...

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 ...

Recently, an Association Workforce Monitor online survey conducted by the Harris Poll asked over 2,000 U.S. adults their thoughts on AI recruiting tools. About one-third of respondents in this recent ...

While appreciation that algorithms and machine-learning programs are not immune to bias is increasingly mainstream, ongoing plans to correct for bias in said programs among businesses that use them ...

The biggest-ever study of real people’s mortgage data shows that predictive tools used to approve or reject loans are less accurate for minorities. We already knew that biased data and biased ...