Topic: bias

In the current virtual journal club Dr Di Luca will present findings from a series of psychophysical investigations where he measured sensitivity and bias in the perception of the timing of stimuli. He will present how improved detection with longer sequences and biases in reporting isochrony can be accounted for by optimal statistical predictions. Among his findings was also that the timing of stimuli that occasionally deviate from a regularly paced sequence is perceptually distorted to appear more regular. Such change depends on whether the context these sequences are presented is also regul

In this webinar, Dr David Pagliaccio discusses the findings of his recent pre-print on workplace bias and discrimination faced by LGBTQIA+ brain scientists in the US.

With the increasing use of automated face recognition (AFR) technologies, it is important to consider whether these systems not only perform accurately, but also equitability or without “bias”. Despite rising public, media, and scientific attention to this issue, the sources of bias in AFR are not fully understood. This talk will explore how human cognitive biases may impact our assessments of performance differentials in AFR systems and our subsequent use of those systems to make decisions. We’ll also show how, if we adjust our definition of what a “biased” AFR algorithm looks like, we may

Gradient descent is a central tool in modern machine learning, largely because it scales well to train large networks on computationally challenging tasks. As such, computational neuroscientists are exploring potential means by which the brain could leverage gradient descent. However, it is unlikely that the brain could implement perfect gradient descent, but instead could rely on using approximations of the true gradient signal. Therefore, it is likely that the gradient estimates used by the brain would have both bias and variance. Here, we use model agnostic mathematical analysis and simulat

A single person can be the source of billions of data points, whether these are generated from everyday internet use, healthcare records, wearable sensors or participation in experimental research. This vast amount of data can be used to make predictions about people and systems: what is the probability this person will develop diabetes in the next year? Will commit a crime? Will be a good employee? Is of a particular ethnicity? Predictions are simply represented by a number, produced by an algorithm. A single number in itself is not biased. How that number was generated, interpreted and subse

ALBA is organizing a special event on ‘Bias in Indian STEM’ at the online conference NeuroFemIndia 2021. Prof Shubha Tole (Tata Institute of Fundamental Research), ALBA Advisor, will be moderating and leading the discussion on biases in Indian STEM academia. The panel will discuss the main biases that women and minorities in India face as they navigate the academic system. This event is part of the NeuroFemIndia Online Conference 2021.