Some close friends have asked if I have been analyzing the COVID-19 datasets. Yes, I have been looking at these datasets. However, my analysis has been just out of curiosity and not with the intent of publishing my forecast or recommendations.

Experienced machine learning professionals understand the limitations of modeling. Given what impact they can have on lives, society and economy, one has to understand their social responsibility in communicating insights.

Hannah Davis works with machine learning, which relies on an input dataset to…Tags: bias, Hannah Davis

There is No Noise — Only BiasUnderstanding why noise in machine learning is nothing but biasIn this post, we explain how bias and noise in machine learning are two sides of the same coin.God does not play dice. — Albert EinsteinEinstein famously gave this statement in reaction to the emerging …

A researcher from Queen’s University Belfast has developed an innovative new algorithm that will help make artificial intelligence (AI) fairer and less biased when processing data. Companies often use AI technologies to sift through huge amounts of data in situations such as an oversubscribed job vacancy or in policing when …

We all are familiar with experiments, we read about them in books or newspapers. Researchers/ scientists perform experiments to validate their hypothesis/ statements or to test a new product. Unlike observational studies, experiments are performed in a controlled environment so that the effect of other external factors/variables can be eliminated …

We all are familiar with experiments, we read about them in books or newspapers. Researchers/ scientists perform experiments to validate their hypothesis/ statements or to test a new product. Unlike …

Understanding the Bias-Variance tradeoff at three different levels: simple, intermediate and advanced.In this post, we explain the bias-variance tradeoff in machine learning at three different levels: simple, intermediate and advanced. We will follow up with some illustrative examples and discuss some practical implications in the end.If you can’t explain it …

DataRobot, the leader in enterprise AI, released new research revealing that nearly half (42%) of AI professionals across the U.S. and U.K. are “very” to “extremely” concerned about AI bias. The research -- based on a survey of more than 350 U.S. and U.K. executives involved in AI and machine …

StackOverflow’s annual developer survey concluded earlier this year, and they have graciously published the (anonymized) 2019 results for analysis. They’re a rich view into the experience of software developers around the world — what’s their favorite editor? how many years of experience? tabs or spaces? and crucially, salary. Software engineers’ …

Last week, Paco Nathan referenced Julia Angwin’s recent Strata keynote that covered algorithmic bias. This Domino Data Science Field Note dives a bit deeper into some of the publicly available research regarding algorithmic accountability and forgiveness, specifically around a proprietary black box model used to predict the risk of recidivism, …

Paco Nathan‘s column covers themes of data science for accountability, reinforcement learning challenges assumptions, as well as surprises within AI and Economics. Introduction Welcome back to our new monthly series! September has been the busiest part of “Conference Season” with excellent new material to review. Three themes jump out recently. …

Kate Crawford discussed bias at a recent SF-based City Arts and Lectures talk and a recording of the discussion will be broadcast, May 6th, on KQED and local affiliates. Members of Domino were in the live audience for the City Arts talk. This Domino Data Science Field Note provides insights …

Speaker Bio: Dr. Vivienne Ming was named one of 10 Women to Watch in Tech by Inc. Magazine, she is a theoretical neuroscientist, entrepreneur, and author. She co-founded Socos Labs, her fifth company, an independent think tank exploring the future of human potential. Dr. Ming launched Socos Labs to combine …

If we took a hard look at every model ever built for classifying who is the optimal candidate for- -a credit loan -a job promotion -a free scholarship -or any other opportunity, would we see a pattern in certain groups of people being granted these opportunities over others?

If we took a hard look at every model ever built for classifying who is the optimal candidate for- -a credit loan -a job promotion -a free scholarship -or any other opportunity, would we see a pattern in certain groups of people being granted these opportunities over others?

As data scientists, we are aware that bias exists in the world. We read up on stories about how cognitive biases can affect decision-making. We know that, for instance, a resume with a white-sounding name will receive a different response than the same resume with a black-sounding name, and that …