I myself do epidemiologic research, which rarely calls for developing machine learning models. Instead, I spend my time developing logistic regression models that I have to be able to interpret for the broader scientific (and sometimes non-scientific) community. I have to be able to explain in some easy, risk-communication way not only the…

Summary: This is a discussion of social injustice, real or perceived, promulgated or perpetuated by machine learning models.  We propose a simple solution based on wide spread misunderstanding of what ML models can do.

This is a discussion of social injustice, real or perceived, promulgated or perpetuated by…

Summary:  Bias in modeling has long been a public concern that is now amplified and focused on the disparate treatment models may cause for African Americans.  Defining and correcting the bias presents difficult issues for data scientists that need to be carefully thought through before reaching conclusions.

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The importance of completeness of linear regressions is an often-discussed issue. By leaving out relevant variables the coefficients might be inconsistent.

But why on earth?! 

Assuming a linear complete model of the form:

z = a + bx + cy + ε.

Where z is supposed to be dependent, x and y are independent and ε is the error term.

Now we drop y to check…

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’ salaries are good, and sometimes both eye-watering and news-worthy.

The tech industry is also painfully aware that it does not always live…

Summary:  There is a great hue and cry about the danger of bias in our predictive models when applied to high significance events like who gets a loan, insurance, a good school assignment, or bail.  It’s not as simple as it seems and here we try to take a more nuanced look.  The result is not as threatening as many headlines make it seem.

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Summary:  Flawed data analysis leads to faulty conclusions and bad business outcomes. Beware of these seven types of bias that commonly challenge organizations' ability to make smart decisions.

This is a great article by Lisa Morgan originally published on InformationWeek.com.  See the original article…

Data scientists must always remember that data sets are not objective -  they are selected, collected, filtered, structured and analyzed by human design. Naked and hidden biases in selecting,…