Algorithmic fairness has inherent mathematical limits

Photo by Tingey Injury Law Firm on Unsplash

As Machine Learning (ML) is increasingly used across core social domains such as advertising, credit, employment, education, and criminal justice to assist (when not making decisions altogether) decision-makers, it is crucial to ensure that these decisions are not biased or discriminatory. In some jurisdictions, those domains are protected by anti-discrimination laws, making measuring and ensuring fairness a legal issue more than just an ethical one. For example, a White House report released in 2016 called for “equal opportunity by design” as a guiding principle in domains such as credit scoring.

Threshold classifiers: a loan granting use case

Some ML systems that are subject to the risk of…

A guide to set up your web app in the cloud in 15 minutes

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In this post, I want to explain my favourite procedure to deploy python webapps into Google Cloud Platform (GCP). I write my code using Jupyter Notebooks, an interactive programming environment for data science. Notebooks allow to develop on the prompt (or REPL) and are great at exploratory data analysis, testing ML models, experimenting with different algorithms, and so on. However, recently notebooks have become much more than an exploratory programming tool: with the addition of a module like , we now have a complete IDE editor/development…

Racial and Gender Bias in AI

Outspoken US Congresswoman Alexandria Ocasio-Cortez recently said that AI can be biased during a Martin Luther King Jr. day event in New York City. She was of course derided the following day by conservative commentators.

But she is right.

Joy Buolamwini, an MIT scientist and founder of the Algorithmic Justice League, published a research that uncovered large gender and racial bias in AI systems sold by tech giants like IBM, Microsoft, and Amazon. Given the task of guessing the gender of a face, all companies performed substantially better on male faces than female faces. The error rates were no more…


Cutting-edge algorithms typically used in Data Science competitions can be extended to handle real life problems. In this project , we used unsupervised learning techniques to describe the relationship between the demographics of the company’s existing customers and the general population of Germany .
This allowed to describe which parts of the general population are more likely to be part of the mail-order company’s main customer base, and which parts of the general population are less so (Customer Segmemtation).

We then developed a supervised learning model to predict which individuals (leads) are most likely to convert into customers using a…

Maurizio Santamicone

AI Expert at Ooredoo Qatar. Founder at Fliptin Technologies, Soulstice Consulting. Investor. Startups. AI, Machine Learning.

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