The hazard of being a wizard. On balance between specialization and the risk to become obsolete.

A wizard is a person who continually improves his or her professional skill in a particular and defined field. I learned about this definition of wizardness from the book “Managing project, people and yourself” by Nikolay Toverosky (the book is in Russian).  

Recently, Nikolay published an interesting post about the hazards of becoming a wizard. The gist of the idea is that while you are polishing your single skill to perfection, the world changes. You may find your super-skill irrelevant anymore (see my Soviet Shoemaker story).

Nikolay doesn’t give any suggestions. Neither do I. 

Below is the link to the original post. The post is in Russian, and you can use Google Translate to read it.

Страница о магах У меня в книге есть глава про полководцев и магов. В её конце я подвожу итог: Несмотря на свою кру­тость, маг уяз­вим. Он поле­зен, только если его навык под­хо­дит к задаче. 658 more words

Почему опасно быть магом — Об управлении проектами и дизайне

On algorithmic fairness & transparency

My teammate, Charles Earl has recently attended the Conference on Fairness, Accountability, and Transparency (FAT*). The conference site is full of very interesting material, including proceedings and video recording of lectures and tutorials.

Reading through the conference proceedings, I found a very interesting paper titled “The Cost of Fairness in Binary Classification.” This paper talks about the measures one needs to take in order not use sensitive features (such as race) as the means to discrimination, with a reasonable accuracy tradeoff.

Skimming through this paper, I recalled a conversation I had about a year ago with a chief data scientist in a startup that provides short-term loans to people who need some money now. The major job of the data science team in that company was to assess the risk of a customer. From the explanation the chief data scientist gave, and from the data sources she described, it was clear that they train their model on the information whether a person is likely to receive a loan from a financial institution. When I pointed out that they exclude categories of people that are rejected but are likely to return the money. “Yes?” she said in a tone as if she couldn’t see what the problem that I tried to raise was. “Well,” I said, it’s unfair for many customers, plus you’re missing the chance to recruit customers who were rejected by others”. “We have enough potential customers,” she said. She didn’t think fairness was an issue worth talking about.


The featured image is by Søren Astrup Jørgensen from Unsplash


Identifying and overcoming bias in machine learning

Data scientists build models using data. Real-life data captures real-life injustice and stereotypes. Are data scientists observers whose job is to describe the world, no matter how unjust it is? Charles Earl, an excellent data scientist, and my teammate says that the answer to this question is a firm “NO.” Read the latest post to learn Charles’ arguments and best practices.

Charles Earl on identifying and overcoming bias in machine learning.

via Data Speaker Series: Charles Earl on Discriminatory Artificial Intelligence — Data for Breakfast