The controversy

Twitter was in the headlines recently for apparent racial bias in the photo preview of some tweets. More specifically, Twitter’s machine learning algorithm that selects which part of an image to show in a photo preview favors showing the faces of white people over black people. For example the following tweet, contains an image of Mitch McConnell (white male) and Barack Obama (black male) twice, but Twitter selects Mitch McConnell both times in the tweet’s preview photo.

Human rights defenders across the world are fighting facial recognition surveillance

Researchers Find Racial Bias in Hospital Algorithm

Viral Tweet About Apple Card Leads to Goldman Sachs Probe

Google using dubious tactics to target people with ‘darker skin’ in facial recognition project: sources

It’s time we faced up to AI’s race problem

Tell HUD: Algorithms Shouldn’t Be an Excuse to Discriminate

And I am afraid that the list above will keep growing and creating more concerns…

You see, those of us who believe technology can change the world for the better and are passionately involved with making algorithms that are…

Using explanations for finding bias in black-box models — The need to shed light on black box models

There is no doubt, that machine learning (ML) models are being used for solving several business and even social problems. Every year, ML algorithms are getting more accurate, more innovative and consequently, more applicable to a wider range of applications. From detecting cancer to banking and self-driving cars, the list of ML applications is never ending.

However, as the predictive accuracy of ML models is getting better, the explainability of such models is seemingly getting weaker. Their intricate and obscure inner structure forces us more often than not to treat them as “black-boxes”, that is, getting their predictions in a…

The Black Box Society book was a source of inspiration for this article (among others).

For the last 14 years I have been conducting research and then practicising consultancy on software quality matters. I was merely trying to find answers to questions like: What defines good software? How can we measure it? How can we make its technical quality transparent? I wouldn’t be boasting if I say that together with my colleagues at Software Improvement Group have done and still do some good work in trying to answer these questions.

But it is the last years I sense some new…

During the last months, I spent (quality) time with people of diverse backgrounds and roles; from executives in the banking sector, founders of health or tech startups and translators to name a few, discussing the impact of technology and algorithmic decision making on their daily work. Not surprisingly the gravity of the deducted decisions as they perceive them (or cognitive insights in a broader sense), is growing very fast.

Interestingly also, most of the people I talked to, had an experience of a slight or serious bias in the deducted insight, that essentially they could bypass using their own intuition…

As programmers/coders we all have to revisit/review/debug our own code as well as others’. Some times the code can be as large as thousands of LoC (=Lines of Code)! Large projects have a large overhead on understanding, before someone is able to add new functionality or fix a bug! Even my own projects seem somewhat incomprehensible at first, when I revisit them after a large period of time! I was always intrigued by simple solutions, but I could never form a few simple guidelines of code legibility that one could follow anywhere, anyhow, with any GUI and with any Framework!

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