Don’t be wrong because you might be fooled: Tips on how to secure your ML model

May 17, 2022

Figuring out the reasons why your ML model might be consistently less accurate in certain classes than others, might help you increase not only its total accuracy but also its adversarial robustness. Introduction Machine Learning (ML) models and especially the Deep Learning (DL) ones can achieve impressive results, especially on unstructured data like images and […]

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Ethos 1.0, or the need to build software with intrinsic human values

November 7, 2018

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 […]

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On Algorithm Accountability: Can we control what we can’t exactly measure?

May 11, 2018

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 […]

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