Fairness and Bias

Thoughtfully (co-)designing AI systems can make a difference in the real world

The AI doomsday scenarios, ignited by books such as The Filter Bubble (2011) and Weapons of Math Destruction (2016), are slowly being superseded by more pragmatic and nuanced views of AI. Views in which we acknowledge we’re in control of AI and able to design them in ways that reflect values of our choice.

Photo by Med Badr Chemmaoui on Unsplash

This shift can be seen in the rising involvement of computer scientists, e.g., through books such as The Ethical Algorithm (2019) or Understand, Manage, and Prevent Algorithmic Mitigate Bias (2019), books that describe and acknowledge the challenges and complexities of algorithmic fairness, but at the same…

Leuk stuk van Lubach, maar als filterbubbelontkenner en iemand die een flink deel van z’n boterham verdient aan het bouwen aan soortgelijke “algoritmen” voel ik me wel geroepen wat nuance in te brengen.

Aanbod of algoritme?

Allereerst Lubachs gekozen neutrale zoekopdracht: “pcr test betrouwbaar”. Hoeveel filmpjes zullen er gaan over de betrouwbaarheid van pcr tests in vergelijking met het aantal complotfilmpjes die de betrouwbaarheid ervan in twijfel trekken? Met een groter aanbod van zulke complotfilmpjes, zul je die simpelweg meer tegenkomen in je resultaten. …

David Graus

AI practitioner. Lead Data Scientist at Randstad Groep Nederland. I’m into RecSys, Natural Language Processing, and Information Retrieval.

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