Don’t Automate Your Moat: Matching AI Autonomy to Risk and Competitive Stakes

AI success comes from redesigning processes from scratch rather than automating old habits.

Also in today’s newsletter: a new company seeks to tackle the power constraints on European data centre growth

Enterprises are experimenting with AI agents internally first, using smaller testing teams and strict governance before deploying customer-facing applications.

Armed with some Python and a white-hot sense of injustice, one medical student spent six months trying to figure out whether an algorithm trashed his job application.

Both parties can agree to do a whole lot on A.I., but they have to move faster.

How can you validate that your variables tell a consistent risk? The post How to Study the Monotonicity and Stability of Variables in a Scoring Model using Python appeared first on Towards Data Science.

Gothenburg promised to optimise school admissions with a piece of code. The resulting chaos showed how unaccountable systems are ruining lives We like to imagine that injustice announces itself loudly. That when something goes wrong in the public system, alarms go off and someone takes responsibility or is held accountable if they do not. But in 2020 in Gothenburg, injustice arrived quietly, disguised as efficiency. For the first time, the city used an algorithm to allocate places in its schools. After all, working out geographical catchment areas and admissions is an administrative headache for any municipality. What better than a machine to optimise distances, preferences and capacity? The system was designed to serve public efficiency: framed as neutral, streamlined and objective. Charlotta Kronblad researches digital transformation at the University of Gothenburg. Continue reading...

An AI agent designed to speed up a company's coding instead wiped out its customer data in seconds, showing potential weaknesses in AI programming.