algorithms

Adobe profiled and interviewed Big Medium’s Josh Clark about the opportunities and challenges facing UX designers in a world of machine learning and artificial intelligence.

Mike Loukides explains why predictive algorithms must be imperfect (and error prone) in order to contend with real-world data.

Alexis Madrigal considers the creative future of machine-generated sketches—and what it teaches us about how humans think, too.

Machine learning is technology’s new frontier, and designers have a crucial part to play. Josh Clark explores our new roles and responsibilities when we design for the machines.

Joy Buolamwini surfaces bias in facial recognition software—and responds to some screwed-up criticism.

Bloomberg peeks in on the tedious worklife of humans pretending to be robots pretending to be humans.

Facebook is prototyping its omniscient bot by powering it with people, the MIT Technology Review reports.

In Backchannel, David Weinberger considers the implications of machines constructing their own models for understanding data, quite divorced from our own (more simplistic) models.

MIT Technology Review covers the unknowable logic of our most sophisticated algorithms—and the implications of having no idea how they actually work.

Google’s move to highlight fact-checking sites may be a promising step in identifying hostile information zones in search results.

Google and others offer wrong answers with matter-of-fact authority. How can designers build stronger signals of trust into data-driven interfaces?