AI ethics
Responsible AI needs practice, not slogans.
This hub gathers Kris Krug’s writing and teaching on bias, data, provenance, labor, authorship, open source, sovereignty, and the uncomfortable parts of deploying AI in real communities.
The working posture is practical: name the tradeoffs, keep people in the loop, and refuse systems that make humans smaller.
Questions worth keeping in the room
Who benefits?
AI strategy should make power visible: who gains capacity, who loses agency, and who is being asked to absorb the risk?
What data is being used?
Bias, consent, privacy, provenance, and data quality are not side quests. They shape the system before anyone sees the interface.
What remains human?
Judgment, accountability, relationship, care, taste, and refusal are not bugs to automate away.
How do teams practice?
Responsible AI becomes real through workflows, review rituals, escalation paths, training, and visible accountability.
Source trail
Punk Rock AI
A culture-first way to use powerful systems without becoming obedient to them.
Responsible AI Professional
A practical certification path for teams turning ethics into operating practice.
AI ethics archive
Posts on bias, open systems, data, creative labor, and the politics inside the tools.
Turn responsibility into practice
For training, talks, or applied AI governance that people can actually use, start with the decisions your team is making now.