UX and the Changing Relationship Between Mind and Machine
summary
Argues that machine learning and deep learning are tightening feedback loops with people, using concrete examples: email response suggestions, fraud-detection network graphs that require a human-in-the-loop now, ICU alarm overload and alarm fatigue, and anti-lock brake and Segway behavior changes. Claims current interfaces force learned behaviors that can mislead or entrench analysts, that ML systems are becoming less transparent, and that human oversight is necessary today though many manual steps will be automated.
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chapters (17)
- 0:00Start
- 1:16UX Design
- 2:18Reciprocal Change
- 4:36Organisms are Algorithms
- 10:11Dynamics, Effects, and Design
- 11:22Feedback Loops
- 19:03Thinking Machines
- 24:39Input/Output and Design Choices
- 25:07Signal
- 25:39Detector Proliferation
- 26:28Visual Prioritization
- 26:51Learned Behavior and Mimicry
- 30:22The Red Queen Effect
- 32:16Feedback and Collaboration
- 36:02Asymmetries and Diversification
- 38:20Specialization and Dependency
- 39:04Al and Human Biological Evolution
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