Emotion recognition in workplace or education

Remove emotion inference from workplace and education features, or confine it to a documented medical or safety purpose behind an explicit, default-off gate.

Strip emotion-inference calls (DeepFace.analyze with the emotion action, FER().detect_emotions, Hume expression measurement, Rekognition face attributes that return Emotions) from every code path serving employees, applicants, students or learners: proctoring, engagement or attention dashboards, interview analysis, meeting analytics. Where a medical or safety use is intended, isolate it in its own module behind a purpose flag that is off by default for workplace and education deployments, and record the purpose and who approved it. Do not swap the removed model for an LLM or vision prompt that infers mood from the same face or voice input.

Where it goes: 1 application source code, 3 config and feature flags, 9 AI output handling.

What reviewers look for: no DeepFace.analyze(..., actions including 'emotion'), FER(), .detect_emotions(, HumeClient expression measurement, Rekognition detect_faces(Attributes=['ALL']) or emotion_score / student_attention / employee_sentiment features on workplace or education paths; any retained emotion inference sits in a separately gated medical or safety module with its purpose recorded.

resp = rekognition.detect_faces(Image={'Bytes': frame}, Attributes=['ALL'])
mood = resp['FaceDetails'][0]['Emotions']
engagement.record(employee_id, mood)

resp = rekognition.detect_faces(Image={'Bytes': frame}, Attributes=['DEFAULT'])
# headcount only: no Emotions attribute requested, nothing tied to an employee
room.record_headcount(len(resp['FaceDetails']))

Engineering guidance, not legal advice. Each provision below may add its own details (a cadence, a deadline, a required notice element): open it for those.