Authentic Dissent

Genuine minority dissent makes a group reason better: a person who truly holds a contrary view makes the majority search more broadly and consider more options, even when the dissenter is wrong. The catch, established by the same research, is that role-played devil's advocacy does not replicate this - assigned dissent gets discounted as performance. So an AI cannot be the dissent; anything a model argues against a plan is constructed, the weaker kind. This skill therefore does not pretend to be the dissenter. It engineers the conditions for real dissent: it audits whether genuine dissent exists, surfaces who holds it, plans how to elicit and protect it, and flags constructed dissent as constructed. The output is a dissent audit and plan.

When to Use

• A group decision shows suspiciously smooth consensus and nobody is really pushing back.
• You can influence how challenge is gathered (who speaks, anonymous input, outside reviewers).
• Before a high-stakes call where you want genuine, not performed, challenge.

When NOT to Use

• As a source of dissent itself: the model's contrarian view is constructed, not authentic (use red-team-light for that, which is honest about being constructed).
• In a purely solo setting with no access to other people - you cannot manufacture authentic dissent.
• When genuine dissent already exists and is being heard.
• To "assign a devil's advocate" and consider the job done (the evidence says that does not deliver the benefit).

Instructions