Evidence · Advanced · About 5 min
Handle uncertainty
Take a position when the evidence is not complete
Both sides · RebuttalRead the case: AI and the future of work
Resolution Advanced AI will on net improve outcomes for workers
a two-year review of 30 employers records 300 retained workers receiving 4% higher inflation-adjusted pay and working 37 rather than 40 hours per week after AI adoption.
- Sixty entry-level roles were removed and 40 technical roles added.
- Of the 60 displaced workers, 18 entered the new roles, 24 found work elsewhere at 8% lower pay, and 18 were still unemployed six months later.
- The other 22 new roles went to different applicants.
- The review does not isolate AI from every other change or forecast the whole economy.
The affirmative argues that productive tools can improve pay and time for many workers. The negative argues that concentrated losses and barriers to new work can outweigh those gains.
You will almost never have perfect evidence. Debate makes you take a side anyway. Say what the record shows, what your prediction assumes and why the reasons still favor your side.
Uncertainty can lower your confidence or change how you weigh things. It can even make a risk more important. It does not mean nobody wins. Hold the other side’s prediction to the same standard, and make the call you can defend.
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The affirmative takes a clear position and still has to answer job losses and how far its evidence reaches.
The negative takes a clear position too, and can’t treat its own predicted harm as certain.
Later transition outcomes, the distribution of gains and better causal comparisons could strengthen one forecast over the other.
Separate what you know
Split the measured result from what you infer and what you predict.
Make the comparison
Explain which reasons and risks matter most.
Know what would change your mind
Name the condition that would make your position stronger or weaker.
Your response
Negative · RebuttalGive a negative judgment about rejection versus reform. Use the later cohort without overstating its cause, and name a finding that would weaken your case.
Read the case: Meritocracy as practiced
Resolution Meritocracy as practiced in modern societies is a harmful fiction that should be rejected
a hiring audit compares 100 lower-income and 100 higher-income applicants.
- Their anonymous work-sample scores were similar, but CV review shortlisted 30 lower-income and 60 higher-income applicants.
- Among those hired, work-sample scores tracked six-month job performance more closely than CV rank.
- In a later cohort, free preparation and an anonymous first stage reduced the shortlist-rate gap from 30 to 10 percentage points; other changes were not controlled.
The affirmative argues that calling the existing process meritocratic legitimizes an advantage not explained by demonstrated ability. The negative argues that useful ability assessment and the improvement after reform are reasons to repair the practice rather than reject it. Neither side may treat this single practice audit as a description of all societies.
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We favor… because… The later cohort supports… Our case weakens if…
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Two positions under the same uncertainty
Resolution Advanced AI will on net improve outcomes for workers
a two-year review of 30 employers records 300 retained workers receiving 4% higher inflation-adjusted pay and working 37 rather than 40 hours per week after AI adoption.
- Sixty entry-level roles were removed and 40 technical roles added.
- Of the 60 displaced workers, 18 entered the new roles, 24 found work elsewhere at 8% lower pay, and 18 were still unemployed six months later.
- The other 22 new roles went to different applicants.
- The review does not isolate AI from every other change or forecast the whole economy.
The affirmative argues that productive tools can improve pay and time for many workers. The negative argues that concentrated losses and barriers to new work can outweigh those gains.
- Affirmative judgment
The recorded pay and time gains show a credible worker-benefit pathway. We give its broad recurring value enough weight to defend a positive overall forecast.
The affirmative takes a clear position and still has to answer job losses and how far its evidence reaches.
- Negative judgment
The concentrated losses and incomplete transitions make that positive forecast unconvincing. We give their severity and possible persistence greater weight.
The negative takes a clear position too, and can’t treat its own predicted harm as certain.
- What changes the comparison
Later transition outcomes, the distribution of gains and better causal comparisons could strengthen one forecast over the other.
Both sides should say what new evidence would change their view.
You can be careful and still make a clear, strong argument.
Test the reasoning
Read the practice case
Resolution Advanced AI will on net improve outcomes for workers
a two-year review of 30 employers records 300 retained workers receiving 4% higher inflation-adjusted pay and working 37 rather than 40 hours per week after AI adoption.
- Sixty entry-level roles were removed and 40 technical roles added.
- Of the 60 displaced workers, 18 entered the new roles, 24 found work elsewhere at 8% lower pay, and 18 were still unemployed six months later.
- The other 22 new roles went to different applicants.
- The review does not isolate AI from every other change or forecast the whole economy.
The affirmative argues that productive tools can improve pay and time for many workers. The negative argues that concentrated losses and barriers to new work can outweigh those gains.
Review and continue
Resolution Meritocracy as practiced in modern societies is a harmful fiction that should be rejected
Read the practice task and case
Give a negative judgment about rejection versus reform. Use the later cohort without overstating its cause, and name a finding that would weaken your case.
a hiring audit compares 100 lower-income and 100 higher-income applicants.
- Their anonymous work-sample scores were similar, but CV review shortlisted 30 lower-income and 60 higher-income applicants.
- Among those hired, work-sample scores tracked six-month job performance more closely than CV rank.
- In a later cohort, free preparation and an anonymous first stage reduced the shortlist-rate gap from 30 to 10 percentage points; other changes were not controlled.
The affirmative argues that calling the existing process meritocratic legitimizes an advantage not explained by demonstrated ability. The negative argues that useful ability assessment and the improvement after reform are reasons to repair the practice rather than reject it. Neither side may treat this single practice audit as a description of all societies.
Your response
You can return to the practice whenever you want.
One model response
We favor reform because relevant work-sample assessment carries useful performance information and the later cohort’s shortlist gap was smaller after changes to preparation and screening. That supports a credible route to improvement, not proof of which reform caused it or that all barriers disappeared. Our case weakens if persistent background advantages survive reforms that preserve useful assessment. We also need to answer whether retaining the meritocratic description itself legitimizes the remaining gap.
The negative gives the reform evidence an actual role.
The later cohort does not isolate each cause.
Useful reform is not automatically an answer to the affirmative’s criticism of the description.
The response takes a position, uses the record for a bounded inference and identifies both a condition for revision and an independent affirmative argument. It does more than list unknowns.
Review the performance
- ArgumentsI give a supported judgment under uncertainty.
- EvidenceI keep the limits of the inference clear.
- RefutationI identify evidence or a reply that could change its weight.
Choose one move to improve, revise your response, then try it aloud.
Common mistakes to avoid
- Using uncertainty as an excuse not to argue.
- Demanding certainty from them while treating your own prediction as a given.
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