Impact-Based Moral Evaluation
Judge moral contribution by measurable benefit rather than visible virtue
- Difficulty
- Moderate
- Time to result
- ~weeks to results
- Steps
- 5
- Confidence
- 86%
Impact-Based Moral Evaluation replaces intuitive admiration with a structured comparison of what people or interventions actually accomplish. Begin by defining the relevant human benefit, then assess reach, magnitude, durability, and causal contribution. Keep this outcome assessment separate from whether the actor appears saintly, belongs to one’s community, or is personally likable. Pinker illustrates the gap by contrasting Mother Teresa’s visible care, Bill Gates’s disease-control ambitions, and Norman Borlaug’s role in agricultural improvements credited with saving enormous numbers of lives. The framework does not claim that every moral value reduces to a number. Instead, it exposes a recurring bias: society’s allocation of praise can be only loosely connected to the scale of good achieved.
Origin
Extracted from Masters in Business, where Steven Pinker compared public admiration for Mother Teresa, Bill Gates, and Norman Borlaug with the scale of their effects on human welfare.
Core principles
- 01Admiration and impact are different variables
- 02Scale and durability matter when comparing good outcomes
- 03Visible compassion can overshadow systemic benefit
- 04Personal likability is a poor proxy for lives improved
How to run it
- 1
Define the benefit
State the concrete outcome that counts as doing good, such as lives saved, disease prevented, income raised, or suffering reduced.
Pro tip Choose measures that can be applied consistently across the alternatives.
Watch out Do not begin with the reputation of the person or institution being judged.
- 2
Estimate reach and magnitude
Assess how many people benefited and how substantial the improvement was for each person.
Pro tip Use ranges when exact figures are uncertain.
Watch out Large audience numbers can conceal trivial or temporary benefits.
- 3
Assess durability and causality
Determine how long the benefits lasted and how much of the outcome can reasonably be attributed to the intervention.
Pro tip Compare against what likely would have happened without the intervention.
Watch out Do not credit an actor for every favorable event that followed their involvement.
- 4
Remove admiration bias
Evaluate whether fame, sacrifice, warmth, group loyalty, or dislike is influencing the judgment independently of outcomes.
Pro tip Score impact before revealing the actor’s identity when possible.
Watch out An unpopular or obscure actor may still have produced the greatest benefit.
- 5
Compare on common criteria
Rank alternatives using the same outcome dimensions while recording uncertainty and any moral constraints that numbers cannot capture.
Pro tip Keep impact scores and rights-based concerns visible as separate columns.
Watch out Do not turn uncertain estimates into false precision.
In the wild
Norman Borlaug remained far less famous than many celebrated humanitarian figures, yet Pinker notes that his work on crops and farming methods was credited with saving a billion lives. The comparison demonstrates how obscurity and technical work can hide enormous moral impact.
→ Outcome scale reverses the ranking suggested by fame or conventional moral imagery.
A donor compares a highly visible local campaign with a less glamorous vaccination program. The donor evaluates beneficiaries reached, health improvement, duration, causal evidence, and cost before considering brand reputation.
→ Funds are directed toward the program with the stronger evidence of substantial and durable benefit.
Common mistakes
Rewarding visible sacrifice
Observers may assign moral credit to emotionally vivid service without comparing its reach or durability with less visible alternatives.
Using likability as evidence
Personal affection or hostility toward an actor says little about how much measurable good that person produced.
Ignoring uncertainty
Impact estimates should guide comparison without pretending that contested attribution or incomplete data is exact.
Is it for you?
Best for
It is best for comparing philanthropic programs, social ventures, public policies, and leaders claiming positive impact.
Not ideal for
It is not ideal when outcomes cannot be estimated responsibly or when rights and duties must override aggregate benefits.
From the transcript
“He's credited with saving a billion lives, more than anyone in history, and no one's heard of him”
“our our description of morality, who we give brownie points to is very loosely related to how much good they do in the world.”
“it isn't really closely related to how much good they do in the world.”
From the episode
Interview With Steven Pinker: Masters in Business (Audio)
Steven Pinker