Strategy and analysis

Ethical Trade-Off Assessment

Compare difficult decisions involving competing values, risks, and social impacts. Useful for AI governance, sustainability, public policy, healthcare, and organisational ethics. The criteria can be changed, added to and weighted.

What the framework is

An ethical trade-off assessment compares options where values pull in different directions, so the choice and its costs are explicit. The template compares options on five criteria: Fairness, Transparency, Social Benefit, Privacy Impact and Long-Term Risk. The criteria can be changed, added to and weighted.

What the result looks like

EXAMPLE
Which approach to customer data should we adopt?
Top ranked
Opt-in only
Fairness 92% · 1st
Transparency 63% · 1st
Social Benefit 38% · 3rd
Privacy Impact 75% · 1st
Long-Term Risk 50% · 2nd
63%
Weighted win rate

Overall

Options Weighted win rate
01
Opt-in only
Fairness 1st · Transparency 1st · Social Benefit 3rd · Privacy Impact 1st · Long-Term Risk 2nd
63%
02
Anonymised analytics
Fairness 2nd · Transparency 2nd · Social Benefit 1st · Privacy Impact 3rd · Long-Term Risk 1st
52%
03
Personalised by default
Fairness 3rd · Transparency 3rd · Social Benefit 2nd · Privacy Impact 2nd · Long-Term Risk 3rd
35%

Overall with the criteria

Rank
Options
Fairness
20%
Transparency
20%
Social Benefit
20%
Privacy Impact
20%
Long-Term Risk
20%
Overall
Weighted win rate
01 Opt-in only
92%
1st
63%
1st
38%
3rd
75%
1st
50%
2nd
63%
02 Anonymised analytics
42%
2nd
58%
2nd
71%
1st
33%
3rd
54%
1st
52%
03 Personalised by default
17%
3rd
29%
3rd
42%
2nd
42%
2nd
46%
3rd
35%

By the options

01 · Opt-in only 63% weighted win rate
–
Fairness
92%
1st of 3 · 22 Wins · 2 Losses
–
Transparency
63%
1st of 3 · 15 Wins · 9 Losses
–
Social Benefit
38%
3rd of 3 · 9 Wins · 15 Losses
–
Privacy Impact
75%
1st of 3 · 18 Wins · 6 Losses
–
Long-Term Risk
50%
2nd of 3 · 12 Wins · 12 Losses
02 · Anonymised analytics 52% weighted win rate
–
Fairness
42%
2nd of 3 · 10 Wins · 14 Losses
–
Transparency
58%
2nd of 3 · 14 Wins · 10 Losses
–
Social Benefit
71%
1st of 3 · 17 Wins · 7 Losses
–
Privacy Impact
33%
3rd of 3 · 8 Wins · 16 Losses
–
Long-Term Risk
54%
1st of 3 · 13 Wins · 11 Losses
03 · Personalised by default 35% weighted win rate
–
Fairness
17%
3rd of 3 · 4 Wins · 20 Losses
–
Transparency
29%
3rd of 3 · 7 Wins · 17 Losses
–
Social Benefit
42%
2nd of 3 · 10 Wins · 14 Losses
–
Privacy Impact
42%
2nd of 3 · 10 Wins · 14 Losses
–
Long-Term Risk
46%
3rd of 3 · 11 Wins · 13 Losses
Scoring Explanations
CRITERIA WEIGHTINGS — The % weighting for each criterion is shown in the table header. Unless this was entered in the setup, the weighting is equal across criteria.
WEIGHTED WIN RATE — Each criterion's result, in the measure they share, averaged using the assigned weightings.

When it earns its place

Use it for decisions about data use, AI deployment, service changes that affect people unevenly, or policy options with winners and losers. It suits ethics committees, governance boards and project teams who need to show their reasoning.

The trap is pretending one option has no costs. Every serious option loses on something. The ranking per criterion shows where each option pays, so the group can decide whether that price is acceptable.

How to run it with a group

  1. Describe each option concretely, including who is affected.
  2. Agree the weights, and record who set them: the weights are the value judgement.
  3. Participants compare the options two at a time on each criterion. Participants do not need an account to respond. Include people affected by the decision where you can.
  4. Read the overall ranking, then the ranking per criterion. Write down what the chosen option gives up, and how you will limit that harm.

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