Ethical Thinking#

Ethics is about conflicts between legitimate goods. End with transparent tradeoffs, not false certainty.

When to use#

• A product, policy or data decision that could affect people: privacy, fairness, consent, manipulation, safety.
• "Should we?" questions that go beyond "can we" or "is it legal".
• AI and data ethics reviews, stakeholder harm scans, moral review of a plan.

Skip: legal advice as such, neutral fact-finding with no values review requested, implementation-only work.

Before you start#

1. State in one block: Focal action (what is being considered) and Pass (Stakeholders, Values, Harms and Benefits, Justice and Power, Options, Recommendation).
2. Gather first. Read the design doc, data fields, policy text or code to learn who and what is actually affected before assuming. Ask up to 3 questions only for affected parties or red lines that tools cannot answer.
3. Light path. Small ask: 2-3 lines per lens, 2 options. For a pure harm scan you may compress Values, but still cover Justice and Power before Options.

Lenses#

1. Stakeholders#

Who is affected: direct, indirect, future, non-human where ecology matters. For vulnerable groups, describe dependence, cognitive load or marginalization in plain words and one sentence on why that raises caution. Justify from the context; never stereotype.

2. Values#

Which values are in play (autonomy, beneficence, non-maleficence, justice, dignity, solidarity, others)? Name at least one value tension: A vs B, and why both matter here.

3. Harms and Benefits#

Concrete harms and benefits. For each: how plausible, under what conditions, and how reversible. Separate predicted from observed when the user supplies history.

4. Justice and Power#

Who carries the burdens and who gets the benefits? Who can say no, and who bears the cost of errors? Check procedural fairness: voice, consent, appeal.

5. Options#

Two or more ethically distinct paths, including "do not proceed" when plausible:

Option: ... - Value fit: ... - Residual harm: ... - Safeguards: ...

6. Recommendation#

A preferred option if the analysis supports one, or conditional guidance. Include the strongest reason against your recommendation and what to monitor if the plan proceeds.

Rules#

1. Do not demonize actors; focus on structures, incentives and foreseeable effects.
2. If values truly clash, say so and recommend a process (deliberation, oversight) instead of fake unanimity.
3. Never invent sensitive personal facts about real people; use only what the user gave.
4. Not legal advice. Where law may bind, write "legal review needed" and do not predict legal outcomes.

Pitfalls#

• A stakeholder list that stops at "users". Include the people who bear the cost without using the product.
• Harm stated with no plausibility or conditions, which reads as either alarmist or empty.
• A Recommendation with no dissent. If you cannot name the strongest objection, the analysis is unfinished.
• Safeguards that are only intentions ("we will be careful"). Name an owner or a mechanism.

Worked example: references/example.md.

Checklist#

• Focal action and Pass stated
• Stakeholders include indirect or future parties when relevant
• At least one explicit value tension
• Harms and benefits give plausibility; options list safeguards
• Justice and Power covers distribution, voice and consent
• Recommendation names residual harm and the strongest dissent