Ethical Decision Making and Integrity Questions

Probe the candidate's approach to ethical dilemmas, integrity, and principled decision making. Candidates should provide examples where they prioritized honesty, transparency, user safety, or other ethical principles, including situations where customer needs conflicted with company interests, or where following the easy path would have compromised values. Assess how they identify ethical risks, escalate concerns, balance competing stakeholder interests ethically, and incorporate fairness, compliance, and long term reputational considerations into technical or product decisions. Look for reflection on trade offs and how they communicated principled positions under pressure.

MediumTechnical

0 practiced

Describe a time you escalated an ethical concern to leadership. Walk through how you documented the issue, how you chose who to involve, what evidence you presented, and how you managed any pushback from product or business teams.

EasyTechnical

0 practiced

Imagine you discover a colleague has intentionally removed or relabeled negative examples from a training dataset to improve model metrics before a demo. As a data scientist, what step-by-step actions would you take immediately and in the following weeks? Include how you would preserve evidence, document the incident, escalate if necessary, and propose process changes to prevent recurrence.

MediumTechnical

0 practiced

Give an example where you proactively changed a project's direction because of ethical concerns. Explain how you influenced stakeholders, the trade-offs you accepted, and how you measured the eventual outcome or impact of the change.

HardTechnical

0 practiced

Explain how to design and run counterfactual fairness tests for a recidivism model. Describe how to construct counterfactuals, key identification assumptions, common pitfalls (such as unrealistic counterfactuals or feature entanglement), and how you would automate these tests in CI/CD to reject builds that introduce fairness regressions.

EasyTechnical

0 practiced

List the most common sources of bias in training datasets (for example: sampling bias, label bias, survivorship bias, measurement error). For each source give one concrete method you would use to detect it during exploratory data analysis and one simple mitigation you might try.

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