Diversity Inclusion and Belonging Questions

Covers design, implementation, and stewardship of diversity, inclusion, equity, and belonging programs that create fair access and a sense of belonging for all employees. Candidates should be prepared to describe concrete actions such as building inclusive hiring processes, removing bias from selection and promotion, creating equitable advancement opportunities, launching and supporting employee resource groups, designing belonging initiatives and accommodation policies, and delivering training and coaching for managers. The description includes measuring impact through diversity metrics, inclusion surveys, retention and promotion rates, and other outcome indicators, as well as iterating programs based on data. At senior levels, articulate understanding of systemic barriers, cross functional partnership with People Operations and leadership, change management strategies to scale initiatives, handling resistance, and long term approaches to embed equity into processes and culture.

HardTechnical

0 practiced

Describe how to quantify intersectional disparities (for example by race and gender) in model outcomes. Propose statistical tests, visualization strategies, and modeling approaches (hierarchical models, interaction terms) that handle small subgroup sizes and control for confounders.

EasyTechnical

0 practiced

What is an Employee Resource Group (ERG)? As an ML engineer, describe two ways you can support ERGs (one technical, one non-technical) and suggest one measurable outcome you'd track to evaluate ERG impact on retention or sense of belonging.

HardSystem Design

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Design a company-wide Model Fairness Governance Framework for ML systems across products. Specify roles and responsibilities (engineers, data owners, model owners, legal, People Ops), review cadence, required documentation (datasheets, fairness risk assessments), tooling, audit processes, and enforcement mechanisms.

MediumTechnical

0 practiced

Scenario: A stakeholder resists DEI initiatives arguing the company should only hire 'the best' and fears lowering standards. As an ML engineer advocating for inclusive hiring and fair models, how would you have a constructive conversation to address concerns, provide data, and propose specific, measurable, data-driven practices?

MediumTechnical

0 practiced

Write Python code (using pandas) to compute precision, recall, and support per protected subgroup given a DataFrame with columns: y_true, y_pred, protected_group. Optimize for clarity, handle missing group values, and explain how you would stabilize estimates for small groups (e.g., smoothing or thresholds).

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