Make work fair: data-driven design for real results

Introduction: design for fairness

The myths we need to debunk

Part I. Make it count: motivating action

Data as an engine for change

Goals that promote desirable behavior

Incentives to drive results

Transparency for accountability

Part II. Make it stick: finding and developing talent

Talent attraction to cast a wide net

Applicant screening for accuracy

Hiring the best person for the job

Career advancement that works for all

Part III. Make it normal: embedding fairness

Work arrangements to level the playing field

Norms that create equal opportunities

Culture that fosters thriving

Conclusion: change for good.