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- A practical guide to SEC ï¬nancial reporting and disclosures for successful regulatory crowdfunding
- Quality shareholders versus transient investors: The alarming case of product recalls
- The Health Equity Accelerator at Boston Medical Center
- Monosha Biotech: Growth Challenges of a Social Enterprise Brand
- Assessing the Value of Unifying and De-duplicating Customer Data, Spreadsheet Supplement
- Building an AI First Snack Company: A Hands-on Generative AI Exercise, Data Supplement
- Building an AI First Snack Company: A Hands-on Generative AI Exercise
- Board Director Dilemmas: The Tradeoffs of Board Selection
- Barbie: Reviving a Cultural Icon at Mattel (Abridged)
- Happiness Capital: A Hundred-Year-Old Family Business's Quest to Create Happiness
Applied: Using Behavioral Science to Debias Hiring
內容大綱
The UK government's Behavioural Insights Team (BIT) needed to hire a new associate and were trying to increase the diversity of their job candidates. This decision was based on academic research showing that recruiters and managers often fell into common traps like "stereotype" and "affinity" bias, where they hired people who looked the part or who were similar in appearance or background as themselves. To overcome these biases, the team had spent hours using a permanent marker to redact the names and educational information from each candidate's CVs, one-by-one. This painstaking process inspired Kate Glazebrook to develop Applied-a technological solution to debias hiring. Applied was a recruitment and hiring platform that used technology to eliminate biased language in job ads and used task-based assessments to reduce favoritism, among other features. Years after founding the company, Glazebrook considered asking her clients to remove CVs altogether. Could Glazebrook convince her new and existing customers to use the platform, even after taking away CVs?