{"blocks": [{"key": "57c8a528", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "70eadd47", "text": "On-site behavioral conversation on fairness in lending", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "e140eb95", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "caaea99a", "text": "Regulators require ‘fair lending’. The portfolio currently issues 50 % of loans to women and 50 % to men. Does this guarantee fairness? Explain the additional analyses you would perform. Why are you interested in machine learning? Describe a past failure and what you would do differently.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "697a8116", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "df56e5da", "text": "Discuss qualified-applicant mix, acceptance rates, pricing parity, disparate impact tests.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}