[{
"type": "thumb-down",
"id": "missingTheInformationINeed",
"label":"Missing the information I need"
},{
"type": "thumb-down",
"id": "tooComplicatedTooManySteps",
"label":"Too complicated / too many steps"
},{
"type": "thumb-down",
"id": "outOfDate",
"label":"Out of date"
},{
"type": "thumb-down",
"id": "samplesCodeIssue",
"label":"Samples/Code issue"
},{
"type": "thumb-down",
"id": "otherDown",
"label":"Other"
}]
[{
"type": "thumb-up",
"id": "easyToUnderstand",
"label":"Easy to understand"
},{
"type": "thumb-up",
"id": "solvedMyProblem",
"label":"Solved my problem"
},{
"type": "thumb-up",
"id": "otherUp",
"label":"Other"
}]
ML Practicum: Fairness in Perspective API
Exercise #2: Remediate Bias
In this programming exercise, you'll use
Fairness Indicators to remediate the
bias you uncovered in Exercise #1
by upweighting negative subgroup examples to help balance the training data.