add fairness question

episodes/fairness.md

@@ -12,8 +12,32 @@ exercises: 0
 
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-- Participants will be able to define and differntiate between various notions of fairness in the context of machine learning. 
-- Participants will be able to define (and implement?) two different ways of modifying the machine learning modeling process to improve the fairness of a model.
-- Participants will understand the limitations of fairness as a metric for machine learning models.
+- Explain what is meant by bias and fairness in the context of machine learning.
+- Describe two different ways of modifying the machine learning modeling process to improve the fairness of a model.
+- Articulate the limitations of fairness scores
 
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+
+:::::::::::::::::::::::::::::::::::::: challenge
+
+### Matching fairness terminology with definitions
+
+Match the following types of formal fairness with their definitions.
+(A) Individual fairness,
+(B) Equalized odds,
+(C) Demographic parity, and 
+(D) Group-level calibration
+
+1. The model is equally accurate across all demographic groups. 
+2. Different demographic groups have the same true positive rates and false positive rates. 
+3. Similar people are treated similarly.
+4. People from different demographic groups receive each outcome at the same rate.
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::: solution
+
+### Solution
+
+A - 3, B - 2, C - 4, D - 1
+
+:::::::::::::::::::::::::

episodes/problem-definition.md

@@ -12,6 +12,9 @@ exercises: 0
 
 ::::::::::::::::::::::::::::::::::::: objectives
 
-- TODO
+- Judge what tasks are appropriate for machine learning
+- Understand why the choice of prediction task / target variable is important.
+- Describe how bias can appear in training data.
+
 
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