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+# Proposal for metrics/dashboard API
+
+Goals:
+* Unified treatment of many different kinds of metrics in dashboard
+* A clean separation between dashboard (for visualization) and different kinds of backends (for metric calculation)
+
+Status quo:
+* Dashboard currently keeps track of the metric values in the cache dictionary `fairlearn.widget._fairlearn_widget.FairlearnWidget._response`
+* The dictionary is updated in `fairlearn.widget._fairlearn_dashboard.FairlearnDashboard._on_request`
+* There's a PR out to fill the whole data structure in  `fairlearn.metrics.create_dashboard_dictionary`
+
+Issues with the status quo:
+* Code duplication / redundancy / brittleness due to copy-paste errors
+* Currently only one kind of a metric (`<metric>_summary`) is supported and hard-wired into the dashboard dictionary
+
+## Proposal
+
+### Part I: More general dashboard dictionary
+
+```python
+{ 
+  "prediction_type":  "binary_classification" or "probabilistic_binary_classification" or "regression",
+  "array_bindings": {   # all 1D arrays, including features and predictior vectors, are here
+    "<array_key>" : {   # the keys can be arbitrary strings; not sure we need to force any convention, but see examples below
+      "name": string,   # the name of a feature would be the feature name, of a prediction vector would be the model name
+      "values": number[],  
+      "value_names": string[],       # an optional field to encode categorical data   
+    },
+    "sensitive_feature gender" : {   # an example feature
+      "name": "gender",
+      "values": [0, 1, 0, 0, 2],
+      "value_names": ["female", "male", "non-binary"],
+    },
+    "y_pred model0" : {      # an example prediction vector             
+      "name": "model0",
+      "values": [0, 0, 1, 1, 0],
+    },
+    "y_true": {
+      "name": "y_true",
+      "values": [0, 1, 1, 1, 0],
+    },
+    "sample_weight": {
+      "name": "sample_weight",
+      "values": [0.1, 0.3, 1, 0.9],
+    }
+    ...
+  },
+  "cache" : [
+    {
+      "function": string,   # python function name; we could either limit to fairlearn.metrics
+                            # or use fully qualified names
+      "arguments": {
+        "<array_argument>": "<array_key>" or null,   # array-valued arguments are matched with array bindings
+        "<numeric_argument>": number or null,        # we should also support numeric arguments, strings, booleans
+        "<string_argument>": string or null,         # null corresponds to None
+        "<boolean_argument>": boolean or null,
+      },
+      "return_value": number or string or boolean or null or dict,
+                                                     # dict could be encoded as { "keys": any[], "values": any[] }
+    },
+    { # an example
+      "function": "fbeta_score_group_summary",
+      "arguments": {
+        "y_true": "y_true",
+        "y_pred": "y_pred model0",
+        "sensitive_features": "sensitive_feature gender",
+        "sample_weight": "sample_weight",
+        "beta": 0.3,
+      },
+      "return_value": {
+        "overall": 0.11,      
+        "by_group": {
+          "keys": [0, 1, 2],
+          "values": [0.15, 0.04, 0.03],
+        }
+      },
+    },
+    ...
+  ]
+}
+```
+
+### Part II: How to remove duplication
+
+`<TODO>`