Specification for how the data should be sliced for bias. It contains a
list of slices, with limitation of two slices. The first slice of data
will be the slice_a. The second slice in the list (slice_b) will be
compared against the first slice. If only a single slice is provided,
then slice_a will be compared against "not slice_a".
Below are examples with feature "education" with value "low", "medium",
"high" in the dataset:
Example 1:
bias_slices = [{'education': 'low'}]
A single slice provided. In this case, slice_a is the collection of data
with 'education' equals 'low', and slice_b is the collection of data with
'education' equals 'medium' or 'high'.
Two slices provided. In this case, slice_a is the collection of data
with 'education' equals 'low', and slice_b is the collection of data with
'education' equals 'high'.
Specification for how the data should be sliced for bias. It contains a
list of slices, with limitation of two slices. The first slice of data
will be the slice_a. The second slice in the list (slice_b) will be
compared against the first slice. If only a single slice is provided,
then slice_a will be compared against "not slice_a".
Below are examples with feature "education" with value "low", "medium",
"high" in the dataset:
Example 1:
bias_slices = [{'education': 'low'}]
A single slice provided. In this case, slice_a is the collection of data
with 'education' equals 'low', and slice_b is the collection of data with
'education' equals 'medium' or 'high'.
Two slices provided. In this case, slice_a is the collection of data
with 'education' equals 'low', and slice_b is the collection of data with
'education' equals 'high'.
Specification for how the data should be sliced for bias. It contains a
list of slices, with limitation of two slices. The first slice of data
will be the slice_a. The second slice in the list (slice_b) will be
compared against the first slice. If only a single slice is provided,
then slice_a will be compared against "not slice_a".
Below are examples with feature "education" with value "low", "medium",
"high" in the dataset:
Example 1:
bias_slices = [{'education': 'low'}]
A single slice provided. In this case, slice_a is the collection of data
with 'education' equals 'low', and slice_b is the collection of data with
'education' equals 'medium' or 'high'.
Two slices provided. In this case, slice_a is the collection of data
with 'education' equals 'low', and slice_b is the collection of data with
'education' equals 'high'.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-01-27 UTC."],[],[]]