Fairness
Metrics
AutoMLx outlines a set of bias/fairness metrics, based on developments in the ML fairness community [1], to assess and measure if a model/dataset complies with a specific metric. The provided metrics all correspond to different notions of fairness, from which the user should carefully select while taking into account their application’s context.
The metrics each implement different criteria defining how a model or dataset should be unbiased toward a protected attribute. If an attribute is protected, then each of its unique values (for example, “male”, “female” or “other”) are considered subgroups that should be protected in some way so as to have equal outcomes from the model. These types of fairness metrics are known as group fairness metrics.
[1] Moritz Hardt et al. “Fairness and Machine Learning: Limitations and Opportunities”. 2019.
For maximal versatility, all supported metrics are offered under two formats:
#. A scikitlearnlike
Scorer
object which can be initialized and reused to test
different models or datasets.
#. A functional interface which can easily be used for oneline computations.
Evaluating a Model
Statistical Parity
 class automl.fairness.metrics.model. ModelStatisticalParityScorer ( protected_attributes , distance_measure = 'diff' , reduction = 'mean' )

Measure the statistical parity [1] of a model’s output between subgroups and the rest of the population.
Statistical parity (also known as Base Rate or Disparate Impact) states that a predictor is unbiased if the prediction is independent of the protected attribute.
Statistical Parity is calculated as PP / N, where PP and N are the number of Positive Predictions and total Number of predictions made, respectively.
 Perfect score

A perfect score for this metric means that the model does not predict positively any of the subgroups at a different rate than it does for the rest of the population. For example, if the protected attributes are race and sex, then a perfect statistical parity would mean that all combinations of values for race and sex have identical ratios of positive predictions. Perfect values are:

1 if using
'ratio'
asdistance_measure
. 
0 if using
'diff'
asdistance_measure
.

 Parameters


protected_attributes ( pandas.Series , numpy.ndarray , list , str ) – Array of attributes or single attribute that should be treated as protected. If an attribute is protected, then all of its unique values are considered as subgroups.

distance_measure (str, optional ) –
Determines the distance used to compare a subgroup’s metric against the rest of the population. Possible values are:

'ratio'
: Uses(subgroup_val / rest_of_pop_val)
.
Inverted to always be >= 1 if needed. *
'diff'
: Uses subgroup_val  rest_of_pop_val 
.Default is
'diff'
. 

reduction (str, optional ) –
Determines how to reduce scores on all subgroups to a single output. Possible values are:

'max'
: Returns the maximal value among all subgroup metrics. 
'mean'
: Returns the mean over all subgroup metrics. 
None
: Returns a{subgroup: subgroup_metric, ...}
dict.
Default is
'mean'
. 

References
[1] Cynthia Dwork et al. “Fairness Through Awareness”. Innovations in Theoretical Computer Science. 2012.
Examples
from automl.fairness.metrics import ModelStatisticalParityScorer scorer = ModelStatisticalParityScorer(['race', 'sex']) scorer(model, X, y_true)
This metric does not require y_true . It can also be called using
scorer(model, X)
 __call__ ( model , X , y_true = None , supplementary_features = None )

Compute the metric using a model’s predictions on a given array of instances
X
. Parameters


model ( object ) – Object that implements a predict(X) function to collect categorical predictions.

X ( pandas.DataFrame ) – Array of instances to compute the metric on.

y_true (pandas.Series, numpy.ndarray, list, optional ) – Array of groundtruth labels. Default is
None
. 
supplementary_features (pandas.DataFrame, optional ) – Array of supplementary features for each instance. Used in case one attribute in
self.protected_attributes
is not contained byX
(e.g. if the protected attribute is not used by the model). Default isNone
, equivalent to empty DataFrame

 Returns

The computed metric value, with format according to
self.reduction
.  Return type
 Raises

AutoMLxValueError –

if a feature is present in both
X
andsupplementary_features
.

 automl.fairness.metrics.model. model_statistical_parity ( y_true = None , y_pred = None , subgroups = None , distance_measure = 'diff' , reduction = 'mean' )

Measure the statistical parity of a model’s output between subgroups and the rest of the population.
For more details, refer to
ModelStatisticalParityScorer
. Parameters


y_true ( pandas.Series , numpy.ndarray , list ) – Array of groundtruth labels.

y_pred ( pandas.Series , numpy.ndarray , list ) – Array of model predictions.

subgroups ( pandas.DataFrame ) – Dataframe containing protected attributes for each instance.

distance_measure (str, optional ) –
Determines the distance used to compare a subgroup’s metric against the rest of the population. Possible values are:

'ratio'
: Uses(subgroup_val / rest_of_pop_val)
.
Inverted to always be >= 1 if needed. *
'diff'
: Uses subgroup_val  rest_of_pop_val 
.Default is
'diff'
. 

reduction (str, optional ) –
Determines how to reduce scores on all subgroups to a single output. Possible values are:

'max'
: Returns the maximal value among all subgroup metrics. 
'mean'
: Returns the mean over all subgroup metrics. 
None
: Returns a{subgroup: subgroup_metric, ...}
dict.
Default is
'mean'
. 

 Returns

The computed metric value, with format according to reduction .
 Return type
 Raises

AutoMLxValueError – If Value of None is received for either y_pred or subgroups .
Examples
from automl.fairness.metrics import model_statistical_parity subgroups = X[['race', 'sex']] model_statistical_parity(y_true, y_pred, subgroups)
This metric does not require y_true . It can also be called using
model_statistical_parity(None, y_pred, subgroups) model_statistical_parity(y_pred=y_pred, subgroups=subgroups)
True Positive Rate Disparity
 class automl.fairness.metrics.model. TruePositiveRateScorer ( protected_attributes , distance_measure = 'diff' , reduction = 'mean' )

Measures the disparity of a model’s true positive rate between subgroups and the rest of the population (also known as equal opportunity).
For each subgroup, the disparity is measured by comparing the true positive rate on instances of a subgroup against the rest of the population.
True Positive Rate [1] (also known as TPR, recall, or sensitivity) is calculated as TP / (TP + FN), where TP and FN are the number of true positives and false negatives, respectively.
 Perfect score

A perfect score for this metric means that the model does not correctly predict the positive class for any of the subgroups more often than it does for the rest of the population. For example, if the protected attributes are race and sex, then a perfect true positive rate disparity would mean that all combinations of values for race and sex have identical true positive rates. Perfect values are:

1 if using
'ratio'
asdistance_measure
. 
0 if using
'diff'
asdistance_measure
.

 Parameters


protected_attributes ( pandas.Series , numpy.ndarray , list , str ) – Array of attributes or single attribute that should be treated as protected. If an attribute is protected, then all of its unique values are considered as subgroups.

distance_measure (str, optional ) –
Determines the distance used to compare a subgroup’s metric against the rest of the population. Possible values are:

'ratio'
: Uses(subgroup_val / rest_of_pop_val)
.
Inverted to always be >= 1 if needed. *
'diff'
: Uses subgroup_val  rest_of_pop_val 
.Default is
'diff'
. 

reduction (str, optional ) –
Determines how to reduce scores on all subgroups to a single output. Possible values are:

'max'
: Returns the maximal value among all subgroup metrics. 
'mean'
: Returns the mean over all subgroup metrics. 
None
: Returns a{subgroup: subgroup_metric, ...}
dict.
Default is
'mean'
. 

References
[1] Moritz Hardt et al. “Equality of Opportunity in Supervised Learning”. Advances in Neural Information Processing Systems. 2016.
Examples
from automl.fairness.metrics import TruePositiveRateScorer scorer = TruePositiveRateScorer(['race', 'sex']) scorer(model, X, y_true)
 __call__ ( model , X , y_true , supplementary_features = None )

Compute the metric using a model’s predictions on a given array of instances
X
. Parameters


model ( object ) – Object that implements a predict(X) function to collect categorical predictions.

X ( pandas.DataFrame ) – Array of instances to compute the metric on.

y_true ( pandas.Series , numpy.ndarray , list ) – Array of groundtruth labels.

supplementary_features (pandas.DataFrame, optional ) – Array of supplementary features for each instance. Used in case one attribute in
self.protected_attributes
is not contained byX
(e.g. if the protected attribute is not used by the model). Default isNone
, equivalent to empty DataFrame

 Returns

The computed metric value, with format according to
self.reduction
.  Return type
 Raises

AutoMLxValueError –

if a feature is present in both
X
andsupplementary_features
.

 automl.fairness.metrics.model. true_positive_rate ( y_true , y_pred , subgroups , distance_measure = 'diff' , reduction = 'mean' )

Measures the disparity of a model’s true positive rate between subgroups and the rest of the population.
For more details, refer to
TruePositiveRateScorer
. Parameters


y_true ( pandas.Series , numpy.ndarray , list ) – Array of groundtruth labels.

y_pred ( pandas.Series , numpy.ndarray , list ) – Array of model predictions.

subgroups ( pandas.DataFrame ) – Dataframe containing protected attributes for each instance.

distance_measure (str, optional ) –
Determines the distance used to compare a subgroup’s metric against the rest of the population. Possible values are:

'ratio'
: Uses(subgroup_val / rest_of_pop_val)
.
Inverted to always be >= 1 if needed. *
'diff'
: Uses subgroup_val  rest_of_pop_val 
.Default is
'diff'
. 

reduction (str, optional ) –
Determines how to reduce scores on all subgroups to a single output. Possible values are:

'max'
: Returns the maximal value among all subgroup metrics. 
'mean'
: Returns the mean over all subgroup metrics. 
None
: Returns a{subgroup: subgroup_metric, ...}
dict.
Default is
'mean'
. 

 Returns

The computed metric value, with format according to reduction .
 Return type
Examples
from automl.fairness.metrics import true_positive_rate subgroups = X[['race', 'sex']] true_positive_rate(y_true, y_pred, subgroups)
False Positive Rate Disparity
 class automl.fairness.metrics.model. FalsePositiveRateScorer ( protected_attributes , distance_measure = 'diff' , reduction = 'mean' )

Measures the disparity of a model’s false positive rate between subgroups and the rest of the population.
For each subgroup, the disparity is measured by comparing the false positive rate on instances of a subgroup against the rest of the population.
False Positive Rate [1] (also known as FPR or fallout) is calculated as FP / (FP + TN), where FP and TN are the number of false positives and true negatives, respectively.
 Perfect score

A perfect score for this metric means that the model does not incorrectly predict the positive class for any of the subgroups more often than it does for the rest of the population. For example, if the protected attributes are race and sex, then a perfect false positive rate disparity would mean that all combinations of values for race and sex have identical false positive rates. Perfect values are:

1 if using
'ratio'
asdistance_measure
. 
0 if using
'diff'
asdistance_measure
.

 Parameters


protected_attributes ( pandas.Series , numpy.ndarray , list , str ) – Array of attributes or single attribute that should be treated as protected. If an attribute is protected, then all of its unique values are considered as subgroups.

distance_measure (str, optional ) –
Determines the distance used to compare a subgroup’s metric against the rest of the population. Possible values are:

'ratio'
: Uses(subgroup_val / rest_of_pop_val)
.
Inverted to always be >= 1 if needed. *
'diff'
: Uses subgroup_val  rest_of_pop_val 
.Default is
'diff'
. 

reduction (str, optional ) –
Determines how to reduce scores on all subgroups to a single output. Possible values are:

'max'
: Returns the maximal value among all subgroup metrics. 
'mean'
: Returns the mean over all subgroup metrics. 
None
: Returns a{subgroup: subgroup_metric, ...}
dict.
Default is
'mean'
. 

References
[1] Alexandra Chouldechova. “Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments”. Big Data (2016).
Examples
from automl.fairness.metrics import FalsePositiveRateScorer scorer = FalsePositiveRateScorer(['race', 'sex']) scorer(model, X, y_true)
 __call__ ( model , X , y_true , supplementary_features = None )

Compute the metric using a model’s predictions on a given array of instances
X
. Parameters


model ( object ) – Object that implements a predict(X) function to collect categorical predictions.

X ( pandas.DataFrame ) – Array of instances to compute the metric on.

y_true ( pandas.Series , numpy.ndarray , list ) – Array of groundtruth labels.

supplementary_features (pandas.DataFrame, optional ) – Array of supplementary features for each instance. Used in case one attribute in
self.protected_attributes
is not contained byX
(e.g. if the protected attribute is not used by the model). Default isNone
, equivalent to empty DataFrame

 Returns

The computed metric value, with format according to
self.reduction
.  Return type
 Raises

AutoMLxValueError –

if a feature is present in both
X
andsupplementary_features
.

 automl.fairness.metrics.model. false_positive_rate ( y_true , y_pred , subgroups , distance_measure = 'diff' , reduction = 'mean' )

Measures the disparity of a model’s false positive rate between subgroups and the rest of the population.
For more details, refer to
FalsePositiveRateScorer
. Parameters


y_true ( pandas.Series , numpy.ndarray , list ) – Array of groundtruth labels.

y_pred ( pandas.Series , numpy.ndarray , list ) – Array of model predictions.

subgroups ( pandas.DataFrame ) – Dataframe containing protected attributes for each instance.

distance_measure (str, optional ) –
Determines the distance used to compare a subgroup’s metric against the rest of the population. Possible values are:

'ratio'
: Uses(subgroup_val / rest_of_pop_val)
.
Inverted to always be >= 1 if needed. *
'diff'
: Uses subgroup_val  rest_of_pop_val 
.Default is
'diff'
. 

reduction (str, optional ) –
Determines how to reduce scores on all subgroups to a single output. Possible values are:

'max'
: Returns the maximal value among all subgroup metrics. 
'mean'
: Returns the mean over all subgroup metrics. 
None
: Returns a{subgroup: subgroup_metric, ...}
dict.
Default is
'mean'
. 

 Returns

The computed metric value, with format according to reduction .
 Return type
Examples
from automl.fairness.metrics import false_positive_rate subgroups = X[['race', 'sex']] false_positive_rate(y_true, y_pred, subgroups)
False Negative Rate Disparity
 class automl.fairness.metrics.model. FalseNegativeRateScorer ( protected_attributes , distance_measure = 'diff' , reduction = 'mean' )

Measures the disparity of a model’s false negative rate between subgroups and the rest of the population.
For each subgroup, the disparity is measured by comparing the false negative rate on instances of a subgroup against the rest of the population.
False Negative Rate [1] (also known as FNR or miss rate) is calculated as FN / (FN + TP), where FN and TP are the number of false negatives and true positives, respectively.
 Perfect score

A perfect score for this metric means that the model does not incorrectly predict the negative class for any of the subgroups more often than it does for the rest of the population. For example, if the protected attributes are race and sex, then a perfect false negative rate disparity would mean that all combinations of values for race and sex have identical false negative rates. Perfect values are:

1 if using
'ratio'
asdistance_measure
. 
0 if using
'diff'
asdistance_measure
.

 Parameters


protected_attributes ( pandas.Series , numpy.ndarray , list , str ) – Array of attributes or single attribute that should be treated as protected. If an attribute is protected, then all of its unique values are considered as subgroups.

distance_measure (str, optional ) –
Determines the distance used to compare a subgroup’s metric against the rest of the population. Possible values are:

'ratio'
: Uses(subgroup_val / rest_of_pop_val)
.
Inverted to always be >= 1 if needed. *
'diff'
: Uses subgroup_val  rest_of_pop_val 
.Default is
'diff'
. 

reduction (str, optional ) –
Determines how to reduce scores on all subgroups to a single output. Possible values are:

'max'
: Returns the maximal value among all subgroup metrics. 
'mean'
: Returns the mean over all subgroup metrics. 
None
: Returns a{subgroup: subgroup_metric, ...}
dict.
Default is
'mean'
. 

References
[1] Alexandra Chouldechova. “Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments”. Big Data (2016).
Examples
from automl.fairness.metrics import FalseNegativeRateScorer scorer = FalseNegativeRateScorer(['race', 'sex']) scorer(model, X, y_true)
 __call__ ( model , X , y_true , supplementary_features = None )

Compute the metric using a model’s predictions on a given array of instances
X
. Parameters


model ( object ) – Object that implements a predict(X) function to collect categorical predictions.

X ( pandas.DataFrame ) – Array of instances to compute the metric on.

y_true ( pandas.Series , numpy.ndarray , list ) – Array of groundtruth labels.

supplementary_features (pandas.DataFrame, optional ) – Array of supplementary features for each instance. Used in case one attribute in
self.protected_attributes
is not contained byX
(e.g. if the protected attribute is not used by the model). Default isNone
, equivalent to empty DataFrame

 Returns

The computed metric value, with format according to
self.reduction
.  Return type
 Raises

AutoMLxValueError –

if a feature is present in both
X
andsupplementary_features
.

 automl.fairness.metrics.model. false_negative_rate ( y_true , y_pred , subgroups , distance_measure = 'diff' , reduction = 'mean' )

Measures the disparity of a model’s false negative rate between subgroups and the rest of the population.
For more details, refer to
FalseNegativeRateScorer
. Parameters


y_true ( pandas.Series , numpy.ndarray , list ) – Array of groundtruth labels.

y_pred ( pandas.Series , numpy.ndarray , list ) – Array of model predictions.

subgroups ( pandas.DataFrame ) – Dataframe containing protected attributes for each instance.

distance_measure (str, optional ) –
Determines the distance used to compare a subgroup’s metric against the rest of the population. Possible values are:

'ratio'
: Uses(subgroup_val / rest_of_pop_val)
.
Inverted to always be >= 1 if needed. *
'diff'
: Uses subgroup_val  rest_of_pop_val 
.Default is
'diff'
. 

reduction (str, optional ) –
Determines how to reduce scores on all subgroups to a single output. Possible values are:

'max'
: Returns the maximal value among all subgroup metrics. 
'mean'
: Returns the mean over all subgroup metrics. 
None
: Returns a{subgroup: subgroup_metric, ...}
dict.
Default is
'mean'
. 

 Returns

The computed metric value, with format according to reduction .
 Return type
Examples
from automl.fairness.metrics import false_negative_rate subgroups = X[['race', 'sex']] false_negative_rate(y_true, y_pred, subgroups)
False Omission Rate Disparity
 class automl.fairness.metrics.model. FalseOmissionRateScorer ( protected_attributes , distance_measure = 'diff' , reduction = 'mean' )

Measures the disparity of a model’s false omission rate between subgroups and the rest of the population.
For each subgroup, the disparity is measured by comparing the false omission rate on instances of a subgroup against the rest of the population.
False Omission Rate (also known as FOR) is calculated as FN / (FN + TN), where FN and TN are the number of false negatives and true negatives, respectively.
 Perfect score

A perfect score for this metric means that the model does not make more mistakes on the negative class for any of the subgroups more often than it does for the rest of the population. For example, if the protected attributes are race and sex, then a perfect false omission rate disparity would mean that all combinations of values for race and sex have identical false omission rates. Perfect values are:

1 if using
'ratio'
asdistance_measure
. 
0 if using
'diff'
asdistance_measure
.

 Parameters


protected_attributes ( pandas.Series , numpy.ndarray , list , str ) – Array of attributes or single attribute that should be treated as protected. If an attribute is protected, then all of its unique values are considered as subgroups.

distance_measure (str, optional ) –
Determines the distance used to compare a subgroup’s metric against the rest of the population. Possible values are:

'ratio'
: Uses(subgroup_val / rest_of_pop_val)
.
Inverted to always be >= 1 if needed. *
'diff'
: Uses subgroup_val  rest_of_pop_val 
.Default is
'diff'
. 

reduction (str, optional ) –
Determines how to reduce scores on all subgroups to a single output. Possible values are:

'max'
: Returns the maximal value among all subgroup metrics. 
'mean'
: Returns the mean over all subgroup metrics. 
None
: Returns a{subgroup: subgroup_metric, ...}
dict.
Default is
'mean'
. 

Examples
from automl.fairness.metrics import FalseOmissionRateScorer scorer = FalseOmissionRateScorer(['race', 'sex']) scorer(model, X, y_true)
 __call__ ( model , X , y_true , supplementary_features = None )

Compute the metric using a model’s predictions on a given array of instances
X
. Parameters


model ( object ) – Object that implements a predict(X) function to collect categorical predictions.

X ( pandas.DataFrame ) – Array of instances to compute the metric on.

y_true ( pandas.Series , numpy.ndarray , list ) – Array of groundtruth labels.

supplementary_features (pandas.DataFrame, optional ) – Array of supplementary features for each instance. Used in case one attribute in
self.protected_attributes
is not contained byX
(e.g. if the protected attribute is not used by the model). Default isNone
, equivalent to empty DataFrame

 Returns

The computed metric value, with format according to
self.reduction
.  Return type
 Raises

AutoMLxValueError –

if a feature is present in both
X
andsupplementary_features
.

 automl.fairness.metrics.model. false_omission_rate ( y_true , y_pred , subgroups , distance_measure = 'diff' , reduction = 'mean' )

Measures the disparity of a model’s false omission rate between subgroups and the rest of the population.
For more details, refer to
FalseOmissionRateScorer
. Parameters


y_true ( pandas.Series , numpy.ndarray , list ) – Array of groundtruth labels.

y_pred ( pandas.Series , numpy.ndarray , list ) – Array of model predictions.

subgroups ( pandas.DataFrame ) – Dataframe containing protected attributes for each instance.

distance_measure (str, optional ) –
Determines the distance used to compare a subgroup’s metric against the rest of the population. Possible values are:

'ratio'
: Uses(subgroup_val / rest_of_pop_val)
.
Inverted to always be >= 1 if needed. *
'diff'
: Uses subgroup_val  rest_of_pop_val 
.Default is
'diff'
. 

reduction (str, optional ) –
Determines how to reduce scores on all subgroups to a single output. Possible values are:

'max'
: Returns the maximal value among all subgroup metrics. 
'mean'
: Returns the mean over all subgroup metrics. 
None
: Returns a{subgroup: subgroup_metric, ...}
dict.
Default is
'mean'
. 

 Returns

The computed metric value, with format according to reduction .
 Return type
Examples
from automl.fairness.metrics import false_omission_rate subgroups = X[['race', 'sex']] false_omission_rate(y_true, y_pred, subgroups)
False Discovery Rate Disparity
 class automl.fairness.metrics.model. FalseDiscoveryRateScorer ( protected_attributes , distance_measure = 'diff' , reduction = 'mean' )

Measures the disparity of a model’s false discovery rate between subgroups and the rest of the population.
For each subgroup, the disparity is measured by comparing the false discovery rate on instances of a subgroup against the rest of the population.
False Discovery Rate (also known as FDR) is calculated as FP / (FP + TP), where FP and TP are the number of false positives and true positives, respectively.
 Perfect score

A perfect score for this metric means that the model does not make more mistakes on the positive class for any of the subgroups more often than it does for the rest of the population. For example, if the protected attributes are race and sex, then a perfect false discovery rate disparity would mean that all combinations of values for race and sex have identical false discovery rates. Perfect values are:

1 if using
'ratio'
asdistance_measure
. 
0 if using
'diff'
asdistance_measure
.

 Parameters


protected_attributes ( pandas.Series , numpy.ndarray , list , str ) – Array of attributes or single attribute that should be treated as protected. If an attribute is protected, then all of its unique values are considered as subgroups.

distance_measure (str, optional ) –
Determines the distance used to compare a subgroup’s metric against the rest of the population. Possible values are:

'ratio'
: Uses(subgroup_val / rest_of_pop_val)
.
Inverted to always be >= 1 if needed. *
'diff'
: Uses subgroup_val  rest_of_pop_val 
.Default is
'diff'
. 

reduction (str, optional ) –
Determines how to reduce scores on all subgroups to a single output. Possible values are:

'max'
: Returns the maximal value among all subgroup metrics. 
'mean'
: Returns the mean over all subgroup metrics. 
None
: Returns a{subgroup: subgroup_metric, ...}
dict.
Default is
'mean'
. 

Examples
from automl.fairness.metrics import FalseDiscoveryRateScorer scorer = FalseDiscoveryRateScorer(['race', 'sex']) scorer(model, X, y_true)
 __call__ ( model , X , y_true , supplementary_features = None )

Compute the metric using a model’s predictions on a given array of instances
X
. Parameters


model ( object ) – Object that implements a predict(X) function to collect categorical predictions.

X ( pandas.DataFrame ) – Array of instances to compute the metric on.

y_true ( pandas.Series , numpy.ndarray , list ) – Array of groundtruth labels.

supplementary_features (pandas.DataFrame, optional ) – Array of supplementary features for each instance. Used in case one attribute in
self.protected_attributes
is not contained byX
(e.g. if the protected attribute is not used by the model). Default isNone
, equivalent to empty DataFrame

 Returns

The computed metric value, with format according to
self.reduction
.  Return type
 Raises

AutoMLxValueError –

if a feature is present in both
X
andsupplementary_features
.

 automl.fairness.metrics.model. false_discovery_rate ( y_true , y_pred , subgroups , distance_measure = 'diff' , reduction = 'mean' )

Measures the disparity of a model’s false discovery rate between subgroups and the rest of the population.
For more details, refer to
FalseDiscoveryRateScorer
. Parameters


y_true ( pandas.Series , numpy.ndarray , list ) – Array of groundtruth labels.

y_pred ( pandas.Series , numpy.ndarray , list ) – Array of model predictions.

subgroups ( pandas.DataFrame ) – Dataframe containing protected attributes for each instance.

distance_measure (str, optional ) –
Determines the distance used to compare a subgroup’s metric against the rest of the population. Possible values are:

'ratio'
: Uses(subgroup_val / rest_of_pop_val)
.
Inverted to always be >= 1 if needed. *
'diff'
: Uses subgroup_val  rest_of_pop_val 
.Default is
'diff'
. 

reduction (str, optional ) –
Determines how to reduce scores on all subgroups to a single output. Possible values are:

'max'
: Returns the maximal value among all subgroup metrics. 
'mean'
: Returns the mean over all subgroup metrics. 
None
: Returns a{subgroup: subgroup_metric, ...}
dict.
Default is
'mean'
. 

 Returns

The computed metric value, with format according to reduction .
 Return type
Examples
from automl.fairness.metrics import false_discovery_rate subgroups = X[['race', 'sex']] false_discovery_rate(y_true, y_pred, subgroups)
Error Rate Disparity
 class automl.fairness.metrics.model. ErrorRateScorer ( protected_attributes , distance_measure = 'diff' , reduction = 'mean' )

Measures the disparity of a model’s error rate between subgroups and the rest of the population.
For each subgroup, the disparity is measured by comparing the error rate on instances of a subgroup against the rest of the population.
Error Rate (also known as inaccuracy) is calculated as (FP + FN) / N, where FP and FN are the number of false positives and false negatives, respectively, while N is the total Number of instances.
 Perfect score

A perfect score for this metric means that the model does not make more mistakes for any of the subgroups more often than it does for the rest of the population. For example, if the protected attributes are race and sex, then a perfect error rate disparity would mean that all combinations of values for race and sex have identical error rates. Perfect values are:

1 if using
'ratio'
asdistance_measure
. 
0 if using
'diff'
asdistance_measure
.

 Parameters


protected_attributes ( pandas.Series , numpy.ndarray , list , str ) – Array of attributes or single attribute that should be treated as protected. If an attribute is protected, then all of its unique values are considered as subgroups.

distance_measure (str, optional ) –
Determines the distance used to compare a subgroup’s metric against the rest of the population. Possible values are:

'ratio'
: Uses(subgroup_val / rest_of_pop_val)
.
Inverted to always be >= 1 if needed. *
'diff'
: Uses subgroup_val  rest_of_pop_val 
.Default is
'diff'
. 

reduction (str, optional ) –
Determines how to reduce scores on all subgroups to a single output. Possible values are:

'max'
: Returns the maximal value among all subgroup metrics. 
'mean'
: Returns the mean over all subgroup metrics. 
None
: Returns a{subgroup: subgroup_metric, ...}
dict.
Default is
'mean'
. 

Examples
from automl.fairness.metrics import ErrorRateScorer scorer = ErrorRateScorer(['race', 'sex']) scorer(model, X, y_true)
 __call__ ( model , X , y_true , supplementary_features = None )

Compute the metric using a model’s predictions on a given array of instances
X
. Parameters


model ( object ) – Object that implements a predict(X) function to collect categorical predictions.

X ( pandas.DataFrame ) – Array of instances to compute the metric on.

y_true ( pandas.Series , numpy.ndarray , list ) – Array of groundtruth labels.

supplementary_features (pandas.DataFrame, optional ) – Array of supplementary features for each instance. Used in case one attribute in
self.protected_attributes
is not contained byX
(e.g. if the protected attribute is not used by the model). Default isNone
, equivalent to empty DataFrame

 Returns

The computed metric value, with format according to
self.reduction
.  Return type
 Raises

AutoMLxValueError –

if a feature is present in both
X
andsupplementary_features
.

 automl.fairness.metrics.model. error_rate ( y_true , y_pred , subgroups , distance_measure = 'diff' , reduction = 'mean' )

Measures the disparity of a model’s error rate between subgroups and the rest of the population.
For more details, refer to
ErrorRateScorer
. Parameters


y_true ( pandas.Series , numpy.ndarray , list ) – Array of groundtruth labels.

y_pred ( pandas.Series , numpy.ndarray , list ) – Array of model predictions.

subgroups ( pandas.DataFrame ) – Dataframe containing protected attributes for each instance.

distance_measure (str, optional ) –
Determines the distance used to compare a subgroup’s metric against the rest of the population. Possible values are:

'ratio'
: Uses(subgroup_val / rest_of_pop_val)
.
Inverted to always be >= 1 if needed. *
'diff'
: Uses subgroup_val  rest_of_pop_val 
.Default is
'diff'
. 

reduction (str, optional ) –
Determines how to reduce scores on all subgroups to a single output. Possible values are:

'max'
: Returns the maximal value among all subgroup metrics. 
'mean'
: Returns the mean over all subgroup metrics. 
None
: Returns a{subgroup: subgroup_metric, ...}
dict.
Default is
'mean'
. 

 Returns

The computed metric value, with format according to reduction .
 Return type
Examples
from automl.fairness.metrics import error_rate subgroups = X[['race', 'sex']] error_rate(y_true, y_pred, subgroups)
Equalized Odds
 class automl.fairness.metrics.model. EqualizedOddsScorer ( protected_attributes , distance_measure = 'diff' , reduction = 'mean' )

Measures the disparity of a model’s true positive and false positive rates between subgroups and the rest of the population.
The disparity is measured by comparing the true positive and false positive rates on instances of a subgroup against the rest of the population.
True Positive Rate (also known as TPR, recall, or sensitivity) is calculated as TP / (TP + FN), where TP and FN are the number of true positives and false negatives, respectively.
False Positive Rate (also known as FPR or fallout) is calculated as FP / (FP + TN), where FP and TN are the number of false positives and true negatives, respectively.
Equalized Odds [1] is computed by taking the maximum distance between TPR and FPR for a subgroup against the rest of the population.
 Perfect score

A perfect score for this metric means that the model has the same TPR and FPR when comparing a subgroup to the rest of the population. For example, if the protected attributes are race and sex, then a perfect Equalized Odds disparity would mean that all combinations of values for race and sex have identical TPR and FPR. Perfect values are:

1 if using
'ratio'
asdistance_measure
. 
0 if using
'diff'
asdistance_measure
.

 Parameters


protected_attributes ( pandas.Series , numpy.ndarray , list , str ) – Array of attributes or single attribute that should be treated as protected. If an attribute is protected, then all of its unique values are considered as subgroups.

distance_measure (str, optional ) –
Determines the distance used to compare a subgroup’s metric against the rest of the population. Possible values are:

'ratio'
: Uses(subgroup_val / rest_of_pop_val)
.
Inverted to always be >= 1 if needed. *
'diff'
: Uses subgroup_val  rest_of_pop_val 
.Default is
'diff'
. 

reduction (str, optional ) –
Determines how to reduce scores on all subgroups to a single output. Possible values are:

'max'
: Returns the maximal value among all subgroup metrics. 
'mean'
: Returns the mean over all subgroup metrics. 
None
: Returns a{subgroup: subgroup_metric, ...}
dict.
Default is
'mean'
. 

References
[1] Moritz Hardt et al. “Equality of Opportunity in Supervised Learning”. Advances in Neural Information Processing Systems. 2016.
Examples
from automl.fairness.metrics import EqualizedOddsScorer scorer = EqualizedOddsScorer(['race', 'sex']) scorer(model, X, y_true)
 __call__ ( model , X , y_true , supplementary_features = None )

Compute the metric using a model’s predictions on a given array of instances
X
. Parameters


model ( object ) – Object that implements a predict(X) function to collect categorical predictions.

X ( pandas.DataFrame ) – Array of instances to compute the metric on.

y_true ( pandas.Series , numpy.ndarray , list ) – Array of groundtruth labels.

supplementary_features (pandas.DataFrame, optional ) – Array of supplementary features for each instance. Used in case one attribute in
self.protected_attributes
is not contained byX
(e.g. if the protected attribute is not used by the model). Default isNone
, equivalent to empty DataFrame

 Returns

The computed metric value, with format according to
self.reduction
.  Return type
 Raises

AutoMLxValueError –

if a feature is present in both
X
andsupplementary_features
.

 automl.fairness.metrics.model. equalized_odds ( y_true , y_pred , subgroups , distance_measure = 'diff' , reduction = 'mean' )

Measures the disparity of a model’s true positive and false positive rates between subgroups and the rest of the population.
For more details, refer to
EqualizedOddsScorer
. Parameters


y_true ( pandas.Series , numpy.ndarray , list ) – Array of groundtruth labels.

y_pred ( pandas.Series , numpy.ndarray , list ) – Array of model predictions.

subgroups ( pandas.DataFrame ) – Dataframe containing protected attributes for each instance.

distance_measure (str, optional ) –
Determines the distance used to compare a subgroup’s metric against the rest of the population. Possible values are:

'ratio'
: Uses(subgroup_val / rest_of_pop_val)
.
Inverted to always be >= 1 if needed. *
'diff'
: Uses subgroup_val  rest_of_pop_val 
.Default is
'diff'
. 

reduction (str, optional ) –
Determines how to reduce scores on all subgroups to a single output. Possible values are:

'max'
: Returns the maximal value among all subgroup metrics. 
'mean'
: Returns the mean over all subgroup metrics. 
None
: Returns a{subgroup: subgroup_metric, ...}
dict.
Default is
'mean'
. 

 Returns

The computed metric value, with format according to reduction .
 Return type
Examples
from automl.fairness.metrics import equalized_odds subgroups = X[['race', 'sex']] equalized_odds(y_true, y_pred, subgroups)
Theil Index
 class automl.fairness.metrics.model. TheilIndexScorer ( protected_attributes , distance_measure = None , reduction = 'mean' )

Measures the disparity of a model’s predictions according to groundtruth labels, as proposed by Speicher et al. [1].
Intuitively, the Theil Index can be thought of as a measure of the divergence between a subgroup’s different error distributions (i.e. false positives and false negatives) against the rest of the population.
 Perfect score

The perfect score for this metric is 0, meaning that the model does not have a different error distribution for any subgroup when compared to the rest of the population. For example, if the protected attributes are race and sex, then a perfect Theil Index disparity would mean that all combinations of values for race and sex have identical error distributions.
 Parameters


protected_attributes ( pandas.Series , numpy.ndarray , list , str ) – Array of attributes or single attribute that should be treated as protected. If an attribute is protected, then all of its unique values are considered as subgroups.

reduction (str, optional ) –
Determines how to reduce scores on all subgroups to a single output. Possible values are:

'max'
: Returns the maximal value among all subgroup metrics. 
'mean'
: Returns the mean over all subgroup metrics. 
None
: Returns a{subgroup: subgroup_metric, ...}
dict.
Default is
'mean'
. 

References
 [1]: Speicher, Till, et al. “A unified approach to quantifying algorithmic

unfairness: Measuring individual & group unfairness via inequality indices.” Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 2018.
Examples
from automl.fairness.metrics import TheilIndexScorer scorer = TheilIndexScorer(['race', 'sex']) scorer(model, X, y_true)
 __call__ ( model , X , y_true , supplementary_features = None )

Compute the metric using a model’s predictions on a given array of instances
X
. Parameters


model ( object ) – Object that implements a predict(X) function to collect categorical predictions.

X ( pandas.DataFrame ) – Array of instances to compute the metric on.

y_true ( pandas.Series , numpy.ndarray , list ) – Array of groundtruth labels.

supplementary_features (pandas.DataFrame, optional ) – Array of supplementary features for each instance. Used in case one attribute in
self.protected_attributes
is not contained byX
(e.g. if the protected attribute is not used by the model). Default isNone
, equivalent to empty DataFrame

 Returns

The computed metric value, with format according to
self.reduction
.  Return type
 Raises

AutoMLxValueError –

if a feature is present in both
X
andsupplementary_features
.

 automl.fairness.metrics.model. theil_index ( y_true , y_pred , subgroups , distance_measure = None , reduction = 'mean' )

Measures the disparity of a model’s predictions according to groundtruth labels, as proposed by Speicher et al. [1].
For more details, refer to
TheilIndexScorer
. Parameters


y_true ( pandas.Series , numpy.ndarray , list ) – Array of groundtruth labels.

y_pred ( pandas.Series , numpy.ndarray , list ) – Array of model predictions.

subgroups ( pandas.DataFrame ) – Dataframe containing protected attributes for each instance.

distance_measure (str, optional ) –
Determines the distance used to compare a subgroup’s metric against the rest of the population. Possible values are:

'ratio'
: Uses(subgroup_val / rest_of_pop_val)
.
Inverted to always be >= 1 if needed. *
'diff'
: Uses subgroup_val  rest_of_pop_val 
.Default is
'diff'
. 

reduction (str, optional ) –
Determines how to reduce scores on all subgroups to a single output. Possible values are:

'max'
: Returns the maximal value among all subgroup metrics. 
'mean'
: Returns the mean over all subgroup metrics. 
None
: Returns a{subgroup: subgroup_metric, ...}
dict.
Default is
'mean'
. 

 Returns

The computed metric value, with format according to reduction .
 Return type
 Raises

AutoMLxValueError – If distance_measure values are given to Theil Index.
References
 [1]: Speicher, Till, et al. “A unified approach to quantifying algorithmic

unfairness: Measuring individual & group unfairness via inequality indices.” Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 2018.
Examples
from automl.fairness.metrics import theil_index subgroups = X[['race', 'sex']] theil_index(y_true, y_pred, subgroups)
Evaluating a Dataset
Statistical Parity
 class automl.fairness.metrics.dataset. DatasetStatisticalParityScorer ( protected_attributes , distance_measure = 'diff' , reduction = 'mean' )

Measures the statistical parity [1] of a dataset. Statistical parity (also known as Base Rate or Disparate Impact) for a dataset states that a dataset is unbiased if the label is independent of the protected attribute.
For each subgroup, statistical parity is computed as the ratio of positive labels in a subgroup.
Statistical Parity (also known as Base Rate or Disparate Impact) is calculated as PL / N, where PL and N are the number of Positive Labels and total number of instances, respectively.
 Perfect score

A perfect score for this metric means that the dataset does not have a different ratio of positive labels for a subgroup than it does for the rest of the population. For example, if the protected attributes are race and sex, then a perfect statistical parity would mean that all combinations of values for race and sex have identical ratios of positive labels. Perfect values are:

1 if using
'ratio'
asdistance_measure
. 
0 if using
'diff'
asdistance_measure
.

 Parameters


protected_attributes ( pandas.Series , numpy.ndarray , list , str ) – Array of attributes or single attribute that should be treated as protected. If an attribute is protected, then all of its unique values are considered as subgroups.

distance_measure ( str ) –
Determines the distance used to compare a subgroup’s metric against the rest of the population. Possible values are:

'ratio'
: Uses(subgroup_val / rest_of_pop_val)
.
Inverted to always be >= 1 if needed. *
'diff'
: Uses subgroup_val  rest_of_pop_val 
.Default is
'diff'
. 

reduction ( str ) –
Determines how to reduce scores on all subgroups to a single output. Possible values are:

'max'
: Returns the maximal value among all subgroup metrics. 
'mean'
: Returns the mean over all subgroup metrics. 
None
: Returns a{subgroup: subgroup_metric, ...}
dict.
Default is
'mean'
. 

References
[1] Cynthia Dwork et al. “Fairness Through Awareness”. Innovations in Theoretical Computer Science. 2012.
Examples
from automl.fairness.metrics import DatasetStatisticalParityScorer scorer = DatasetStatisticalParityScorer(['race', 'sex']) scorer(X=X, y_true=y_true) scorer(None, X, y_true)
 __call__ ( model = None , X = None , y_true = None , supplementary_features = None )

Compute the metric on a given array of instances
X
. Parameters


model ( object ) – Object that implements a predict(X) function to collect categorical predictions.

X ( pandas.DataFrame. ) – Array of instances to compute the metric on.

y_true ( pandas.Series , numpy.ndarray , list ) – Array of groundtruth labels.

supplementary_features (pandas.DataFrame, optional ) – Array of supplementary features for each instance. Used in case one attribute in
self.protected_attributes
is not contained byX
(e.g. if the protected attribute is not used by the model). Raise an AutoMLxValueError if a feature is present in bothX
andsupplementary_features
. Default isNone
, equivalent to empty DataFrame

 Returns

The computed metric value, with format according to
self.reduction
.  Return type
 Raises

AutoMLxValueError – If a feature is present in both
X
andsupplementary_features
.
 automl.fairness.metrics.dataset. dataset_statistical_parity ( y_true , subgroups , distance_measure = 'diff' , reduction = 'mean' )

Measures the statistical parity of a dataset.
For more details, refer to
DatasetStatisticalParityScorer
. Parameters


y_true (pandas.Series, numpy.ndarray, list, optional ) – Array of groundtruth labels

subgroups ( pandas.DataFrame ) – Dataframe containing protected attributes for each instance.

distance_measure ( str ) –
Determines the distance used to compare a subgroup’s metric against the rest of the population. Possible values are:

'ratio'
: Uses(subgroup_val / rest_of_pop_val)
.
Inverted to always be >= 1 if needed. *
'diff'
: Uses subgroup_val  rest_of_pop_val 
.Default is
'diff'
. 

reduction ( str ) –
Determines how to reduce scores on all subgroups to a single output. Possible values are:

'max'
: Returns the maximal value among all subgroup metrics. 
'mean'
: Returns the mean over all subgroup metrics. 
None
: Returns a{subgroup: subgroup_metric, ...}
dict.
Default is
'mean'
. 

Examples
from automl.fairness.metrics import dataset_statistical_parity subgroups = X[['race', 'sex']] dataset_statistical_parity(y_true, subgroups)
Consistency
 class automl.fairness.metrics.dataset. ConsistencyScorer ( protected_attributes )

Measures the consistency of a dataset.
Consistency is measured as the number of ratio of instances that have a different label from the k=5 nearest neighbors.
 Perfect score

A perfect score for this metric is 0, meaning that the dataset does not have different labels for instances that are similar to one another.
 Parameters

protected_attributes ( pandas.Series , numpy.ndarray , list , str ) – Array of attributes or single attribute that should be treated as protected. If an attribute is protected, then all of its unique values are considered as subgroups.
Examples
from automl.fairness.metrics import ConsistencyScorer scorer = ConsistencyScorer(['race', 'sex']) scorer(X=X, y_true=y_true) scorer(None, X, y_true)
 __call__ ( model = None , X = None , y_true = None , supplementary_features = None )

Call self as a function.
 automl.fairness.metrics.dataset. consistency ( y_true , subgroups )

Measures the consistency of a dataset.
For more details, refer to
ConsistencyScorer
. Parameters


y_true (pandas.Series, numpy.ndarray, list, optional ) – Array of groundtruth labels

subgroups ( pandas.DataFrame ) – Dataframe containing protected attributes for each instance.

Examples
from automl.fairness.metrics import consistency subgroups = X[['race', 'sex']] consistency(y_true, subgroups)
Smoothed EDF
 class automl.fairness.metrics.dataset. SmoothedEDFScorer ( protected_attributes )

Measures the smoothed Empirical Differential Fairness (EDF) of a dataset, as proposed by Foulds et al. [1].
Smoothed EDF returns the minimal exponential deviation of positive target ratios comparing a subgroup to the rest of the population.
This metric is related to
DatasetStatisticalParity
with reduction=’max’ and distance_measure=’ratio’ , with the only difference being thatSmoothedEDFScorer
returns a logarithmic value instead. Perfect score

A perfect score for this metric is 0, meaning that the dataset does not have a different ratio of positive labels for a subgroup than it does for the rest of the population. For example, if the protected attributes are race and sex, then a perfect smoothed EDF would mean that all combinations of values for race and sex have identical ratios of positive labels.
 Parameters

protected_attributes ( pandas.Series , numpy.ndarray , list , str ) – Array of attributes or single attribute that should be treated as protected. If an attribute is protected, then all of its unique values are considered as subgroups.
References
 [1] Foulds, James R., et al. “An intersectional definition of fairness.”

2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020.
Examples
from automl.fairness.metrics import SmoothedEDFScorer scorer = SmoothedEDFScorer(['race', 'sex']) scorer(X=X, y_true=y_true) scorer(None, X, y_true)
 __call__ ( model = None , X = None , y_true = None , supplementary_features = None )

Call self as a function.
 automl.fairness.metrics.dataset. smoothed_edf ( y_true , subgroups )

Measures the smoothed Empirical Differential Fairness (EDF) of a dataset, as proposed by Foulds et al. [1].
For more details, refer to
SmoothedEDFScorer
. Parameters


y_true (pandas.Series, numpy.ndarray, list, optional ) – Array of groundtruth labels

subgroups ( pandas.DataFrame ) – Dataframe containing protected attributes for each instance.

References
 [1] Foulds, James R., et al. “An intersectional definition of fairness.”

2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020.
Examples
from automl.fairness.metrics import smoothed_edf subgroups = X[['race', 'sex']] smoothed_edf(y_true, subgroups)