diff --git a/orangecontrib/fairness/evaluation/scoring.py b/orangecontrib/fairness/evaluation/scoring.py
index 93b4d00..278eaa6 100644
--- a/orangecontrib/fairness/evaluation/scoring.py
+++ b/orangecontrib/fairness/evaluation/scoring.py
@@ -99,8 +99,8 @@ class StatisticalParityDifference(FairnessScorer):
 
     name = "SPD"
     long_name = str(
-        "<p>Statistical Parity Difference (SPD): Measures the difference in ratios of "
-        "favorable outcomes. An ideal value is 0.0.</p>"
+        "<p>Statistical Parity Difference (SPD): The difference in favorable "
+        "outcomes proportions between groups. An ideal value is 0.0.</p>"
         "<ul>"
         "<li>SPD &lt; 0: The privileged group has a higher rate of favorable outcomes.</li>"
         "<li>SPD &gt; 0: The privileged group has a lower rate of favorable outcomes.</li>"
@@ -118,9 +118,8 @@ class EqualOpportunityDifference(FairnessScorer):
 
     name = "EOD"
     long_name = str(
-        "<p>Equal Opportunity Difference (EOD): It measures the difference in "
-        "true positive rates. An ideal value is 0.0, indicating the difference "
-        "in true positive rates is the same for both groups.</p>"
+        "<p>Equal Opportunity Difference (EOD): The difference in true positive rates between "
+        "groups. An ideal value is 0.0 meaning both groups have the same true positive rate.</p>"
         "<ul>"
         "<li>EOD &lt; 0: The privileged group has a higher true positive rate.</li>"
         "<li>EOD &gt; 0: The privileged group has a lower true positive rate.</li>"
@@ -138,11 +137,9 @@ class AverageOddsDifference(FairnessScorer):
 
     name = "AOD"
     long_name = str(
-        "<p>Average Odds Difference (AOD): This metric calculates the average difference "
-        "between the true positive rates (correctly predicting a positive outcome) and false "
-        "positive rates (incorrectly predicting a positive outcome) for both the privileged "
-        "and unprivileged groups. A value of 0.0 indicates equal rates for both groups, "
-        "signifying fairness.</p>"
+        "<p>Average Odds Difference (AOD): The average of the differences in true "
+        "and false positive rates between privileged and unprivileged groups. "
+        "A value of 0.0 indicates equal rates for both groups.</p>"
         "<ul>"
         "<li>AOD &lt; 0: Indicates bias in favor of the privileged group.</li>"
         "<li>AOD &gt; 0: Indicates bias against the privileged group.</li>"
@@ -160,20 +157,17 @@ class DisparateImpact(FairnessScorer):
 
     name = "DI"
     long_name = str(
-        "<p>Disparate Impact (DI): The ratio of ratios of favorable outcomes for an unprivileged "
-        "group to that of the privileged group. An ideal value of 1.0 means the ratio is "
-        "the same for both groups.</p>"
+        "<p>Disparate Impact (DI) is the ratio of favorable outcome "
+        "proportions between an unprivileged and privileged group. "
+        "Value of 1.0 indicates that the ratio is equal for both groups.</p>"
         "<ul>"
-        "<li>DI &lt; 1.0: The privileged group receives favorable outcomes at a higher rate "
-        "than the unprivileged group.</li>"
-        "<li>DI &gt; 1.0: The privileged group receives favorable outcomes at a lower rate "
-        "than the unprivileged group.</li>"
+        "<li>DI &lt; 1.0: The privileged group has a higher rate of favorable outcomes.</li>"
+        "<li>DI &gt; 1.0: The privileged group has a lower rate of favorable outcomes.</li>"
         "</ul>"
     )
 
     # TODO: When using randomize, models sometimes predict the same class for all instances
     # This can lead to division by zero in the Disparate Impact score
-    # (and untrue results for the other scores)
-    # What is the best way to handle this?
+    # and untrue results for the other scores.
     def metric(self, classification_metric):
         return classification_metric.disparate_impact()
diff --git a/orangecontrib/fairness/modeling/adversarial.py b/orangecontrib/fairness/modeling/adversarial.py
index 637e83c..409bf99 100644
--- a/orangecontrib/fairness/modeling/adversarial.py
+++ b/orangecontrib/fairness/modeling/adversarial.py
@@ -154,7 +154,6 @@ def _fit_model(self, data):
 
         # Fit storage and fit functions were modified to use a Table/Storage object
         # This is because it's the easiest way to get the domain, and meta attributes
-        # TODO: Should I use the X,Y,W format instead of the table format ? (Same for the model)
         def fit(self, data: Table) -> AdversarialDebiasingModel:
             (
                 standard_dataset,
diff --git a/orangecontrib/fairness/modeling/postprocessing.py b/orangecontrib/fairness/modeling/postprocessing.py
index afc99c4..2848b1f 100644
--- a/orangecontrib/fairness/modeling/postprocessing.py
+++ b/orangecontrib/fairness/modeling/postprocessing.py
@@ -104,7 +104,6 @@ def incompatibility_reason(self, domain):
 
     # Fit storage and fit functions were modified to use a Table/Storage object
     # This is because it's the easiest way to get the domain, and meta attributes
-    # TODO: Should I use the X,Y,W format instead of the table format ? (Same for the model)
     def fit_storage(self, data):
         if isinstance(data, Table):
             self.fit(data)
diff --git a/orangecontrib/fairness/widgets/owadversarialdebiasing.py b/orangecontrib/fairness/widgets/owadversarialdebiasing.py
index e3e9b31..fbf50a7 100644
--- a/orangecontrib/fairness/widgets/owadversarialdebiasing.py
+++ b/orangecontrib/fairness/widgets/owadversarialdebiasing.py
@@ -72,6 +72,8 @@ class OWAdversarialDebiasing(ConcurrentWidgetMixin, OWBaseLearner):
     icon = "icons/adversarial_debiasing.svg"
     priority = 30
 
+    resizing_enabled = True
+
     class Inputs(OWBaseLearner.Inputs):
         """Inputs for the widgets, which are the same as for the super class (Data, Preprocessor)"""
 
@@ -112,7 +114,6 @@ class Information(OWBaseLearner.Information):
     )
 
     # We define the settings we want to use
-    # TODO: Should i use context/domain settings?
     hidden_layers_neurons = Setting(100)
     number_of_epochs = Setting(50)
     batch_size = Setting(128)
diff --git a/orangecontrib/fairness/widgets/owdatasetbias.py b/orangecontrib/fairness/widgets/owdatasetbias.py
index 3596b83..0fd7630 100644
--- a/orangecontrib/fairness/widgets/owdatasetbias.py
+++ b/orangecontrib/fairness/widgets/owdatasetbias.py
@@ -37,6 +37,7 @@ class OWDatasetBias(OWWidget):
 
     want_control_area = False
     resizing_enabled = False
+    resizing_enabled = True
 
     class Inputs:
         """Input for the widget - dataset."""
@@ -74,26 +75,25 @@ def set_data(self, data: Optional[Table]) -> None:
         disparate_impact = dataset_metric.disparate_impact()
         statistical_parity_difference = dataset_metric.statistical_parity_difference()
         self.disparate_impact_label.setText(
-            f"Disparate Impact (ideal = 1): {round(disparate_impact, 3)}"
+            f"Disparate Impact (ideal = 1): {round(disparate_impact, 3):.3f}"
         )
         self.disparate_impact_label.setToolTip(
-            "<p>Disparate Impact (DI): Measures the ratio of the ratios of favorable class "
-            "values for an unprivileged group to that of the privileged group. An ideal value "
-            "of 1.0 means the ratio of favorable class values is the same for both groups.</p>"
+            "<p>Disparate Impact (DI) is the ratio of favorable outcome "
+            "proportions between an unprivileged and privileged group. "
+            "Value of 1.0 indicates that the ratio is equal for both groups.</p>"
             "<ul>"
-            "<li>DI &lt; 1.0: The privileged group has a higher percentage of favorable class values.</li>"
-            "<li>DI &gt; 1.0: The privileged group has a lower percentage of favorable class values.</li>"
+            "<li>DI &lt; 1.0: The privileged group has a higher rate of favorable outcomes.</li>"
+            "<li>DI &gt; 1.0: The privileged group has a lower rate of favorable outcomes.</li>"
             "</ul>"
         )
         self.statistical_parity_difference_label.setText(
-            f"Statistical Parity Difference (ideal = 0): {round(statistical_parity_difference, 3)}"
+            f"Statistical Parity Difference (ideal = 0): {round(statistical_parity_difference, 3):.3f}"
         )
         self.statistical_parity_difference_label.setToolTip(
-            "<p>Statistical Parity Difference (SPD): Measures the difference in ratios of "
-            "favorable class values between the unprivileged and the privileged groups. An "
-            "ideal value for this metric is 0.</p>"
+            "<p>Statistical Parity Difference (SPD): The difference in favorable "
+            "outcomes proportions between groups. An ideal value is 0.0.</p>"
             "<ul>"
-            "<li>SPD &lt; 0: The privileged group has a higher percentage of favorable class values.</li>"
-            "<li>SPD &gt; 0: The privileged group has a lower percentage of favorable class values.</li>"
+            "<li>SPD &lt; 0: The privileged group has a higher rate of favorable outcomes.</li>"
+            "<li>SPD &gt; 0: The privileged group has a lower rate of favorable outcomes.</li>"
             "</ul>"
         )
diff --git a/orangecontrib/fairness/widgets/owreweighing.py b/orangecontrib/fairness/widgets/owreweighing.py
index bafd96f..ef927c2 100644
--- a/orangecontrib/fairness/widgets/owreweighing.py
+++ b/orangecontrib/fairness/widgets/owreweighing.py
@@ -41,7 +41,6 @@ def __call__(self, data):
         data = self.model.transform(data)
         return data.instance_weights
 
-    # TODO: Check if this is ok
     InheritEq = True
 
 
diff --git a/orangecontrib/fairness/widgets/utils.py b/orangecontrib/fairness/widgets/utils.py
index 72a6f93..6f144f4 100644
--- a/orangecontrib/fairness/widgets/utils.py
+++ b/orangecontrib/fairness/widgets/utils.py
@@ -309,7 +309,7 @@ def table_to_standard_dataset(data) -> None:
         data = Impute()(data)
 
     xdf, ydf, mdf = data.to_pandas_dfs()
-    # Merge xdf and ydf TODO: Check if I need to merge mdf
+    # Merge xdf and ydf (currently not merging mdf because it is not used)
     # This dataframe consists of all the data, the categorical variables values are
     # represented with the index of the value in domain[attribute].values
     df = ydf.merge(xdf, left_index=True, right_index=True)