diff --git a/examples/tutorials/curation/plot_1_automated_curation.py b/examples/tutorials/curation/plot_1_automated_curation.py
index 8f87be9de1..97fc05bc6e 100644
--- a/examples/tutorials/curation/plot_1_automated_curation.py
+++ b/examples/tutorials/curation/plot_1_automated_curation.py
@@ -43,8 +43,8 @@
 # model and some metadata about the model.
 
 model, model_info = sc.load_model(
-    repo_id = "SpikeInterface/toy_tetrode_model",
-    trusted = ['numpy.dtype']
+    repo_id="SpikeInterface/toy_tetrode_model",
+    trust_model=True
 )
 
 
@@ -87,9 +87,9 @@
 # a confidence for each unit contained in the ``sorting_analyzer``.
 
 labels = sc.model_based_label_units(
-    sorting_analyzer = sorting_analyzer,
-    repo_id = "SpikeInterface/toy_tetrode_model",
-    trusted = ['numpy.dtype']
+    sorting_analyzer=sorting_analyzer,
+    repo_id="SpikeInterface/toy_tetrode_model",
+    trust_model=True
 )
 
 print(labels)
diff --git a/src/spikeinterface/curation/model_based_curation.py b/src/spikeinterface/curation/model_based_curation.py
index 2c3f43c6dc..69c7f75419 100644
--- a/src/spikeinterface/curation/model_based_curation.py
+++ b/src/spikeinterface/curation/model_based_curation.py
@@ -473,14 +473,8 @@ def _load_model_from_folder(model_folder=None, model_name=None, trust_model=Fals
         skops_file = skops_files[0]
 
     if trust_model and trusted is None:
-        try:
-            model = skio.load(skops_file)
-        except UntrustedTypesFoundException as e:
-            exception_msg = str(e)
-            # the exception message contains the list of untrusted objects. The following
-            #  search assumes it is the only list in the message.
-            string_list = re.search(r"\[(.*?)\]", exception_msg).group()
-            trusted = [list_item for list_item in string_list.split("'") if len(list_item) > 2]
+        untrusted = skio.get_untrusted_types(file=skops_file)
+        trusted = untrusted
 
     model = skio.load(skops_file, trusted=trusted)
     _patch_sklearn_imputer_compatibility(model)
diff --git a/src/spikeinterface/curation/tests/test_model_based_curation.py b/src/spikeinterface/curation/tests/test_model_based_curation.py
index a42ba2250d..800817cdda 100644
--- a/src/spikeinterface/curation/tests/test_model_based_curation.py
+++ b/src/spikeinterface/curation/tests/test_model_based_curation.py
@@ -25,7 +25,10 @@ def model(trained_pipeline_path):
     It has been trained locally and, when applied to `sorting_analyzer_for_unitrefine_curation` will label its 10 units with
     the following labels: [1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0]."""
 
-    model = load_model(trained_pipeline_path, trusted=["numpy.dtype"])
+    model = load_model(
+        trained_pipeline_path,
+        trust_model=True,
+    )
     return model
 
 
@@ -66,7 +69,7 @@ def test_metric_ordering_independence(sorting_analyzer_for_unitrefine_curation,
     prediction_prob_dataframe_1 = model_based_label_units(
         sorting_analyzer=sorting_analyzer_for_unitrefine_curation,
         model_folder=trained_pipeline_path,
-        trusted=["numpy.dtype"],
+        trust_model=True,
     )
 
     sorting_analyzer_for_unitrefine_curation.compute(
@@ -76,7 +79,7 @@ def test_metric_ordering_independence(sorting_analyzer_for_unitrefine_curation,
     prediction_prob_dataframe_2 = model_based_label_units(
         sorting_analyzer=sorting_analyzer_for_unitrefine_curation,
         model_folder=trained_pipeline_path,
-        trusted=["numpy.dtype"],
+        trust_model=True,
     )
 
     assert prediction_prob_dataframe_1.equals(prediction_prob_dataframe_2)
@@ -194,7 +197,7 @@ def test_exception_raised_when_metric_params_not_equal(sorting_analyzer_for_unit
         "template_metrics", metric_names=["half_width", "peak_to_trough_duration", "number_of_peaks"]
     )
 
-    model, model_info = load_model(model_folder=trained_pipeline_path, trusted=["numpy.dtype"])
+    model, model_info = load_model(model_folder=trained_pipeline_path, trust_model=True)
     model_based_classification = ModelBasedClassification(sorting_analyzer_for_unitrefine_curation, model)
 
     # an error should be raised if `enforce_metric_params` is True
@@ -215,6 +218,6 @@ def test_exception_raised_when_metric_params_not_equal(sorting_analyzer_for_unit
         "template_metrics", metric_names=["half_width", "peak_to_trough_duration"]
     )
 
-    model, model_info = load_model(model_folder=trained_pipeline_path, trusted=["numpy.dtype"])
+    model, model_info = load_model(model_folder=trained_pipeline_path, trust_model=True)
     model_based_classification = ModelBasedClassification(sorting_analyzer_for_unitrefine_curation, model)
     model_based_classification._check_params_for_classification(enforce_metric_params=True, model_info=model_info)
diff --git a/src/spikeinterface/preprocessing/tests/test_decimate.py b/src/spikeinterface/preprocessing/tests/test_decimate.py
index 99a9e1128c..05d43a2c3e 100644
--- a/src/spikeinterface/preprocessing/tests/test_decimate.py
+++ b/src/spikeinterface/preprocessing/tests/test_decimate.py
@@ -126,7 +126,7 @@ def test_decimate_antialias_by_chunks(decimation_factor):
         for chunk_size in [137, int(decimated_rate * 2)]:
             rec3 = rec2.save(format="memory", chunk_size=chunk_size, n_jobs=1, progress_bar=False)
             traces3 = rec3.get_traces()
-            np.testing.assert_allclose(traces3, traces2, rtol=1e-6, atol=1e-6)
+            np.testing.assert_allclose(traces3, traces2, rtol=1e-5, atol=1e-5)
 
 
 @pytest.mark.parametrize("decimation_factor", [6, 10])