diff --git a/src/sagemaker/clarify.py b/src/sagemaker/clarify.py
index 2f7f3dc53d..14bb675681 100644
--- a/src/sagemaker/clarify.py
+++ b/src/sagemaker/clarify.py
@@ -94,6 +94,8 @@
                             {object: object},
                         )
                     ],
+                    # Arbitrary JSON object as baseline
+                    {object: object},
                 ),
                 SchemaOptional("num_clusters"): int,
                 SchemaOptional("use_logit"): bool,
@@ -1211,7 +1213,7 @@ class SHAPConfig(ExplainabilityConfig):
 
     def __init__(
         self,
-        baseline: Optional[Union[str, List]] = None,
+        baseline: Optional[Union[str, List, Dict]] = None,
         num_samples: Optional[int] = None,
         agg_method: Optional[str] = None,
         use_logit: bool = False,
@@ -1224,7 +1226,7 @@ def __init__(
         """Initializes config for SHAP analysis.
 
         Args:
-            baseline (None or str or list): `Baseline dataset <https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-feature-attribute-shap-baselines.html>`_
+            baseline (None or str or list or dict): `Baseline dataset <https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-feature-attribute-shap-baselines.html>`_
                 for the Kernel SHAP algorithm, accepted in the form of:
                 S3 object URI, a list of rows (with at least one element),
                 or None (for no input baseline). The baseline dataset must have the same format
diff --git a/tests/unit/test_clarify.py b/tests/unit/test_clarify.py
index 714f9d316c..4fe1ecb5f2 100644
--- a/tests/unit/test_clarify.py
+++ b/tests/unit/test_clarify.py
@@ -563,14 +563,20 @@ def test_invalid_model_predicted_label_config():
     )
 
 
-def test_shap_config():
-    baseline = [
-        [
-            0.26124998927116394,
-            0.2824999988079071,
-            0.06875000149011612,
-        ]
-    ]
+@pytest.mark.parametrize(
+    "baseline",
+    [
+        ([[0.26124998927116394, 0.2824999988079071, 0.06875000149011612]]),
+        (
+            {
+                "instances": [
+                    {"features": [0.26124998927116394, 0.2824999988079071, 0.06875000149011612]}
+                ]
+            }
+        ),
+    ],
+)
+def test_valid_shap_config(baseline):
     num_samples = 100
     agg_method = "mean_sq"
     use_logit = True