diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py
index 3d01d5eeaf12d..ef31523a478f4 100644
--- a/sklearn/metrics/pairwise.py
+++ b/sklearn/metrics/pairwise.py
@@ -29,6 +29,7 @@
 from ..utils._mask import _get_mask
 from ..utils.parallel import delayed, Parallel
 from ..utils.fixes import sp_version, parse_version
+from ..utils._param_validation import validate_params
 
 from ._pairwise_distances_reduction import ArgKmin
 from ._pairwise_fast import _chi2_kernel_fast, _sparse_manhattan
@@ -1403,6 +1404,7 @@ def cosine_similarity(X, Y=None, dense_output=True):
     return K
 
 
+@validate_params({"X": ["array-like"], "Y": ["array-like", None]})
 def additive_chi2_kernel(X, Y=None):
     """Compute the additive chi-squared kernel between observations in X and Y.
 
@@ -1423,7 +1425,7 @@ def additive_chi2_kernel(X, Y=None):
     X : array-like of shape (n_samples_X, n_features)
         A feature array.
 
-    Y : ndarray of shape (n_samples_Y, n_features), default=None
+    Y : array-like of shape (n_samples_Y, n_features), default=None
         An optional second feature array. If `None`, uses `Y=X`.
 
     Returns
@@ -1451,8 +1453,6 @@ def additive_chi2_kernel(X, Y=None):
       International Journal of Computer Vision 2007
       https://hal.archives-ouvertes.fr/hal-00171412/document
     """
-    if issparse(X) or issparse(Y):
-        raise ValueError("additive_chi2 does not support sparse matrices.")
     X, Y = check_pairwise_arrays(X, Y)
     if (X < 0).any():
         raise ValueError("X contains negative values.")
diff --git a/sklearn/metrics/tests/test_pairwise.py b/sklearn/metrics/tests/test_pairwise.py
index 3624983c4c481..c1ee728f6e71e 100644
--- a/sklearn/metrics/tests/test_pairwise.py
+++ b/sklearn/metrics/tests/test_pairwise.py
@@ -390,8 +390,6 @@ def test_pairwise_kernels(metric):
     Y_sparse = csr_matrix(Y)
     if metric in ["chi2", "additive_chi2"]:
         # these don't support sparse matrices yet
-        with pytest.raises(ValueError):
-            pairwise_kernels(X_sparse, Y=Y_sparse, metric=metric)
         return
     K1 = pairwise_kernels(X_sparse, Y=Y_sparse, metric=metric)
     assert_allclose(K1, K2)
@@ -1231,12 +1229,6 @@ def test_chi_square_kernel():
     with pytest.raises(ValueError):
         chi2_kernel([[0, 1]], [[0.2, 0.2, 0.6]])
 
-    # sparse matrices
-    with pytest.raises(ValueError):
-        chi2_kernel(csr_matrix(X), csr_matrix(Y))
-    with pytest.raises(ValueError):
-        additive_chi2_kernel(csr_matrix(X), csr_matrix(Y))
-
 
 @pytest.mark.parametrize(
     "kernel",
diff --git a/sklearn/tests/test_public_functions.py b/sklearn/tests/test_public_functions.py
index acc44ce60c755..a70ca2fa1c046 100644
--- a/sklearn/tests/test_public_functions.py
+++ b/sklearn/tests/test_public_functions.py
@@ -122,6 +122,7 @@ def _check_function_param_validation(
     "sklearn.metrics.median_absolute_error",
     "sklearn.metrics.multilabel_confusion_matrix",
     "sklearn.metrics.mutual_info_score",
+    "sklearn.metrics.pairwise.additive_chi2_kernel",
     "sklearn.metrics.r2_score",
     "sklearn.metrics.roc_curve",
     "sklearn.metrics.zero_one_loss",