diff --git a/CHANGELOG.md b/CHANGELOG.md
index 8ec86995483..3cac9ed61cf 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -46,7 +46,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed a bug in `ssim` when `return_full_image=True` where the score was still reduced ([#1204](https://github.com/Lightning-AI/metrics/pull/1204))
 
 
--
+- Fixed mps support in jaccard index ([#1205](https://github.com/Lightning-AI/metrics/pull/1205))
 
 
 ## [0.9.3] - 2022-08-22
diff --git a/src/torchmetrics/functional/classification/jaccard.py b/src/torchmetrics/functional/classification/jaccard.py
index 4f9b9c2400e..e834d0691c5 100644
--- a/src/torchmetrics/functional/classification/jaccard.py
+++ b/src/torchmetrics/functional/classification/jaccard.py
@@ -62,7 +62,7 @@ def _jaccard_from_confmat(
 
         # If this class is absent in both target AND pred (union == 0), then use the absent_score for this class.
         scores = intersection.float() / union.float()
-        scores[union == 0] = absent_score
+        scores = scores.where(union != 0, torch.tensor(absent_score, dtype=scores.dtype, device=scores.device))
 
         if ignore_index is not None and 0 <= ignore_index < num_classes:
             scores = torch.cat(
diff --git a/src/torchmetrics/utilities/data.py b/src/torchmetrics/utilities/data.py
index abf19598343..f9cd329fb11 100644
--- a/src/torchmetrics/utilities/data.py
+++ b/src/torchmetrics/utilities/data.py
@@ -16,7 +16,12 @@
 import torch
 from torch import Tensor, tensor
 
-from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_1_6, _TORCH_GREATER_EQUAL_1_7, _TORCH_GREATER_EQUAL_1_8
+from torchmetrics.utilities.imports import (
+    _TORCH_GREATER_EQUAL_1_6,
+    _TORCH_GREATER_EQUAL_1_7,
+    _TORCH_GREATER_EQUAL_1_8,
+    _TORCH_GREATER_EQUAL_1_12,
+)
 
 if _TORCH_GREATER_EQUAL_1_8:
     deterministic = torch.are_deterministic_algorithms_enabled
@@ -242,7 +247,10 @@ def _squeeze_if_scalar(data: Any) -> Any:
 
 
 def _bincount(x: Tensor, minlength: Optional[int] = None) -> Tensor:
-    """``torch.bincount`` currently does not support deterministic mode on GPU.
+    """PyTorch currently does not support``torch.bincount`` for:
+
+        - deterministic mode on GPU.
+        - MPS devices
 
     This implementation fallback to a for-loop counting occurrences in that case.
 
@@ -253,7 +261,7 @@ def _bincount(x: Tensor, minlength: Optional[int] = None) -> Tensor:
     Returns:
         Number of occurrences for each unique element in x
     """
-    if x.is_cuda and deterministic():
+    if x.is_cuda and deterministic() or _TORCH_GREATER_EQUAL_1_12 and x.is_mps:
         if minlength is None:
             minlength = len(torch.unique(x))
         output = torch.zeros(minlength, device=x.device, dtype=torch.long)
diff --git a/src/torchmetrics/utilities/imports.py b/src/torchmetrics/utilities/imports.py
index 3622635d2ff..8e0f890cd63 100644
--- a/src/torchmetrics/utilities/imports.py
+++ b/src/torchmetrics/utilities/imports.py
@@ -104,6 +104,7 @@ def _compare_version(package: str, op: Callable, version: str) -> Optional[bool]
 _TORCH_GREATER_EQUAL_1_8: Optional[bool] = _compare_version("torch", operator.ge, "1.8.0")
 _TORCH_GREATER_EQUAL_1_10: Optional[bool] = _compare_version("torch", operator.ge, "1.10.0")
 _TORCH_GREATER_EQUAL_1_11: Optional[bool] = _compare_version("torch", operator.ge, "1.11.0")
+_TORCH_GREATER_EQUAL_1_12: Optional[bool] = _compare_version("torch", operator.ge, "1.12.0")
 
 _JIWER_AVAILABLE: bool = _package_available("jiwer")
 _NLTK_AVAILABLE: bool = _package_available("nltk")