diff --git a/docs/_book.yaml b/docs/_book.yaml
index 01746e7e70..1b75f50b7a 100644
--- a/docs/_book.yaml
+++ b/docs/_book.yaml
@@ -29,8 +29,6 @@ upper_tabs:
         path: /tensorboard/tensorboard_projector_plugin
       - title: "What-If tool"
         path: /tensorboard/what_if_tool
-      - title: "Fairness Indicators"
-        path: /tensorboard/fairness-indicators
       - title: "Profiling tool"
         path: /tensorboard/tensorboard_profiling_keras
       - title: "TensorBoard in notebooks"
diff --git a/docs/fairness-indicators.md b/docs/fairness-indicators.md
deleted file mode 100644
index 53d504c22a..0000000000
--- a/docs/fairness-indicators.md
+++ /dev/null
@@ -1,113 +0,0 @@
-# Evaluating Models with the Fairness Indicators Dashboard [Beta]
-
-![Fairness Indicators](./images/fairness-indicators.png)
-
-Fairness Indicators for TensorBoard enables easy computation of
-commonly-identified fairness metrics for _binary_ and _multiclass_ classifiers.
-With the plugin, you can visualize fairness evaluations for your runs and easily
-compare performance across groups.
-
-In particular, Fairness Indicators for TensorBoard allows you to evaluate and
-visualize model performance, sliced across defined groups of users. Feel
-confident about your results with confidence intervals and evaluations at
-multiple thresholds.
-
-Many existing tools for evaluating fairness concerns don’t work well on large
-scale datasets and models. At Google, it is important for us to have tools that
-can work on billion-user systems. Fairness Indicators will allow you to evaluate
-across any size of use case, in the TensorBoard environment or in
-[Colab](https://github.com/tensorflow/fairness-indicators).
-
-## Requirements
-
-To install Fairness Indicators for TensorBoard, run:
-
-```
-python3 -m virtualenv ~/tensorboard_demo
-source ~/tensorboard_demo/bin/activate
-pip install --upgrade pip
-pip install fairness_indicators
-pip install tensorboard-plugin-fairness-indicators
-```
-
-## Demo
-
-If you want to test out Fairness Indicators in TensorBoard, you can download
-sample TensorFlow Model Analysis evaluation results (eval_config.json, metrics
-and plots files) and a `demo.py` utility from Google Cloud Platform,
-[here](https://console.cloud.google.com/storage/browser/tensorboard_plugin_fairness_indicators/)
-using following command.
-```
-pip install gsutil
-gsutil cp -r gs://tensorboard_plugin_fairness_indicators/ .
-```
-
-Navigate to directory containing downloaded files.
-```
-cd tensorboard_plugin_fairness_indicators
-```
-
-This evaluation data is based on the
-[Civil Comments dataset](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification),
-calculated using Tensorflow Model Analysis's
-[model_eval_lib](https://github.com/tensorflow/model-analysis/blob/master/tensorflow_model_analysis/api/model_eval_lib.py)
-library. It also contains a sample TensorBoard summary data file for reference.
-
-
-The `demo.py` utility writes a TensorBoard summary data file, which will be read
-by TensorBoard to render the Fairness Indicators dashboard (See the
-[TensorBoard tutorial](https://github.com/tensorflow/tensorboard/blob/master/README.md)
-for more information on summary data files).
-
-Flags to be used with the `demo.py` utility:
-
--   `--logdir`: Directory where TensorBoard will write the summary
--   `--eval_result_output_dir`: Directory containing evaluation results
-    evaluated by TFMA (downloaded in last step)
-
-Run the `demo.py` utility to write the summary results in the log directory:
-
-`python demo.py --logdir=. --eval_result_output_dir=.`
-
-Run TensorBoard:
-
-Note: For this demo, please run TensorBoard from the same directory containing
-all the downloaded files.
-
-`tensorboard --logdir=.`
-
-This will start a local instance. After the local instance is started, a link
-will be displayed to the terminal. Open the link in your browser to view the
-Fairness Indicators dashboard.
-
-### Demo Colab
-
-[Fairness_Indicators_TensorBoard_Plugin_Example_Colab.ipynb](https://github.com/tensorflow/fairness-indicators/blob/master/g3doc/tutorials/Fairness_Indicators_TensorBoard_Plugin_Example_Colab.ipynb)
-contains an end-to-end demo to train and evaluate a model and visualize fairness evaluation
-results in TensorBoard.
-
-## Usage
-
-To use the Fairness Indicators with your own data and evaluations:
-
-1.  Train a new model and evaluate using
-    `tensorflow_model_analysis.run_model_analysis` or
-    `tensorflow_model_analysis.ExtractEvaluateAndWriteResult` API in
-    [model_eval_lib](https://github.com/tensorflow/model-analysis/blob/master/tensorflow_model_analysis/api/model_eval_lib.py).
-    For code snippets on how to do this, see the Fairness Indicators colab
-    [here](https://github.com/tensorflow/fairness-indicators).
-
-2.  Write Fairness Indicators Summary using `tensorboard_plugin_fairness_indicators.summary_v2` API.
-
-    ```
-    writer = tf.summary.create_file_writer(<logdir>)
-    with writer.as_default():
-        summary_v2.FairnessIndicators(<eval_result_dir>, step=1)
-    writer.close()
-    ```
-
-3.  Run TensorBoard
-
-    -   `tensorboard --logdir=<logdir>`
-    -   Select the new evaluation run using the drop-down on the left side of
-        the dashboard to visualize results.
diff --git a/docs/images/fairness-indicators.png b/docs/images/fairness-indicators.png
deleted file mode 100644
index bb9593e736..0000000000
Binary files a/docs/images/fairness-indicators.png and /dev/null differ