update URLs (#503)

CONTRIBUTING.rst

@@ -82,7 +82,7 @@ you wish to contribute to this, please reach out
 Website
 -------
 Any comments or suggestions regarding the
-`AIF360 homepage <https://aif360.mybluemix.net/>`_ may be posted to GitHub as
+`AIF360 homepage <https://aif360.res.ibm.com/>`_ may be posted to GitHub as
 well. This content may eventually be merged with the rest of the documentation.
 
 Example Notebooks

README.md

@@ -1,9 +1,9 @@
 # AI Fairness 360 (AIF360)
 
 [![Continuous Integration](https://github.com/Trusted-AI/AIF360/actions/workflows/ci.yml/badge.svg)](https://github.com/Trusted-AI/AIF360/actions/workflows/ci.yml)
-[![Documentation](https://readthedocs.org/projects/aif360/badge/?version=latest)](http://aif360.readthedocs.io/en/latest/?badge=latest)
+[![Documentation](https://readthedocs.org/projects/aif360/badge/?version=latest)](https://aif360.readthedocs.io/en/latest/?badge=latest)
 [![PyPI version](https://badge.fury.io/py/aif360.svg)](https://badge.fury.io/py/aif360)
-[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/aif360)](https://cran.r-project.org/package=aif360)
+[![CRAN\_Status\_Badge](https://www.r-pkg.org/badges/version/aif360)](https://cran.r-project.org/package=aif360)
 
 The AI Fairness 360 toolkit is an extensible open-source library containing techniques developed by the
 research community to help detect and mitigate bias in machine learning models throughout the AI application lifecycle. AI Fairness 360 package is available in both Python and R.
@@ -15,15 +15,15 @@ The AI Fairness 360 package includes
 It is designed to translate algorithmic research from the lab into the actual practice of domains as wide-ranging
 as finance, human capital management, healthcare, and education. We invite you to use it and improve it.
 
-The [AI Fairness 360 interactive experience](http://aif360.mybluemix.net/data)
+The [AI Fairness 360 interactive experience](https://aif360.res.ibm.com/data)
 provides a gentle introduction to the concepts and capabilities. The [tutorials
 and other notebooks](./examples) offer a deeper, data scientist-oriented
 introduction. The complete API is also available.
 
 Being a comprehensive set of capabilities, it may be confusing to figure out
 which metrics and algorithms are most appropriate for a given use case. To
 help, we have created some [guidance
-material](http://aif360.mybluemix.net/resources#guidance) that can be
+material](https://aif360.res.ibm.com/resources#guidance) that can be
 consulted.
 
 We have developed the package with extensibility in mind. This library is still

aif360/aif360-r/README.Rmd

@@ -17,7 +17,7 @@ library(aif360)
 # AI Fairness 360 (AIF360) R Package
 
 <!-- badges: start -->
-[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/aif360)](https://cran.r-project.org/package=aif360)
+[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/aif360)](https://cran.r-project.org/package=aif360)
 <!-- badges: end -->
 
 ## Overview
@@ -52,9 +52,9 @@ install_aif360()
 AIF360 is distributed as a Python package and so needs to be installed within a Python environment on your system. By default, the install_aif360() function attempts to install AIF360 within an isolated Python environment (“r-reticulate”).
 
 You can check using `reticulate::conda_python()` and `reticulate::py_config()`
- 
+
 ### Suggested steps
- 
+
  1)  Install reticulate  and check if you have miniconda installed. If you do, go to step 2.
 
  ```r
@@ -63,13 +63,13 @@ You can check using `reticulate::conda_python()` and `reticulate::py_config()`
  ```
  If you get an error: `Error: Unable to find conda binary. Is Anaconda installed?`, please install
  miniconda
- 
+
 ```r
 reticulate::install_miniconda()
 ```
-If everything worked, you should get the message: 
+If everything worked, you should get the message:
 
-`* Miniconda has been successfully installed at '/home/rstudio/.local/share/r-miniconda'.` 
+`* Miniconda has been successfully installed at '/home/rstudio/.local/share/r-miniconda'.`
 
 You can double check:
 
@@ -130,14 +130,14 @@ formatted_dataset <- aif360::binary_label_dataset(data_path = data,
 
 ```
 
-## Troubleshooting 
+## Troubleshooting
 
 If you encounter any errors during the installation process, look for your issue here and try the solutions.
 
 ### Locked binding
-If you get an error: `cannot change value of locked binding`, please restart the R session. Then try reactivating your Python environment and running the following commands exactly once: 
+If you get an error: `cannot change value of locked binding`, please restart the R session. Then try reactivating your Python environment and running the following commands exactly once:
 ```r
-library(aif360) 
+library(aif360)
 load_aif360_lib()
 ```
 

aif360/aif360-r/README.md

@@ -5,7 +5,7 @@
 
 <!-- badges: start -->
 
-[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/aif360)](https://cran.r-project.org/package=aif360)
+[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/aif360)](https://cran.r-project.org/package=aif360)
 <!-- badges: end -->
 
 ## Overview
@@ -141,7 +141,7 @@ restart the R session. Then try reactivating your Python environment and
 running the following commands exactly once:
 
 ``` r
-library(aif360) 
+library(aif360)
 load_aif360_lib()
 ```
 

aif360/sklearn/metrics/__init__.py

@@ -2,6 +2,6 @@
 ``aif360.sklearn`` implements a number of fairness metrics for group fairness
 and individual fairness. For guidance on which metric to use for a given
 application, see our
-`Guidance <http://aif360.mybluemix.net/resources#guidance>`_ page.
+`Guidance <https://aif360.res.ibm.com/resources#guidance>`_ page.
 """
 from aif360.sklearn.metrics.metrics import *

docs/source/Getting Started.rst

@@ -9,7 +9,7 @@ throughout the AI application lifecycle. This document will provide an overview
 its features and conventions for users of the toolkit.
 
 Definitions of relevant terms may be found in the
-`Glossary <https://aif360.mybluemix.net/resources#glossary>`_ page.
+`Glossary <https://aif360.res.ibm.com/resources#glossary>`_ page.
 
 Installation
 ============
@@ -86,7 +86,7 @@ above. This is ongoing work to reduce friction between the libraries.
 
 Algorithms are divided into three classes based on how they mitigate bias. For
 guidance on choosing an algorithm, see the
-`Resources <https://aif360.mybluemix.net/resources#guidance>`_ page.
+`Resources <https://aif360.res.ibm.com/resources#guidance>`_ page.
 
 Pre-processing
 ^^^^^^^^^^^^^^

examples/README.md

@@ -1,6 +1,6 @@
 # AI Fairness 360 Examples (Tutorials and Demos)
 
-This directory contains a diverse collection of jupyter notebooks that use [AI Fairness 360](http://aif360.mybluemix.net/) in various ways.
+This directory contains a diverse collection of jupyter notebooks that use [AI Fairness 360](https://github.com/Trusted-AI/AIF360/) in various ways.
 Both tutorials and demos illustrate working code using AIF360.  Tutorials provide additional discussion that walks
 the user through the various steps of the notebook.