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new 53523a9 Update TrustyAI page to link to current website and GH org
(#80)
53523a9 is described below
commit 53523a9d458308249581714077b4da580ec0a16e
Author: Rob Geada <[email protected]>
AuthorDate: Mon Oct 7 15:39:42 2024 +0100
Update TrustyAI page to link to current website and GH org (#80)
* Update TrustyAI page to link to current website and GH org
* Remove link to TrustyAI in header
---
_includes/header-navigation.html | 3 --
trustyai.md | 64 ++--------------------------------------
2 files changed, 2 insertions(+), 65 deletions(-)
diff --git a/_includes/header-navigation.html b/_includes/header-navigation.html
index ee242af..5d4550e 100644
--- a/_includes/header-navigation.html
+++ b/_includes/header-navigation.html
@@ -27,9 +27,6 @@
<li>
<a
href="https://apache.github.io/incubator-kie-kogito-docs/serverlessworkflow/latest/";
target="_blank">Serverless Workflow</a>
</li>
- <li>
- <a href="{{site.baseurl}}/trustyai/" class="{% if page.url
contains '/trustyai/' %}active{% endif %}">Trusty AI</a>
- </li>
<li>
<a class="button-cta" href="{{site.github_fork_url}}"
target="_blank">GitHub</a>
</li>
diff --git a/trustyai.md b/trustyai.md
index fd6952f..19fd8ee 100644
--- a/trustyai.md
+++ b/trustyai.md
@@ -7,65 +7,5 @@ permalink: /trustyai/
# TrustyAI Initiative
<br/>
-The TrustyAI initiative aims to offer value-added services to the Kogito
ecosystem to make it a trustworthy Business Automation solution. The project
currently focuses on the open DMN standard and introspects the decision-making
process using the following aspects:
-
-- **Explainability**: Enrich model execution information through XAI algorithms
-- **Tracing and Accountability**: Extract, collect, and publish metadata for
auditing and compliance
-- **Runtime Monitoring**: Expose services in dashboards to assess data from
both a business and an operational perspective
-
-## Value-added services
-
-### Explanability
-TrustyAI aims to help explain black-box machine learning models by using XAI
techniques.
-Black-box models are not transparent about their processing, and the user only
knows the inputs and outcomes of the system.
-An example of this is a neural network: These models are accurate for
predictions, but their complexity can make them hard to interpret.
-
-XAI techniques are used within TrustyAI to introspect these black-box models
to describe predictions and outcomes.
-This is done by looking at a feature importance chart. This is where the
model’s inputs are ordered by the most important ones for the decision-making
process.
-This can help to capture if a model is biased. Model bias can damage a
company’s reputation.
-For example, if you have a hiring machine learning system and the most
important feature they looked at was gender, this would show a bias.
-TrustyAI reduces the chance of this by showing the important features used in
the machine learning model to reduce business risk.
-
-### Tracing and Accountability
-The Audit UI is a dashboard that targets business users or auditors, where
each transaction is stored and can be analysed.
-For each transaction, it is possible to access the user inputs, the outcomes
produced, and the explanation of each of them.
-
-The Audit UI traces every decision made and helps to create accountability of
the system.
-Accountability is guaranteed by the caseworker who can access the outcomes
from the decision.
-This means they can ensure that the outcomes of the system meet the
requirements of the company.
-
-### Runtime Monitoring
-Runtime monitoring allows for both business and operational metrics to be
displayed in a Grafana dashboard configurable by the user.
-Business monitoring dashboards are generated based on model information (DMN
or machine learning models) so that users can monitor business aspects and have
an aggregated view of decision behaviors.
-Finally, operational aspects can be monitored to keep track of the health of
the services.
-
-
-## Contributing
-
-The source code is hosted on GitHub as part of Kogito repositories. If you
need to report a bug or request a new feature, look for a similar one on our
[Jira issue tracker](https://issues.jboss.org/projects/KOGITO). If you don’t
find any, create a new issue.
-
-For suggested bug fixes or improvements specific to the Explainability aspect,
you can also use the [research FAI Jira
project](https://issues.jboss.org/projects/FAI).
-
-### Chat
-
-Join our community chat by joining the
[#trusty-ai](https://kie.zulipchat.com/#narrow/stream/232681-trusty-ai) channel
on [https://kie.zulipchat.com/](https://kie.zulipchat.com/).
-
-## Resources
-
-All TrustyAI services are integrated with the Kogito platform and documented
as part of the [Kogito
documentation](https://docs.kogito.kie.org/latest/html_single/).
-
-### Videos
-
-- [Explanation by Example: the OptaPlanner way](https://youtu.be/4H3U6xyCgMI)
(OptaPlanner Week) - Daniele Zonca, Rui Vieira and Tommaso Teofili
-- [Can you trust your AI?](https://youtu.be/HdEwp2RhG7w) (KIE Live 13) -
Daniele Zonca
-- [How to integrate your Kogito application with TrustyAI - Part
1](https://youtu.be/exbOCrq8gJE) - Jacopo Rota
-- [How to integrate your Kogito application with TrustyAI - Part
2](https://youtu.be/DtY5aWSYWfU) - Jacopo Rota
-- [How to integrate your Kogito application with TrustyAI - Part
3](https://youtu.be/8xoqDerWY2s) - Jacopo Rota
-
-### Blog
-
-You can find more details about what is going on in the [TrustyAI
blogposts](https://blog.kie.org/category/all?s=trusty+trustyai) on the [KIE
blog](https://blog.kie.org/).
-
-### Papers
-
-A detailed analysis of TrustyAI's explainability techniques is available in
the [TrustyAI Explainability Toolkit](https://arxiv.org/abs/2104.12717)
pre-print.
+The TrustyAI project is now independent of Kogito:
+please see their [current website](https://trustyai-explainability.github.io/)
and [GitHub organization](https://github.com/trustyai-explainability) for more
information on the project.
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