Update README.md

README.md

@@ -7,7 +7,8 @@ language:
 tags:
 - traffic sign recognition
 - effect of background
-- background correlations
+- background correlation
+- XAI
 - synthetic
 - synset
 - OCTAS
@@ -30,6 +31,28 @@ Masoud Roschani, [Jens Ziehn](https://www.linkedin.com/in/jrziehn/), and Juergen
 **Funded by:** [Fraunhofer](https://www.fraunhofer.de/en.html) Internal Programs under Grant No. PREPARE 40-02702 within the ML4Safety project and the [German Federal Ministry for Economic Affairs and Climate Action](https://www.bundeswirtschaftsministerium.de/Navigation/EN/Home/home.html), within the program “New Vehicle and System Technologies” as part of the [AVEAS](https://aveas.org/) research project. <br>
 **License:** CC-BY 4.0 <br>
 
+## Description
+
+Common approaches to explainable AI (XAI) for deep learning (DL)-based image classification focus on analyzing the importance of input features on the classification task 
+in a given model: saliency methods like SHAP and GradCAM are used to measure the impact of spatial regions of the input image on the classification result. Combined with 
+ground truth information about the location of the object in the input image, for example, a binary mask, it is determined whether object pixels had a high impact on the 
+classification result, or whether the classification focused on background pixels. The former is considered to be a sign of a healthy classifier, whereas the latter is 
+assumed to suggest overfitting on spurious correlations.
+
+A major challenge, however, is that these intuitive interpretations are difficult to test quantitatively, and hence the output of such explanations lacks explanation 
+itself. One particular reason is that correlations in real-world data are difficult to avoid, and whether they are spurious or legitimate is debatable. To shed light on 
+this issue and test whether feature importance-based XAI reliably distinguishes between true learning and problematic overfitting, we utilize the task of traffic sign 
+recognition and systematically generated six synthetic datasets, which only differ in their degree of camera variation and background correlation. Thereby, a correlated 
+background means that each traffic sign is depicted in its most probable environment according to German traffic code / regulation StVO (Straßenverkehrs-Ordnung) 
+categorized in "urban", "nature", and "urban and nature". A traffic sign warning of wildlife crossing is, for example, likely to be set up on a rural road with natural 
+background, while a sign warning of pedestrians is probable to be placed in an urban context. An uncorrelated background, however, means that the background is randomly 
+chosen and thus not semantically linked to the depicted traffic sign class.
+
+For dataset generation, we utilized our parameterizable rendering pipeline from our work on the <em>Synset Signset Germany</em> dataset. The pipeline is based on the 
+Fraunhofer simulation platform [OCTAS](https://octas.org/). The dataset consists of six subdatasets: correlated and uncorrelated backgrounds cross the camera variation 
+stages frontal, medium and high. Each of these datasets contains 82 classes of traffic signs with 1,100 images per class, resulting in 90,200 images per dataset, summing 
+up to a total of 541,200 images. The images were rendered with the rasterization-based engine [OGRE3D](https://www.ogre3d.org/).
+
 ## Citation
 
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
@@ -50,6 +73,21 @@ Sielemann, A., Barner, V., Wolf, S., Roschani, M., Ziehn, J., and Beyerer, J. (2
 Measuring the Effect of Background on Classification and Feature Importance in Deep Learning for AV Perception. <br>
 In 2025 IEEE International Automated Vehicle Validation Conference (IAVVC).
 
+## Uses
+
+The dataset was designed for the investigation of the effect of background correlations on the classification performance and the spatial distribution of important 
+classification features within the task of traffic sign recognition.
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+
+The dataset should not be used for critical applications, particularly high-risk applications as named by the European AI Act under Annex III 
+(which includes "AI systems intended to be used for the ‘real-time’ and ‘post’ remote biometric identification of natural persons" and 
+"AI systems intended to be used as safety components in the management and operation of road traffic"), without exhaustive research into the fitness of the dataset, 
+to evaluate whether it is "relevant, sufficiently representative, and to the best extent possible free of errors and complete
+in view of the intended purpose of the system." No such claim is not made with the publication of this dataset.
+
 ## Bias, Risks, and Limitations
 
 ### Recommendations