In this supplementary material we present more information about the dataset (including a datasheet for the dataset) and extensive results that could not fit in the main paper. In sec. 2 we include a datasheet for our dataset. In sec. 4 we look at the statistics of our two benchmarks CalFire and CaiRoad. The data is publicly available at https://www.cs.cornell.edu/projects/ Our code for accessing Sentinel-2 images, creating change events and baselines can be found at https://github.com/utkarshmall13/ We include a datasheet for our dataset following the methodology from "Datasheets for Datasets" [7]. In this section we include the prompts from [7] in blue and in black are our answers. Was there a specific task in mind? Was there a specific gap that needed to be filled? The dataset was created to foster research on the problem of automatic discovery and semantic understanding of change events in satellite imagery. More specifically, the dataset should aid in developing systems that can automatically detect change events in satellite imagery and assign to each a semantic label that indicates the nature of the event, e.g., forest fires, road construction etc. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number. The dataset contains RGB bands from Sentinel-2 satellite imagery. Users should keep in mind that changes smaller than the resolution be undetectable. For example, changes to roofs of houses, movements of traffic will not be detected. The datasets should be used for larger changes such as forest fire, crop changes etc. 2.2 Composition What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)?

The letter also raised concerns over a 2023 National Physical Laboratory study, which found the NeoFace system used by the Met was less accurate for women and people of colour depending on the algorithm that has been set. The study's authors found the system could show bias at lower thresholds, though at the higher settings the Met says it uses, performance was found to be equitable across ethnicity and gender. These thresholds are confidence levels the system uses to decide a match - lower ones flag more people but risk more mistakes and bias, while higher ones are stricter and more balanced. Campaigners said there was no legal obligation for the force to avoid the lower thresholds, and argued policing resources would be better spent on safety measures at the carnival. Deputy Assistant Commissioner Matt Ward, who is leading this year's policing operation at the carnival, said LFR had led to more than 1,000 arrests since the start of 2024 and that independent testing showed the system was "accurate and balanced with regard to ethnicity and gender" at the thresholds used by the Met.

Chemical information in scientific literature is commonly presented in various ways, such as text, tables, charts, and images.

Fairness has become an important topic in machine learning.

Code is released at https://github.com/yuelinan/DARE .

Social media platforms were conceived to act as online'town squares' where people