Lioness

#Data4wildlife challenge

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#DATA4WILDLIFE challenge

Background:

Historically, the top three factors driving the global biodiversity extinction crisis have been habitat loss, human-wildlife conflict, and poaching. Over about the past decade, a new factor has propelled into the spotlight: online crime. Online wildlife crime causes species declines. The algorithmic amplification of wildlife crime via social media platforms, mobile applications and high-speed broadband access is a grave concern for nongovernmental organizations working to combat online crime.  Join us for the #DATA4WILDLIFE challenge and help slow species declines!

When:

The #DATA4WILDLIFE challenge will take place on 29/30 January 2022. Applications are now open and will close at 22.00 on 12 January 2021. Teams will be announced on 17 January 2022 and drop in mentorship sessions will be held during that week to prepare teams for the two day challenge on 29/30 January 2022. 

The winning teams from the hackathon will be announced on 7 February 2022 and will receive a number of prizes/mentorship opportunities from innovation strategists, operations engineers and global wildlife crime leaders. 

The #DATA4WILDLIFE challenge is powered by Bright Tide and funded by the National Science Foundation-funded planning grant (Division of Information & Intelligent Systems Award IIS-2039951) which is a new collaboration between University of MarylandWorcester Polytechnic Institute and Focused Conservation Solutions. The challenge is also supported by the Conservation Criminology Institute led by Dr. Meredith Gore. To learn more about the research collaboration can be found at Disrupting Illicit Supply Networks.

The #DATA4WILDLIFE challenge is committed to diversity, equity and inclusion (DEI) throughout our processes and ensuring that DEI it is embedded into everything we do. 

What are the 3 challenges:

 

Challenge Question #1 (Data for Deterrence): Social media platforms are one online location where wildlife products can be illegally bought, sold, advertised and marketed using images, text, and combinations of images/text. Right now, data scientists can’t use their tools to create new information about online wildlife crime, such as seeing new trend lines, drawing inference, or identifying counterintuitive correlations.  Challenge #1 will create a benchmark dataset of online wildlife crime images, text, and emojis on three social media platforms (Instagram, TikTok, SnapChat). This database can be used by analysts  and authorities to differentiate online wildlife crime from unproblematic depictions of wildlife.

 

Challenge Question #2 (Random Acts of Trade): Wildlife crime involves moving an animal or animal product from source to destination via a transit location. This illegal supply chain can cross both physical and virtual spaces. Right now, engineers can’t use their tools to create new information about the illegal supply chain structure or process. Challenge #2 will create a virtual and spatialized supply chain for a specific animal or animal product and identify the places and times where virtual and physical spaces touch. Understanding the intersections of physical and virtual supply chains will allow different organizations to track supply patterns and changes in supply chain patterns.


Challenge Question #3 (Media for the Masses): The media remains one of the most important witnesses of wildlife crime. Stories in the media tell us about animals and animal products, wildlife crime offenders and defenders, lawyers and judges, and how the wildlife crime happened. Right now, data scientists can’t use their tools to create new information about online wildlife crime, such as seeing new trend lines, drawing inference, or identifying counterintuitive correlations. Challenge #3 will create a wildlife crime media aggregator in multiple languages as well as a database of aggregated media that can be used to deter online wildlife crime.

Applications close at 22.00 on 12 January 2022

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