bug-bounty438
google354
xss345
microsoft282
facebook246
apple172
exploit163
rce160
malware102
account-takeover95
cve91
csrf83
bragging-post80
writeup79
browser77
privilege-escalation68
react60
authentication-bypass57
cloudflare54
dos53
node52
ssrf51
docker51
phishing49
aws48
access-control47
smart-contract45
oauth45
supply-chain44
ethereum43
defi42
web342
sql-injection41
lfi37
idor34
smart-contract-vulnerability32
web-application31
race-condition31
reverse-engineering31
info-disclosure31
clickjacking31
wordpress30
vulnerability-disclosure30
cloud29
burp-suite28
information-disclosure28
solidity27
web-security27
ctf26
responsible-disclosure26
0
2/10
Western AI models fail in overseas agricultural contexts due to training bias toward European and U.S. data, lacking localization for crops, languages, connectivity constraints, and socioeconomic realities of the Global South. Organizations like NASA Harvest and Digital Green demonstrate that effective agricultural AI requires local data collection, model adaptation, vernacular language support, and farmer-centric design to avoid deepening inequalities.
ai-model-bias
machine-learning
computer-vision
agricultural-technology
data-localization
global-south
digital-colonialism
satellite-imagery
crop-classification
model-adaptation
food-security
deforestation-monitoring
generative-ai
language-models
Catherine Nakalembe
University of Maryland
NASA Harvest
Oren Ahoobim
Dalberg Advisors
Microsoft
Digital Green
FarmerChat
Rikin Gandhi
Farmers for Forests
Arti Dhar
Meta Detectron2
ChutkiAI
Google
Amazon
IBM
Alibaba
International Panel of Experts on Sustainable Food Systems