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In 2021, hosted the first-ever awards!! HackerNoon Startup of the Year The goal of this campaign was to give recognition to the hardworking startups who pulled through the pandemic -- growing and thriving (despite all of the challenges). More than 44,000 startups were nominated all across the globe! They were selected from over 4,553 cities, with a total number of * cough * “4 billion” votes. Insane, right? To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Compromised awards are under review. Cities with tie votes will be updated to have two winners!) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement series Here are the winners in the : Asia region Top Startup Cities in East Asia Seoul The Startup of the Year in is with 377 votes! Seoul dable Central is the Startup of the Year! It came out ahead with 121 votes. UpNow Central Sheung Wan came out as the Startup of the Year in with 154 votes. Votee Sheung Wan Tokyo The Startup of the Year in is with 41 votes! Tokyo ACES Wan Chai The Startup of the Year in is with 54 votes! Wan Chai Zetl Osaka, Japan In , is the winner! Osaka Nanophoton Taiwan came out as the Startup of the Year in ! Tico Taiwan Dongcheng, China In , is declared the Startup of the Year! Dongcheng Waiju Taipei came out as the Startup of the Year in ! Tico Taipei Akasaka In , is the winner! Akasaka WAKUWAKU Chuo In , is declared the Startup of the Year! Chuo GO-NET Ginza came out as the Startup of the Year in ! Netyear Ginza Kanazawa In , is the winner! Kanazawa Granzella Minami-aoyama In , is declared the Startup of the Year! Minami-aoyama Tokyo6 Entertainment Shibuya came out as the Startup of the Year in ! Hupro Japan Shibuya Toranomon In , is the winner! Toranomon Small Worlds Toshima In , is declared the Startup of the Year! Toshima Altplus Guangzhou, China came out as the Startup of the Year in ! Leyaoyao Guangzhou Hong Kong, Hong Kong In , is declared the Startup of the Year! Hong Kong Episode Six Kunming In , is the winner! Kunming Green A Sapporo In , is declared the Startup of the Year! Sapporo Polar Star Space Shirokanedai came out as the Startup of the Year in ! Nupp1 Shirokanedai Wuhan In , is the winner! Wuhan CSICEE Baoan, China In , is declared the Startup of the Year! Baoan Jumper Baoshan, China In , is declared the Startup of the Year! Baoshan Renren Games Chengdu, China came out as the Startup of the Year in ! Ju You Xi Chengdu Fujisawa, Japan In , is the winner! Fujisawa Scohia Pharma Fukuoka, Japan In , is declared the Startup of the Year! Fukuoka Kyulux Haidian, China came out as the Startup of the Year in ! 51vv Haidian Hongkou, China In , is the winner! Hongkou Kidstone Jiading, China In , is declared the Startup of the Year! Jiading IMRobotic Kawasaki, Japan came out as the Startup of the Year in ! QD Laser, Inc. Kawasaki Kyoto, Japan In , is declared the Startup of the Year! Kyoto Hacarus Minato, Japan In , is the winner! Minato Visionary Fun Nagoya, Japan came out as the Startup of the Year in ! Prodrone Nagoya Ningbo, China The winner in is . Ningbo OlymTech Corporation Aomori In , is declared the Startup of the Year! Aomori Forte Japan Bandai-cho In , is the winner. Bandai-cho Delta-Fly Pharm Changchun In , the Startup of the Year is . Changchun Kaidi Animation Fuchu In , is declared the Startup of the Year! Fuchu TMS Fukuoka-shi The winner in is . Fukuoka-shi SkyDisc Fukushima In , the Startup of the Year is . Fukushima Matsuri Technologies Gotanda In , is the winner. Gotanda Basset Hachioji In , is declared the Startup of the Year! Hachioji Adriakaim Harbin The winner in is J . Harbin oining Technology Iwaki In , is the winner. Iwaki Tao Engineering Izumo In , the Startup of the Year is . Izumo PuREC Kagoshima In , is declared the Startup of the Year! Kagoshima Farmers Support Kamakura In , the Startup of the Year is . Kamakura KAYAC Kami-meguro The winner in is . Kami-meguro Buzzreach Kanagawa In , is the winner. Kanagawa LoiLo Kashiwa In , the Startup of the Year is . Kashiwa Intelligent Surfaces Kita In , is declared the Startup of the Year! Kita Ax Robotix Kobe The winner in is . Kobe Coconomi Koriyama In , the Startup of the Year is . Koriyama Tocoton Kumamoto In , is the winner. Kumamoto Bloom Technology Kunitachi In , is declared the Startup of the Year! Kunitachi Trys Liaocheng The winner in is . Liaocheng Jinzheng Animation Minami In , is declared the Startup of the Year! Minami ATLAB Miyazaki In , is the winner. Miyazaki Caster Nagano In , is declared the Startup of the Year! Nagano Precision Forestry Nagaoka The winner in is . Nagaoka Plant Form Nihombashi-hamacho In , the Startup of the Year is . Nihombashi-hamacho Shizen Energy Niigata In , is declared the Startup of the Year! Niigata UniBio Japan Nishi-shinagawa In , is the winner. Nishi-shinagawa Play Heart Okayama In , is the winner! Okayama Arch Farm Okinawa In , is declared the Startup of the Year! Okinawa BlogNinja Otemachi In , the Startup of the Year is . Otemachi beBit, Inc Qingdao Shi In , is the winner! Qingdao Shi Manyoubang Quanzhou In , is declared the Startup of the Year! Quanzhou KungFu Animation Group Roppongi The winner in is . Roppongi Holo-X Saitama In , the Startup of the Year is . Saitama GameTector Sendai In , is the winner! Sendai Sendai Smart Machines Setagaya In , is declared the Startup of the Year! Setagaya Skyestate Shenzhen In , the Startup of the Year is . Shenzhen Aiator Shinjuku In , is declared the Startup of the Year! Shinjuku Scalar Shizuoka In , is the winner! Shizuoka PiPhotonics Sumida The winner in is . Sumida Holotch Tachikawa In , the Startup of the Year is . Tachikawa PlusOne Takamatsu In , is declared the Startup of the Year! Takamatsu Ken Robotec Tokushima In , is the winner! Tokushima DennoKotsu Toyonaka In , the Startup of the Year is . Toyonaka FISH BIOTECH Tsukuba In , is declared the Startup of the Year! Tsukuba WARPSPACE Wakayama In , the Startup of the Year is . Wakayama 4D Sensor Xiangzhou In , is the winner! Xiangzhou ALLiN Edutech Group Xianyang The winner in is . Xianyang TONLY Xiaoshan In , is declared the Startup of the Year! Xiaoshan GONGHUI Yamaguchi In , the Startup of the Year is . Yamaguchi Archis Yangpu The winner in is . Yangpu Tmxmall Yau Tsim Mong In , is the winner! Yau Tsim Mong Airnugget Yeoksamdong In , the Startup of the Year is . Yeoksamdong WizSchool Yichang In , is declared the Startup of the Year! Yichang Hubei Kento Electronic Yokohama In , is the winner! Yokohama Next Innovation Yubei In , the Startup of the Year is . Yubei Ophiux Network Yueqing The winner in is . Yueqing Yueqing Easythink Media Zhuhai In , is the winner! Zhuhai Aijuba Hongo In , the Startup of the Year is . Hongo Smile Robotics, Inc Admiralty In , is declared the Startup of the Year! Admiralty BUNCH Southern In , the Startup of the Year is . Southern Retykle Yongdungpo In , is the winner! Yongdungpo Push News Taichung In , is declared the Startup of the Year! Taichung Wondercise Cyberport, Hong Kong In , the Startup of the Year is . Cyberport Animoca Brands Hefei, China In , is the winner! Hefei Tsinglink New Territories, Hong Kong The winner in is . New Territories, Hong Kong Zeek Dezhou In , the Startup of the Year is . Dezhou Dezhou Baji Kitakyushu In , is declared the Startup of the Year! Kitakyushu Reif Seongnam In , is the winner! Seongnam EOFlow Yanshi In , the Startup of the Year is . Yanshi CASI Vision Top Startup Cities in South Asia Hyderabad, India The Startup of the Year in is with 962 votes Hyderabad Quixy Lahore, Pakistan is the Startup of the Year! It came out ahead with 186 votes. SmartWindows Lahore Bangalore, India When it comes to , is the Startup of the Year with 210 votes! Bangalore vBridge Hub Pune, India couldn’t be beaten for Startup of the Year in , it won with 129 votes! InfraCloud Pune Jaipur, India The Startup of the Year in is with 166 votes Jaipur Emizen Tech Lucknow, India is the Startup of the Year! It came out ahead with 279 votes. Keeros Superfoods Lucknow Noida, India came out as the Startup of the Year in with 156 votes! Bagisto Noida Mumbai, India When it comes to , is the Startup of the Year with 96 votes! Mumbai Sequretek Sylhet couldn’t be beaten for Startup of the Year in , it won with 122 votes! DataXpie Sylhet Mylapore, India In , is the winner! Mylapore Yalimart Goa, India In , the Startup of the Year is Goa Filter Coffee Delhi, India In , is declared the Startup of the Year! Delhi EkAnek Networks Karachi, Pakistan The winner in is Karachi Bykea Ahmedabad, India In , is the winner Ahmedabad MageComp Dhaka, Bangladesh The winner in is Dhaka SOLshare Islamabad, Pakistan In Islamabad, is declared the Startup of the Year! TAG Gurgaon, India In , the Startup of the Year is Gurgaon ixigo Bengaluru, India In , is the winner! Bengaluru Prakshep Private Limited Haryana, India couldn’t be beaten for Startup of the Year in ! Footprints Childcare Haryana Kolkata, India In , is the winner! Kolkata Wow! Momo New Delhi, India In , is declared the Startup of the Year! New Delhi Rusk Media Indore, India The winner in is Indore Chattar Patar Kormangala In , is the winner Kormangala Sanctum Technologies Ghaziabad came out as the Startup of the Year in Craftfurnish.com Ghaziabad! Mohali In , the Startup of the Year is Mohali DataKund Mysore The Startup of the Year in is Mysore Excelsoft Karnal came out as the Startup of the Year in ! Dunar Karnal Kolhapur In , is the winner! Kolhapur Zanvar Group Patiya In , the Startup of the Year is Patiya Kloud Project Kathmandu In , is declared the Startup of the Year! Kathmandu Vairav Technology Bangalore City, India In is the winner! Bangalore City, PagarBook Ernakulam The winner in is Ernakulam Epixel Solutions Colombo, Sri Lanka couldn’t be beaten for Startup in the Year in MAs Foods Colombo Andheri, India When it comes to , is the Startup of the Year! Andheri DoorMint Kanpur, India came out as the Startup of the Year in ! ServiceWalaa Kanpur Faisalabad In , is the winner! Faisalabad DigiKhata Nasik The Startup of the Year in is Nasik Sahyadri Farms Patna came out as the Startup of the Year in ! Asvin Tech Patna Chennai, India In , is the winner! Chennai Agnikul Cosmos Faridabad, India In , is declared the Startup of the Year! Faridabad UClean Panjim, India The winner in is Panjim TempoGO Ranchi, India In , is the winner Ranchi Osam Dairy Surat, India In , is declared the Startup of the Year! Surat Wishbook Thane, India In , is the winner! Thane Delight Foods Trivandrum, India came out as the Startup of the Year in ! GenRobotic Trivandrum Vijayawada, India In , the Startup of the Year is Vijayawada Eruvaka Technologies Visakhapatnam, India In , Ltd is declared the Startup of the Year! Visakhapatnam Greenline Labs Pvt Ahmadabad In , is the winner! Ahmadabad KiwiQA Services Bandra came out as the Startup of the Year in ! Car Club Bandra Bombay In , is the winner! Bombay Locobuddy Mobile Technology (Sportobuddy) Cochin In , the Startup of the Year is Cochin Tuttifrutti Interactive Gandhinagar In , is declared the Startup of the Year! Gandhinagar Kroop AI Guwahati In , the Startup of the Year is Guwahati GNRC Hospitals Jamshedpur In , is declared the Startup of the Year! Jamshedpur Intelliber Kabul The winner in is Kabul Miracle Shop (Vocinno Technology) Karur In , is the winner Karur Karur Vysya Bank Kochi The winner in is Kochi BestDoc Kozhikode In , is declared the Startup of the Year! Kozhikode OfficeKit HR Ludhiana In , the Startup of the Year is Ludhiana Trendy Beat Network Madhapur In , is the winner! Madhapur Head Digital Works Mangalore When it comes to , is the Startup of the Year Mangalore Hangyo Nagpur came out as the Startup of the Year in ! UPrudence Agro Nagpur Rawalpindi The Startup of the Year in is Rawalpindi Pro Options Marketing Rishikesh came out as the Startup of the Year in ! Descatuk Rishikesh Secunderabad couldn’t be beaten for Startup of the Year in Prakriti Biosystems Engineering Secunderabad Tirupur In , is the winner! Tirupur Tubeknit Fashions Udupi The winner in is Udupi Krishitantra Top Startup Cities in Southeast Asia Singapore, Singapore The Startup of the Year in is with 6556 votes! Singapore Cake DeFi Hanoi, Vietnam is the Startup of the Year! It came out ahead with 683 votes. DeFi For You Hanoi Kuala Lumpur, Malaysia came out as the Startup of the Year in with 109 votes! Trambellir Kuala Lumpur Pasig, Philippines When it comes to , . is the Startup of the Year with 110 votes! Pasig Quickreach Inc Bangkok, Thailand couldn’t be beaten for Startup in the Year in , it won with 60 votes! Perception Codes Bangkok Johor Bahru In , the Startup of the Year is . Johor Bahru AGR1 Bandung, Indonesia In , is declared the Startup of the Year! Bandung Prosa Portugal The winner in is . Portugal SCEMAI Tangerang, Indonesia In , is the winner Tangerang Yummy Corp Jakarta, Indonesia In , is the winner! Jakarta Tokocrypto Dki Jakarta, Indonesia In , is declared the Startup of the Year! Dki Jakarta Limakilo Bangkok Metropolis In , is the winner! Bangkok Meticuly Medan The winner in is ! Medan DIKA Manila, Philippines In , is declared the Startup of the Year! Manila PawnHero Phnom Penh, Cambodia In , the Startup of the Year is Phnom Penh vKirirom Bali In , is the winner! Bali Radius App Penang When it comes to , is the Startup of the Year Penang EasyParcel Surabaya came out as the Startup of the Year in ! Super Surabaya Setiabudi, Indonesia The Startup of the Year in is Setiabudi Chilibeli Cebu City came out as the Startup of the Year in MiCab Cebu City! Jawa couldn’t be beaten for Startup in the Year in DAUR Jawa Ho Chi Minh City, Vietnam In , is the winner! Ho Chi Minh City Pharmacity Selangor, Malaysia is the Startup of the Year! BabyDash Selangor Jakarta Pusat, Indonesia In , is the winner! Jakarta Pusat StickEarn Semarang When it comes to , is the Startup of the Year! Semarang Food Bear Petaling Jaya, Malaysia The winner in is . Petaling Jaya Deemples Kebayoran Baru, Indonesia couldn’t be beaten for Startup in the Year in ! Yogrt Kebayoran Baru Kuningan The winner in is . Kuningan Prinzio Bukit Merah came out as the Startup of the Year in ! DC Alliance Bukit Merah Huai Khwang In , the Startup of the Year is . Huai Khwang AppMan Co.,Ltd Kuta In , is declared the Startup of the Year! Kuta Gringgo Mandaluyong came out ahead as the Startup of the Year! Work Inspire Mandaluyong Midview City In , is the winner! Midview City BioMers Phuket The Startup of the Year in is ! Phuket Sapahn Yangon, Myanmar The winner in is . Yangon Wave Money Denpasar When it comes to , is the Startup of the Year! Denpasar Revasi Nakhon Ratchasima, Thailand The winner in is . Nakhon Ratchasima Siam Lottery co., Ltd Taguig, Philippines In , is the winner! Taguig PayMongo Bogor came out as the Startup of the Year in ! TIRANYX Bogor Senai, Malaysia won as the Startup of the Year. Nutrition Technologies Senai Bekasi Kota couldn’t be beaten for Startup in the Year in ! 8villages Bekasi Kota Top Startup Cities in Middle East Tel Aviv, Israel The Startup of the Year in is with 1,065 votes! Tel Aviv hyro Beirut, Lebanon is the Startup of the Year! It came out ahead with 1,276 votes. Hovi Digital Lab Beirut Riyadh, Saudi Arabia With 350 votes, came out as the Startup of the Year in ! Flicron Riyadh Tel Aviv-yafo, Israel When it comes to , is the Startup of the Year with 167 votes! Tel Aviv-yafo Cymbio Haifa, Israel couldn’t be beaten for Startup in the Year in , it won with 183 votes! Pico Haifa Amman, Jordan In , with 141 votes, is the winner! Amman Edexy Dubai, United Arab Emirates In , the Startup of the Year is with 74 votes. Dubai Ziina Doha, Qatar In , is declared the Startup of the Year! Doha SkipCash Tehran, Iran The winner in is . BeyondBoard AR Tehran Jerusalem, Israel In , is the winner! Jerusalem SolidBlock Rishon Le Zion The Startup of the Year in is ! Rishon Le Zion Passparto Ness Ziona, Israel is the Startup of the Year! Soapy Ness Ziona Kuwait, Kuwait came out as the Startup of the Year in ! Brapper Shop Kuwait Manama, Bahrain couldn’t be beaten for Startup in the Year in ! Doctori Manama Lod, Israel is the winner in ! Phinergy Lod Agudat Israel, Israel When it comes to is the Startup of the Year with [vote ] votes! Agudat Israel, Aqaurius Engines Beersheba, Israel In , the Startup of the Year is . Beersheba Visual Estate Ltd Giv'atayim, Israel In , is declared the Startup of the Year! Giv'atayim WSC Sports Modi'in, Israel The winner in is . Modi'in Loginno Nazareth, Israel In , is the winner! Nazareth AR Guide In Medical Ramat Gan, Israel The Startup of the Year in is . Ramat Gan Datos Health Baghdad When it comes to , is the Startup of the Year! Baghdad Mishwar Khobar In , the Startup of the Year is . Khobar Garage Misgav couldn’t be beaten for Startup in the Year in ! Eden Shield Misgav Sharjah came out as the Startup of the Year in ! Bookends Sharjah Affula In , the Startup of the Year is . Affula eMotion Technologies Bene Beraq, Israel In , is the winner Bene Beraq Reflectiz Caesarea, Israel In , the Startup of the Year is . Caesarea CardiacSense Dubayy In , is declared the Startup of the Year! Dubayy North Ladder Muscat couldn’t be beaten for Startup in the Year in ! iLab Marine Muscat Tirat Carmel In , the Startup of the Year is . Tirat Carmel PassCall Advanced Technologies Hod Hasharon, Israel The winner in is . Hod Hasharon Idomoo Holon, Israel came out ahead as the Startup of the Year! OptimalPlus Holon Jeddah, Saudi Arabia When it comes to , is the Startup of the Year! Jeddah Arabian Combustion Technologies Ramat Hasharon, Israel is the Startup of the Year! Lumen Ramat Hasharon Al Khobar came out as the Startup of the Year in ! Swyft Al Khobar Bet Shemesh In , is the winner! Bet Shemesh Degania Medical Binyamina When it comes to , is the Startup of the Year! Binyamina OncoHost Makkah In , the Startup of the Year is Makkah Scimagine Or Akiva Last but not least, couldn’t be beaten for Startup in the Year in the city of ! WMT Or Akiva More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network The post was written by Kushal Tamrakar and Valerie Ejegi, edited by Hang Ngo. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

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Southeast Turkey and Syria were left devastated after a series of earthquakes hit the region. The first quake, of magnitude 7.8, hit the city of Gaziantep at 4 AM in the morning on February 6th. What followed was a disaster. At the time of this writing, we have lost more than 40,000 people. Over 80,000 are injured. Many are still under the rubble. , the community directly affected by this disaster is around 13 million people, or 1-2 out of every 10 people in Turkey and Syria. The impacted area amounts to the surface area of Austria, two times that of Denmark, and three that of Belgium. Per estimations https://twitter.com/arkitektcom/status/1623692510894739456?embedable=true To navigate these times, I found myself thinking and reading a lot about better ways for collective action for crisis response. In many cases, for various reasons, the largest governing body is not able to coordinate as needed. Below is a personal account of local crisis response, along with a curation of the wonderful voluntary work that emerged after this earthquake and some more ideas for humanity’s repetitive resilience in the face of disasters. A personal account of the humanitarian crisis, from afar Turkey is an earthquake country. The seismic activities stem from the collisions between the Eurasian, African, and Arabian plates. The latest generation residing in the Marmara region, including myself, vividly remember the that took about 20,000 lives. My grandma had lost her younger sister and mother in the , surviving for 3-4 days under the debris. Everyone has memories of some earthquake in their family history. 1999 Izmit earthquake 1939 Erzincan earthquake With the , many residential buildings, including municipality buildings, airports, and hospitals, were among those demolished. The earthquake happened around 4 AM, and photos with daylight showed buildings still in shambles with no teams or machinery around. 2023 Turkey-Syria earthquake https://twitter.com/timursoykan/status/1622556005094633473?embedable=true Waking up that morning, everyone I knew, including myself, was  frantically publishing emergency messages, requesting excavators to rescue our friends’ families on social media, hoping that some other contact in the region might see the message and help out. Where was the machinery? Where were the teams? So many national and international medical search and rescue teams were on their way to the region, yet, many couldn’t even enter the cities because of the traffic jam. Some others reported hours of waiting as officials were trying to assign them to a region. Medical teams were extremely short of supplies. Like everything wasn’t enough, there was news of a big fire, too. https://twitter.com/DikenComTr/status/1622854339701112835?embedable=true Footage of quake survivors in despair, demanding action to save their loved ones roamed the Internet. There was news of people being called to police centers because they were harshly criticizing the government for the lack of timely support. In their defense, some government officials claimed they were spreading disinformation to further elevate the panic environment. Some of the arrest orders were for people who had just lost their families in the quake. Using the Internet and Social Media as a Resource People safe from the earthquake were sad, angry, frustrated, and not letting this hinder them in their efforts to pack and ship first-response supplies. https://twitter.com/basyazar/status/1623585909298937857?embedable=true Volunteers started that marketed their cheap, poorly-engineered buildings as otherwise. Others built websites like and ; set up to discuss what else can be done; . They touched upon many things from recommendations to spread the word to psychiatrists associations’ guides on keeping calm. For international teams, there are . Larger, more organized volunteer organizations like , , or did not cease their call for more financial support. making lists of the construction firms and their owners afetbilgi.com deprem.io communities on Discord put resources together disaster management dictionaries Ahbap Akut Research Institute on Turkey Turkish Philanthropy Funds All of a sudden, Twitter got banned. The main tool with which people ask for help and coordinate resources. Luckily, the ban did not last for long after widespread protests. Around this time, people noticed how some correspondents of the major media outlets turned the microphone away and shut down people the moment they started talking about how their families were still under the rubble. https://twitter.com/buseyyyyk/status/1623213471897735168?s=20&t=j1HtCO0nt1G6ckOfaD7lCQ&embedable=true Soon after, came news of violence -  violence toward Syrian immigrants, toward “predators”; photos of people with enormous firearms. https://twitter.com/neseidil/status/1624702981231747072?embedable=true Last but not least, a continuous flow of crippling news, also of living beings trying to save others. https://twitter.com/SEDENAmx/status/1624673070773415936?embedable=true Dissecting the humanitarian crisis: Disinformation, untimeliness and under-preparation I don’t want this piece to be a political account. I haven’t personally verified all the footage I shared above. : , wrong information; or , information that is wrong on purpose, roams the Internet every day. This is problem #1 Misinformation disinformation Their tide is higher whenever there’s a crisis. Luckily, there are platforms based out of Turkey, like or , that help with fact-checking. Still, they can’t always impact some real-time spread. teyit.org dogrulukpayi.com https://twitter.com/PhilosophyMttrs/status/1624747667162112001?s=20&embedable=true is that the people in need don’t always get the they deserve when they need them. The service range is wide, from being brought back to life by a strong pull out of the debris, to an emergency amputation, a bowl of hot soup, or a place to stay. The necessary coordination is complex, with many resources that need to be stocked in relevant hubs beforehand and successful ways for efficient dispatching algorithms. Problem #2 services is that of , both individually and collectively on multiple layers of scale. A classic at play with its different flavors. After all, no one can really tell with a few glances whether a building is safe, and any construction company is better off financially when they build 5 houses in parallel instead of properly managing the project of a single one, cutting off from materials. The third problem under-preparation problem of collective action https://twitter.com/JudithGeology/status/1625118307706716164?s=20&t=xx-kcYBA9y42AC2sd7VH4Q&embedable=true Challenges ahead Now, two weeks after the first wave of the quake, it’s a good time to assess the situation and plan ahead. First and foremost, the quake survivors have lost so much. For their short-term stay, there need to be structures set up before they are dispatched to longer-term stays, which need to be facilitated and eased with adequate law-making. Education and health facilities need to be set up or adapted. As we try to pick up the pieces of what was once our cities, our homes, this elongated phase of recovery also desperately needs civilian support. This very tragic instance and so many other types of crises are prone to cross our way. Just another example, from the top of my head, are summer after summer, or the . We, humans of the world, need ways that help us act faster despite all the bureaucracies, inefficiencies, and stupidities that stand in the way of survival for others. the wildfires that terrify the Mediterranean 2021 European floods Last but not least, we need to make sure that we’re individually and collectively ready to act as efficiently as possible in the face of another disaster. Mind you fellow Istanbulites, scientists have been warning us about the upcoming Istanbul earthquake for at least a decade now. Still, many ignore this unpleasant news, ignoring the necessity to start preparing both on an individual and collective level. We need a stronger civil society worldwide Individual preparation will get us far, but only to a certain extent. Like many other big endeavors, we need to act with peer community members to make sure our impact is larger. Let’s look at some options on how this larger impact could take shape. Stay Informed First and foremost, we need to make sure we’re properly informed of any disasters that are prone to happen, what preventive mechanisms there are, and whether it was properly executed by those in power. Also, we need to be informed of the latest biggest humanitarian crises, like the , which has displaced millions of people, or the . Take the time to research all the events, trends, and decision-making that led to the movement. Syrian Civil War 2022 Russian invasion of Ukraine Black Lives Matter Provide Support Where You Can Most likely, those crises have affected where you live. If you’re lucky enough not to find yourself amidst an overflowing amount of causes you need to help out with, just start with actors providing support and solidarity to victims of these calamities. shows how civil society has weakened in the last decade, and along with it, democracies worldwide are weakened, too. Civil organizations are generally severely underfunded. Cross-border donations are usually not persistent enough, making it hard to speak of a globally open civic space. Perhaps what It takes is as small a step as dedicating attention to global issues to strengthen civil society worldwide. Listing their advice and solution suggestions to strengthen civil society, a local organization, Civil Pages, the following: Research proposes Internalization of democratic culture [in global civil society]. Strengthening the institutional structures [of civil organizations] with professional support. Utilizing scientific methods of data collection and evaluation, employing qualified human resources. Reproduction of financial resources; efficient and transparent use of existing resources. Keeping the channels of dialogue open and interacting with the society, the state, the ‘other,’ and other organizations, establishing mutual trust. Abandoning identity politics, acting like a political party and vertical hierarchy. Establishing an equal-base relationship with the government, avoiding the hegemony of power. Protection of independence against political, economic, and cultural power centers. Making maximum use of new opportunities brought by technology and digital media. We can and should demand better from our governments Managing disasters is perhaps one of the hardest duties of any government. Without meddling in any politics, favoritism, or ideology, we can and should analyze how governments acted post-crisis and demand that we learn from mistakes made. Not only from our own mistakes in one instance, but from others’ mistakes, too. Thinking of earthquakes, Japan is among the first countries that come to mind. While earthquakes were big disasters that caused thousands of casualties many years ago, now, the country has managed to protect nearly all its habitants with a set of strategies mixing technology, logistics & resource planning, and regulations. sets clear priorities and targets for governments, along with recommendations on “building back better.” The framework highlights the need for a careful analysis of any disaster’s local effect and characteristics. It also talks about disaster risk governance at national, regional, and global levels. Reporting on their learnings for a program designed to help developing countries in disaster management, . The Sendai Framework for Disaster Risk Reduction the government of Japan and the World Bank talk about the importance of ensuring the resilience of water, sanitation, and telecommunications systems during disaster times Generalized to help governmental city planners of developing areas plan for urban resilience, the UN also has fantastic resources like the . Last but not least, the US Federal Emergency Management Agency has fantastic resources and programs like the , which states,  “a program is a nationally supported, locally implemented initiative that teaches people how to better prepare themselves for hazards that may affect their communities. CERT trains them in basic disaster response skills such as team organization, disaster medical operations, fire safety, and light search and rescue”. City Resilience Action Planning Tool Community Emergency Response Teams Conclusion We need to take steps now as a  global community and strive to put all living beings in dignified living conditions. Otherwise, in the busy agenda surrounding our lives, what we experienced in the last weeks is doomed to be forgotten soon. And sooner or later, another disaster will strike us again, leaving us frustrated and angry. We'll ask ourselves, why we didn’t do anything before. Some first pointers to get involved: Turkey Earthquake Donations, full list on Notion Open Development Network / (community of tech-savvy builders of solutions for the earthquake, main language is Turkish) Açık Yazılım Ağı r/Earthquakes Don’t forget, your best way to survive is through building a community. The lead image for this article was generated by HackerNoon's via the prompt "earthquake aftermath". AI Image Generator

Before We Forget Again: What We Learned from the Turkey Earthquake Response

আমরা আবার ভুলে যাবার আগে: তুরস্কের ভূমিকম্পের প্রতিক্রিয়া থেকে আমরা কী শিখেছি

Bevor wir es wieder vergessen: Was wir aus der Reaktion auf das Erdbeben in der Türkei gelernt haben

Antes de que olvidemos otra vez: lo que aprendimos de la respuesta al terremoto de Turquía

Avant d'oublier à nouveau : ce que nous avons appris de la réponse au tremblement de terre en Turquie

इससे पहले कि हम फिर से भूल जाएं: तुर्की भूकंप प्रतिक्रिया से हमने क्या सीखा

再び忘れる前に：トルコの地震対応から学んだこと

다시 잊기 전에: 터키 지진 대응에서 배운 것

Antes que esqueçamos de novo: o que aprendemos com a resposta ao terremoto na Turquia

Tekrar Unutmadan: Türkiye Deprem Müdahalesinden Öğrendiklerimiz

Trước khi chúng ta quên đi một lần nữa: Những gì chúng ta học được từ ứng phó với trận động đất ở Thổ Nhĩ Kỳ

在我们再次忘记之前：我们从土耳其地震响应中学到了什么

Artificial intelligence hype was pervasive last year and the reality of A.I. is white-hot right now. In fact, — up 31 percent from 2016. And, in Q3 2017. 61 percent of enterprises say they implemented A.I. in their organization in 2017 791 public companies mentioned A.I. in their earnings calls What does this mean? It means that A.I. is nearing “big data” or “blockchain” levels of notoriety. Soon, even the most unlikely companies will claim to use the technology (imagine company) to ride the zeitgeist and get some good press. In fact, TGI Fridays is using A.I. to automatically customize cocktails according to your preferences, so Alexa can morph you into a mixologist in the comfort of your own home. Now, that’s a practical use of A.I. and a fantastic brand extension experience. Long Island Iced Tea becoming a blockchain While the A.I. hype is real, so is the technology. The applications for artificial intelligence are endless, powerful, and deployable across an entire organization. Some of the applications today are awe-inspiring — and some are simply about efficiently scaling with optimal velocity. But it’s not magic. Companies need a focused approach for how to use A.I. based on their desired outcomes. In fact, companies that currently use A.I. have begun to see how this technology can yield efficient results in the form of increased customer engagement and retention. This week, I participated in a panel at the conference for digital marketing executives, to discuss how businesses use A.I. to improve customer insight and performance marketing. VentureBeat TRANSFORM Photos of event here: I was honored to join , General Partner at Foundation Capital; , Vice President of Artificial Intelligence at Zillow Group; and , Director of Growth Marketing at Tilting Point on the panel. Their ideas and commentary on the A.I. landscape were insightful. Ashu Garg Jasjeet Thind Matthew Sadofsky A.I. is Driving Better and More Unique Customer Experiences As the CEO of — an A.I.-powered social media marketing platform that delivers actionable recommendations on content, platform, and timing for engaging and growing your audience — I’ve seen first-hand how the technology can deliver insights to deeply personalize one’s experience, impact, and results while saving precious time by negating much of the grunt work or overly complex tasks. Sensai Good A.I. tools enable your company to market to individuals, rather than personas, based on what they need, when they need it. Furthermore, robust A.I. can anticipate that need beforehand. During #VBTransform, one of the more notable examples of this level of personalization is TGI Fridays — yes, the fast-casual dining chain. You might not think of TGI Fridays as a candidate for leveraging A.I., but Sherif Mityas — the Chief Experience Officer — has used the technology to recommend signature cocktails, based off their personal ordering patterns. “The goal is to increase customer engagement and, subsequently, revenues,” said Mityas in a Wall Street Journal report. “A.I. allows the brand to really, truly differentiate ourselves again.” (See the full article .) here Brian Hovis, Vice President of Marketing at Nordstrom, also shared how A.I. not only transforms the customer experience in Nordstrom stores, but also drives the creation of completely new store concepts that take advantage of different metro geographies — which delights their customers. Chatbots also came up time and time again as an easily implementable and highly effective application of A.I. A fireside chat between Mamie Peers of the Cosmopolitan of Las Vegas, and Clara De Soto, founder of Reply.ai, was particularly inspiring. For the Cosmopolitan, chatbots have driven a stunning increase in revenue per guest via their chatbot they’ve named Rose. Rose’s voice — confident, intriguing, alluring, sophisticated — aligns with the Cosmopolitan’s brand, and drives a 38 percent increase in customer satisfaction among those who converse with her. As a woman in tech, I am not a fan of Uber due to its ongoing sexual discrimination and harassment offenses (I use Lyft instead), but I was impressed by the clarity of vision and clear articulation of infusing artificial intelligence throughout the entire customer journey with acute focus on onboarding. The simplicity of the slide presented by Uber’s Director of Product, Jairam Ranganathan, was a great example of how to make a sometimes nebulous and evolving technology readily understood. I only wish we were able to hear more about their advancements in driverless car and other smart mobility developments. Uber’s A.I. approach Perhaps one of the most entertaining presentations was Vanja Josifovski, Pinterest’s CTO who describes the platform as a visual discovery engine as opposed to a social network. He said they maintain the original vision of the founders to encourage people to spend time offline. I am curious how that translates during advertising revenue and KPI discussions with investors, so I’ll have to ask them when I next see them. It’s certainly laudable in the face of so many companies that aim to exacerbate digital addictions. I suppose if you’re spending more time offline then you may be capturing and sharing more inspiring visual photos to share and pin. A.I. Can Replace Your Employees. But It Can Also Help Retain Them Artificial intelligence can sometimes be a pejorative term — especially when it’s in reference to employees and hiring. Most people are scared that the technology is going to render their job obsolete. And, yes, bots are replacing many of the repetitive tasks of customer service, but not always with the same purpose. Stewart Rogers of VentureBeat detailed how two of China’s biggest companies are using A.I. with wildly different approaches. First, there’s Xiaomi, who used bots to cut costs by replacing 66 percent of their call center staff. But Tencent used A.I. to reduce demanding and repetitive work, making its staff more effective and happy, so it could retain them. Rogers added, “So the lesson is: if you don’t want A.I. to replace you, work for nice people.” When is It Time to Build Rather than Buy or Partner? On our panel, I was joined by some really smart people who work for much larger companies than my early-stage startup. Yet, I’ve also served huge enterprises like Viacom and the BBC, so I’ve been on their side of the fence. A critical point for any business is knowing when to bring capabilities in-house. Paying 10 percent of a small marketing budget to an agency may make sense, but as your budget grows, it may become more effective to hire experts in-house. Matthew Sadofsky, who runs performance marketing for multiple mobile games, said it well: “We’re spending $3 million a month acquiring and retaining customers. Before we got to that point, I realized, I could hire some really smart people for what we’re paying the agency.” We also got a question about best practices, and I was happy to take that one on, since we have . Best practices are only a starting point. Every company has a unique audience, and they have to test and learn to understand how to launch effectively. a ton of learnings about that from Sensai A.I. is Ready for You Overall, the vibe at VentureBeat TRANSFORM was very encouraging. We’re seeing that businesses of all sizes are leveraging accessible A.I. tools to improve their business performance, retain employees, and grow revenue sustainably. Amir Khosrowshahi of Intel spoke on the conference’s second day about how Intel is democratizing A.I. by reducing the computing power required. Just as Moore’s Law has helped Intel reduce computers from something you store in an office basement to something you wear on your wrist, they’re striving to bring A.I. to more people in more places with slimmer requirements. Accessible A.I. is emerging. It’s not too early to start experimenting with A.I. tools. At Sensai we’re focused on delivering A.I. insights to SMBs that would otherwise take hundreds or thousands of humans to gather. This levels the social media playing field, which can get crowded with big budgets of enterprise level corporations. At Sensai, it’s an honor to be part of the artificial intelligence revolution, right when it’s catching fire. We’re excited to see more A.I. applications come to market, and hopefully we’ll see even more female founders entering the space soon. How are we implementing A.I. today? At Sensai, we are building an accessible and affordable A.I.-driven platform and products for businesses and artists. Some examples of of A.I.-driven insights include: : As I mentioned above, best practices are only a starting point. Time to post is critical on algorithmic-driven platforms, but it’s not one-size-fits-all, as many social media practitioners suggest. Not until you can sift through the myriad data of when one’s audience is active and engaging with social posts and to what degree, as well as multiple dimensions including customer verticals, their objectives and interests, their post contents and topics, paired with their audience composition — demographics, geolocation, social behaviors, seasonality, temporal, their subject of interest at the time, and more. Deep clustering and segmentation by A.I. and machine learning help surface optimal times to post, individualized for one customer. Best time to post recommendation : If you were a content marketer, or more specifically a social content marketer, how could you predict what would yield your expected return? With topic modeling and clustering model using A.I., NLP, and machine learning, we’re able to identify, classify, and determine specific content topics which would ignite interaction from your audience. Topic modeling : As many social specialists may already know, hashtags are used in determining how and to what degree social contents are being tagged, ranked, searched, discovered, and rendered to social media users. But how would you know which hashtags used in your posts would be relevant and would return an optimal level of interaction by your viewers beyond just the contents being ranked, searched, and discovered? The use of A.I., NLP, and machine learning models enables streaming of petabytes if not zetabytes of unstructured data from social media platforms, which we can then analyze, cluster, and rank, in order to surface hashtags that bear semantic relevance to the content. But you have to do it quickly: the 4 V’s of social data — volume, variety, velocity, and veracity — need to be executed in real-time while the hashtags are relevant and current. Hashtag recommendation I am looking forward to following up with many of the artificial intelligence leaders I met at TRANSFORM, so that we can all elevate our learning and collaborate in the ongoing development of new tools that improve life — maybe even humanity.

AI: The Hype is Real, and It’s Ready for You

AI systems are moving from development and testing into production. At their heart, AI systems are simply computation and therefore deploying a system remotely provides remote computation. If this system involves multiple steps of computation, there are many advantages to be gained by providing this computation as composable chunks that can be combined. I’m describing a concept that is usually called a . You package each bit of computation into a component. Then you send data through multiple components in sequence. Pipelines can be a very valuable deployment tool. They allow us to reuse bits of computation and make our architecture more flexible, allowing us to respond to new demands with agility. pipeline This post is divided into two parts. The first (which you are reading now) explains ways to architect Machine Learning pipelines with a concrete example. The dives into all the glorious details of implementing the different architectures with , including an implementation of the real-world example. second part GraphPipe Why Pipeline? Constructing pipelines provides many of the same advantages that decoupling does in software development. These advantages include: Units of computation are easy to replace. If you discover a better implementation for one chunk, you can replace it without changing the rest of the system. Flexibility: Each bit of computation is exposed via a common interface. If any part becomes a bottleneck, you can scale that component independently. Common scaling techniques might involve a load balancer or additional backends. Scalability: when the system is divided into meaningful pieces it creates natural points of extension for new functionality. Extensibility: Classy Image Recognition Pipelining becomes much clearer with a concrete example. Lets discuss how we might build the backend of an example application. Our example app is , a mobile app that classifies images. This application allows a user to select one or more images and it determines the class of each image. It also allows the user to enter one or more urls to get classifications for images from the web. We are targeting users with older phones and limited mobile bandwidth, so the majority of our computation must be done on the server side. We are going to architect (and pipeline!) the server components of Classy. Classy For classification, we will use an implementation of the venerable VGG model (a convolutional classification model that won the ImageNet competition a few years back). We might start by deploying the VGG model with a GraphPipe server. Normally VGG accepts images as a 224x224x3 array of floating point values. Additionally it expects the image to be preprocessed by being put in BGR order (instead of the normal RGB), and for each channel to be normalized by subtracting a constant value that is the average value for that channel across the entire ImageNet data set. Initial Architecture Classy Initial Architecture The simplest architecture for our system is to do the preprocessing on the mobile side. The application loads images from internal storage or downloads them from urls, does the preprocessing internally, and then sends the data to the remote VGG for inference. There are a few of drawbacks to our initial architecture: Sending uncompressed pixel data is inefficient. A 224x224x3 array of floats is about 600K. It would be much better if we could just send compressed jpgs (a few K) or urls (a few bytes) directly. Preprocessing on the phone uses cpu and may drain the battery (admittedly loading a jpg is not very processor-intensive, but one could imagine more complex preprocessing steps where this would be a concern). Preprocessing on the client side couples the client to the server implementation. If you decide to replace VGG with another model like Inception (which has different preprocessing requirements), you will have to update your client code. Our refined architectures will attempt to remove these drawbacks. Client Sequencing The first win is to move the preprocessor onto the server side. This will become a component that takes one or more jpgs and does the preprocessing necessary to prepare them for VGG. Note that we could include the preprocessing in the VGG model itself, but keeping it separate allows for other preprocessors to be plugged into the backend; we may want to support pngs or gifs, for example. Client Sequencing Architecture In our new architecture, Classy loads the image, sends it to the preprocessor, and sends the result to VGG. We refer to this pattern as . Each model provides some computation, and the client makes remote requests to each model in turn. Client sequencing is the most flexible approach: The computations can be used individually, or even reordered. client sequencing With this new architecture, Classy now has no specific logic tying it to the server classification implementation. It also doesn’t need to burn cpu to do preprocessing. Unfortunately, the bandwidth requirements have actually gotten worse! It now has to send and receive more data than it did in the initial architecture. We’ll solve this problem in the next iteration. Server Sequencing Rather then sending the data to two different models, it would be much more efficient for Classy to send the jpgs to the preprocessor, and then have the preprocessor send the data to VGG, retrieve the classes, and return them. Server Sequencing Architecture We call this architecture . One advantage of this approach is that the logic for communicating with VGG is hidden behind the preprocessor. As far as the Classy is concerned, it is only interacting with a single model that expects jpgs and returns classes. This comes at the cost of embedding the code to communicate with VGG inside the preprocessor. server sequencing In general, server sequencing can be much more efficient than client sequencing, especially if the first and second components are physically close together like in our design . It does sacrifice a bit of flexibility, however. The preprocessor is tightly coupled to VGG. In our example this makes sense, but there are times when we would prefer something a bit more flexible. Hybrid Sequencing There is one other inefficiency in our system. When we are classifying images based on web urls, we must download them first. We could save bandwidth with a component that downloads the images for us and passes them along to the preprocessor. This component takes a list of urls, and returns the classifications that it gets from the preprocessor. Hybrid Sequencing Architecture We could use server sequencing for this component, but downloading images and passing them along to other components could be useful in many pipelines. We therefore will use a modification called hybrid sequencing. In this architecture, the downloader sends data to the preprocessor just as in server sequencing. The difference is that Classy is expected to provide configuration specifying where the preprocessor is located. This enables the downloader to be used by other clients in other pipelines as well. Essentially, the client sends its data along with information that says: “Do your computation, then send the results to the model located over there and return the results to me”. This keeps the performance of server sequencing, but returns some of the flexibility of client sequencing. It does have some limitations when compared to the client sequencing approach. For example, intermediate results from the first model cannot be retrieved and used locally. Putting it Together Classy Final Architecture We will combine the last two sequencing architectures into our final architecture. Classy needs to support local images as well as remote urls, so in this version Classy can communicate with the downloader or the preprocessor. Note that because we are using a standard protocol for remote communication, the downloader can use the same client code to talk to the preprocessor that Classy uses internally. This architecture is flexible: Classy can send local jpgs to the preprocessor, or it can send urls to the downloader. When it talks to the downloader, it sends the location of the preprocessor as the next stage. If we decide to replace VGG with an alternative implementation, we can simply replace VGG and the preprocessor; the downloader and the client do not need to be changed. This architecture is scalable: all of the communication is GraphPipe over http. We can colocate models for efficient transmission, or we can stick load balancers and multiple backends into any part of the pipeline that is getting overloaded. Finally, this architecture is extensible: we could add another preprocessor to handle pngs or gifs and use the same VGG model behind it. We could even use the same downloader for urls if we provide config pointing to the new preprocessor along with our request. Conclusion This post provided an overview of machine learning model pipelining using a real example application. Pipelining will become more common as we see AI systems broadly deployed into production. in this series shows how to implement the three sequencing approaches discussed above and walks through the implementation of Classy. Part II

Machine Learning Model Pipelines: Part I

Artificial intelligence is rampant in schools, but education professionals aren’t teaching students the importance of ethical utilization. The problem is that it’s a complex topic, and educators simply don’t have the time or resources to tackle it. Where do they get the relevant materials? How do they fit it into their existing lesson plans? Here’s how questions like these are holding AI ethics classes back — and what decision-makers are doing to fix the problem. The Importance of Teaching AI Ethics in School AI ethics is a set of principles that govern the development, deployment and utilization of algorithm-based tools. They are meant to guide stakeholders, minimizing the damage such technologies can do to the environment, society or the economy. On a smaller scale, they focus on preventing bias, discrimination, misinformation and abuse. Teaching AI ethics in school may seem unnecessary, but its importance cannot be overstated. AI has already become a staple in many industries. According to a McKinsey & Company report, 72% of organizations use it for at least one business function. If its upward trajectory continues, its impact could rival that of computers, smartphones or the internet. Avoiding talking about AI in the classroom because of its newness needlessly repeats history. It is the same as years ago when students were told they wouldn’t always have a calculator in their back pocket right after the smartphone was invented. This technology’s popularity is growing in both personal and professional domains. Students must be prepared to use it responsibly when entering the workforce to ensure better outcomes for their industry and society. AI is powerful. Advanced algorithms can analyze massive datasets exponentially faster than humans. They excel at pattern recognition, trend prediction and content creation. Whether educators like it or not, AI will likely change the world. If they prepare their students accordingly, they can ensure that change is positive. Ethical Dilemmas Students Face When Using AI Typical ethical risks students face when using AI include technology abuse, harassment and plagiarism. The latter is one of the most common. Thanks to generative tools, academic dishonesty is easy to get away with. Google Trends reveals that searches for “AI essay writing” increased by a staggering 2,041% after ChatGPT’s release. Unfortunately, plagiarism isn’t the most severe offense. Lately, university, high school and even middle school students have been caught using generative tools to take part in deepfake-related sex crimes. In South Korea, one journalist uncovered one such group that had over 2,000 members. Many victims were believed to be under 16 — under Korea’s age of consent. Even if kids aren’t using regular photos of their classmates to create explicit material, AI use still revolves around consent. This concept revisits data privacy and security principles — all data end users enter is no longer private. The platform or service provider may use it to train their model or enhance their marketing strategies. Many of today’s students view generative and machine learning models as entertaining, harmless tools — even when they are actively harming others. Teachers have a unique opportunity to educate them early on about the consequences of such actions, stopping unacceptable behavior like this at the source. Is Ethical AI Use Being Taught in Schools Today? School board standards, classroom policies and state laws vary dramatically, so AI adoption has been erratic at best. This split between those who embrace it and those who don’t has stalled standardization. However, some are still taking a chance on ethics courses. Elementary School While various academics and industry experts argue ethics discussions should start in elementary school, relatively few institutions even use algorithm-based tools that early. There are virtually no public resources available for grade school administrators. Middle and High School Many middle and high schools aren’t discussing the intricacies of ethical AI. Instead, they are cracking down on AI use. Studies show that the accuracy of AI detection tools is typically about 50%, so most instances slip by unnoticed. As a result, kids often learn bad habits. Promisingly, some decision-makers have seen the need for AI ethics. For instance, California Governor Gavin Newsom signed Bill No. 2876 into law in September 2024. When curriculum frameworks are revised in January 2025, schools must incorporate AI literacy content — which guides critical and ethical use — into the classroom. Not all educators receive playbooks from experts. Some, like Jeff Simon, a mathematics teacher at Sage Creek High School, are navigating this new field on their own. Simon said he is trying to “teach the ethical use” of AI since it is  “just going to keep on growing.” College and University Most AI ethics classes are offered at higher education institutions because working those discussions into data science or computer engineering courses is easier. At the very least, many have developed policies for generative AI use. Reasons Why AI Ethics Isn’t Being Taught in School AI is not just present in education — it is thriving. For reference, in 2024, 86% of students admitted to using AI for schoolwork. Despite its popularity, many schools have no guiding principles on how to use these models ethically. Time is one of the main constraints — educators face the impossible task of doing more with less. They already spend 54 hours at work per week. How will they learn about AI and develop classroom policies that align with ethics in addition to teaching curriculum, grading papers and creating lesson plans? The field of AI is complex and rapidly evolving. Even with district-wide support, educational materials may quickly become outdated. Many schools still use decades-old textbooks to teach, so any solution would require extensive retrofitting. Moreover, how do they send home ethics homework? What would tests look like? How would they assign fair grades? Educators may be willing to embrace the concept of ethical AI classes if given the resources, but there’s no clear framework on how that could be done. Should they begin teaching it in elementary school? Will it replace a class, be offered as an elective or be integrated into the existing curriculum? There’s no one-size-fits-all solution. Many teachers have adopted “not in my classroom” policies in response to these concerns. As a result, students likely won’t learn how to use these tools the right way. Many will take whatever inaccurate, unregulated knowledge they cobble together from peers, parents and the internet as the truth — which may have far-reaching impacts. Addressing Concerns to Make AI Ethics Classes a Reality There are several ways education professionals can address why AI ethics isn’t being taught in schools. Place AI Ethics Wherever It Fits Integrating AI ethics into the classroom can mean creating a new elective or squeezing it in at the end of the period. Placing it where it naturally appears is best. For example, when students are assigned an eight-page essay, they can hear about responsible generative AI use. Teachers and administrators should adapt these new courses to students’ grade, technical expertise and knowledge levels to make the subject matter age-appropriate and engaging. Assignments should be relatively open-ended but follow a clear ethical framework. Tailor Subject Matter for End Users Typically, conversations about ethical AI focus on data privacy and output bias, centering around people who develop, deploy or manage these tools. End users interact with the user interface — not the backend — so their approach must differ slightly. Core principles should be accountability, value alignment, explainability, fairness and integrity. Pupils should learn critical thinking to combat bias, reasoning to prevent overreliance and objective analysis to mitigate the spread of misinformation. Model Responsible AI Use If state law or school board policies make teaching the ethics of AI in the classroom difficult, educators should take advantage of workarounds. Modeling responsible, honorable behavior goes a long way. Alternatively, they can invite a guest speaker. Facilitate In-Class Ethics Discussions Facilitating discussions, debates and group work in class lets high school teachers and college professors act as moderators. Posing questions about data de-identification, copyright infringement and summarization as they relate to AI can initiate productive conversations. Modern Lessons for a Modern Age Digitalization’s popularity is exponentially increasing. Soon, AI may be a part of every industry in the world. If educators modernize their lessons for this modern age, they can ensure today’s youth have a headstart once they reach the workforce. Artificial intelligence is rampant in schools, but education professionals aren’t teaching students the importance of ethical utilization. The problem is that it’s a complex topic, and educators simply don’t have the time or resources to tackle it. Artificial intelligence is rampant in schools, but education professionals aren’t teaching students the importance of ethical utilization. The problem is that it’s a complex topic, and educators simply don’t have the time or resources to tackle it. Where do they get the relevant materials? How do they fit it into their existing lesson plans? Here’s how questions like these are holding AI ethics classes back — and what decision-makers are doing to fix the problem. The Importance of Teaching AI Ethics in School AI ethics is a set of principles that govern the development, deployment and utilization of algorithm-based tools. They are meant to guide stakeholders, minimizing the damage such technologies can do to the environment, society or the economy. On a smaller scale, they focus on preventing bias, discrimination, misinformation and abuse. Teaching AI ethics in school may seem unnecessary, but its importance cannot be overstated. AI has already become a staple in many industries. According to a McKinsey & Company report, 72% of organizations use it for at least one business function. If its upward trajectory continues, its impact could rival that of computers, smartphones or the internet. 72% of organizations use it 72% of organizations use it Avoiding talking about AI in the classroom because of its newness needlessly repeats history. It is the same as years ago when students were told they wouldn’t always have a calculator in their back pocket right after the smartphone was invented. This technology’s popularity is growing in both personal and professional domains. Students must be prepared to use it responsibly when entering the workforce to ensure better outcomes for their industry and society. AI is powerful. Advanced algorithms can analyze massive datasets exponentially faster than humans. They excel at pattern recognition, trend prediction and content creation. Whether educators like it or not, AI will likely change the world. If they prepare their students accordingly, they can ensure that change is positive. Ethical Dilemmas Students Face When Using AI Typical ethical risks students face when using AI include technology abuse, harassment and plagiarism. The latter is one of the most common. Thanks to generative tools, academic dishonesty is easy to get away with. Google Trends reveals that searches for “AI essay writing” increased by a staggering 2,041% after ChatGPT’s release. increased by a staggering 2,041% increased by a staggering 2,041% Unfortunately, plagiarism isn’t the most severe offense. Lately, university, high school and even middle school students have been caught using generative tools to take part in deepfake-related sex crimes. In South Korea, one journalist uncovered one such group that had over 2,000 members. Many victims were believed to be under 16 — under Korea’s age of consent. uncovered one such group uncovered one such group Even if kids aren’t using regular photos of their classmates to create explicit material, AI use still revolves around consent. This concept revisits data privacy and security principles — all data end users enter is no longer private. The platform or service provider may use it to train their model or enhance their marketing strategies. Many of today’s students view generative and machine learning models as entertaining, harmless tools — even when they are actively harming others. Teachers have a unique opportunity to educate them early on about the consequences of such actions, stopping unacceptable behavior like this at the source. Is Ethical AI Use Being Taught in Schools Today? School board standards, classroom policies and state laws vary dramatically, so AI adoption has been erratic at best. This split between those who embrace it and those who don’t has stalled standardization. However, some are still taking a chance on ethics courses. Elementary School While various academics and industry experts argue ethics discussions should start in elementary school, relatively few institutions even use algorithm-based tools that early. There are virtually no public resources available for grade school administrators. Middle and High School Many middle and high schools aren’t discussing the intricacies of ethical AI. Instead, they are cracking down on AI use. Studies show that the accuracy of AI detection tools is typically about 50% , so most instances slip by unnoticed. As a result, kids often learn bad habits. is typically about 50% is typically about 50% Promisingly, some decision-makers have seen the need for AI ethics. For instance, California Governor Gavin Newsom signed Bill No. 2876 into law in September 2024. When curriculum frameworks are revised in January 2025, schools must incorporate AI literacy content — which guides critical and ethical use — into the classroom. signed Bill No. 2876 into law signed Bill No. 2876 into law Not all educators receive playbooks from experts. Some, like Jeff Simon, a mathematics teacher at Sage Creek High School, are navigating this new field on their own. Simon said he is trying to “teach the ethical use” of AI since it is  “just going to keep on growing.” trying to “teach the ethical use” trying to “teach the ethical use” College and University Most AI ethics classes are offered at higher education institutions because working those discussions into data science or computer engineering courses is easier. At the very least, many have developed policies for generative AI use. Reasons Why AI Ethics Isn’t Being Taught in School AI is not just present in education — it is thriving. For reference, in 2024, 86% of students admitted to using AI for schoolwork. Despite its popularity, many schools have no guiding principles on how to use these models ethically. 86% of students admitted 86% of students admitted Time is one of the main constraints — educators face the impossible task of doing more with less. They already spend 54 hours at work per week. How will they learn about AI and develop classroom policies that align with ethics in addition to teaching curriculum, grading papers and creating lesson plans? spend 54 hours at work spend 54 hours at work The field of AI is complex and rapidly evolving. Even with district-wide support, educational materials may quickly become outdated. Many schools still use decades-old textbooks to teach, so any solution would require extensive retrofitting. Moreover, how do they send home ethics homework? What would tests look like? How would they assign fair grades? Educators may be willing to embrace the concept of ethical AI classes if given the resources, but there’s no clear framework on how that could be done. Should they begin teaching it in elementary school? Will it replace a class, be offered as an elective or be integrated into the existing curriculum? There’s no one-size-fits-all solution. Many teachers have adopted “not in my classroom” policies in response to these concerns. As a result, students likely won’t learn how to use these tools the right way. Many will take whatever inaccurate, unregulated knowledge they cobble together from peers, parents and the internet as the truth — which may have far-reaching impacts. Addressing Concerns to Make AI Ethics Classes a Reality There are several ways education professionals can address why AI ethics isn’t being taught in schools. Place AI Ethics Wherever It Fits Integrating AI ethics into the classroom can mean creating a new elective or squeezing it in at the end of the period. Placing it where it naturally appears is best. For example, when students are assigned an eight-page essay, they can hear about responsible generative AI use. Teachers and administrators should adapt these new courses to students’ grade, technical expertise and knowledge levels to make the subject matter age-appropriate and engaging. Assignments should be relatively open-ended but follow a clear ethical framework. Tailor Subject Matter for End Users Typically, conversations about ethical AI focus on data privacy and output bias, centering around people who develop, deploy or manage these tools. End users interact with the user interface — not the backend — so their approach must differ slightly. Core principles should be accountability, value alignment, explainability, fairness and integrity. Pupils should learn critical thinking to combat bias, reasoning to prevent overreliance and objective analysis to mitigate the spread of misinformation. Model Responsible AI Use If state law or school board policies make teaching the ethics of AI in the classroom difficult, educators should take advantage of workarounds. Modeling responsible, honorable behavior goes a long way. Alternatively, they can invite a guest speaker. Facilitate In-Class Ethics Discussions Facilitating discussions, debates and group work in class lets high school teachers and college professors act as moderators. Posing questions about data de-identification, copyright infringement and summarization as they relate to AI can initiate productive conversations. Modern Lessons for a Modern Age Digitalization’s popularity is exponentially increasing. Soon, AI may be a part of every industry in the world. If educators modernize their lessons for this modern age, they can ensure today’s youth have a headstart once they reach the workforce.

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SENDAI SMART MACHINES

ssmcoltd.co.jp

1-10 employees

Since 2016

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EVERGREEN INDEX #97

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