Super Advisor

superadvisor

Django is the most popular Python-based web framework for a while now. Django is powerful, robust, full of capabilities and surrounded by a supportive community. Django is based on models, views and templates, similarly to other MVC frameworks out there. Django provides you with a development server out of the box once you start a new project using the commands: $ django-admin startproject my_project$ python ./manage.py runserver 8000 With two lines in the terminal, you can have a working development server on your local machine so you can start coding. One of the tricky parts when it comes to Django is deploying the project so it will be available from different devices around the globe. As technological entrepreneurs, we need to not only develop apps with backend and frontend but also deploy them to a production environment which has to be modular, maintainable and of course secure. Deployment of a Django app requires different mechanisms which will be listed. Before we begin, we need to perform an alignment in terms of the tools we are going to use throughout this post: Python version 2.7.6 Django version 1.11 Linux Ubuntu server hosted on DigitalOcean cloud provider Linux Ubuntu local machine Git repository containing your codebase I assume you are already using 1, 2, 4 and 5. About the Linux server, we are about to create it together during the first step of the deployment tutorial. Please note that this post discusses deployment on a single Ubuntu server. This configuration is great for small projects, but in order to scale your resources up to support larger amounts of traffic, you should consider a , using load balancers, floating IP addresses, redundancy and more. high-availability server infrastructure Linux is much more popular for serving web apps than Windows. Additionally, Python and Django work together very well with Linux, and . not so well with Windows There are many reasons for choosing DigitalOcean as a cloud provider, especially for small projects that will be deployed on a single droplet (a virtual server in DigitalOcean terminology). DigitalOcean is a great solution for software projects and startups which start small and scale up step by step. Read more about in terms of an early-stage startup software project. my comparison between DigitalOcean and Amazon Web Services There are some I highly recommend you to follow before starting the deployment process. The best practices include working with a virtual environment, exporting requirements.txt file and configuring the settings.py file for working with multiple environments. best practices for setting up your Django project This post will cover the deployment process of a Django project from A to Z on a brand-new Linux Ubuntu server. Feel free to choose your favorite cloud provider other than DigitalOcean for deployment. As aforesaid, the built-in development server of Django is weak and is not built for scale. You can use it for developing your Django project yourself or share it with your co-workers, but not more than that. In order to serve your app in a production environment, we need to use several components that will talk to each other and make the magic happen. Hosting a web application usually requires the orchestration of three actors: Web server Gateway Application The web server The web server receives an HTTP request from the client (the browser) and is usually responsible for load balancing, proxying requests to other processes, serving static files, caching and more. The web server usually interprets the request and sends it to the gateway. Common web server and Apache and Nginx. In this tutorial, we will use Nginx (which is also my favorite). The Gateway The gateway translates the request received from the web server so the application can handle it. The gateway is often responsible for logging and reporting as well. We will use Gunicorn as our Gateway for this tutorial. The Application As you may already guess, the application refers to your Django app. The app takes the interpreted request, process it using the logic you implemented as a developer, and returns a response. Assuming you have an existing ready-for-deployment Django project, we are going to deploy your project by following these steps: Creating a new DigitalOcean droplet Installing pre requisites: pip, virtual environment, and git Pulling the Django app from Git Setting up PostgreSQL Configuring Gunicorn with Supervisor Configuring Nginx for listening to requests Securing your deployed app: setting up firewall Creating a droplet A droplet in refers to a virtual Linux server with CPU, RAM and disk space. The first step in this tutorial is about creating a new droplet and connect to it via SSH. Assuming your local machine is running Ubuntu, we are going to create a new SSH key pair in order to easily and securely connect to our droplet once it is created. Connection using SSH keys (rather than a password) is both more simple and secure. If you already have an SSH key pair, you can skip the creation process. On your local machine, enter in the terminal: DigitalOcean $ ssh-keygen -t rsa You should get two more questions, where to locate the keys (the default is fine) and whether you want to set up a password (not essential). Now the key pair is located in: /home/user/.ssh/ where is your public key and is your private key. In order to use the key pair to connect to a remote server, the public key should be located on the remote server and the private key should be located on your local machine. id_rsa.pub id_rsa Notice that the public key can be located on every remote server you wish to connect to. But, the private key must be kept only on your local machine! Sharing the private key will enable other users to connect to your server. After signing up with , open the and click on the Add SSH Key button. In your terminal copy the newly-created public key: DigitalOcean SSH page $ cat /home/user/.ssh/id_rsa.pub Enter the new public key you generated and name it as you wish. Now once the key is stored in your account, you can assign it with every droplet you create. The droplet will contain the key so you connect to it from your local machine, while password authentication will be disabled by default, which is highly recommended. Now we are ready to create our droplet. Click on “Create Droplet” at the top bar of your DigitalOcean dashboard. Choose Ubuntu 16.04 64bit as your image, droplet size which is either 512MB RAM or 1GB, whatever region that makes sense to you. You can select the private networking feature (which is not essential for this tutorial). Make sure to select the SSH key you’ve just added to your account. Name your new droplet and click “Create”. Once your new droplet has been created, you should be able to connect to it easily using the SSH key you created. In order to do that, copy the IP address of your droplet from your droplets page inside your dashboard, go to your local terminal and type: $ ssh root@IP_ADDRESS_COPIED Make sure to replace with your droplet’s IP address. You should be already connected by now. IP_ADDRESS_COPIED Tip for advanced users: in case you want to configure an even simpler way to connect, add an alias to your droplet by editing the file: $ nano /home/user/.ssh/config and adding: Host remote-server-name    Hostname DROPLET_IP_ADDRESS    User root Make sure to replace with a name of your choice, and with the IP address of the server. remote-server-name DROPLET_IP_ADDRESS Save the file by hitting and then close it with . Now all you need to do in order to connect to your droplet is typing: Ctrl+O Ctrl+X $ ssh remote-server-name That simple. Installing prerequisites Once connected to your droplet, we are going to install some software in order to start our deployment process. Start by updating your repositories and installing pip and virtualenv. $ sudo apt-get update Hopefully, you work with a virtual environment on your local machine. In case you don’t, I highly recommend you reading my in order to realize why working with virtual environments is an essential part of your Django development process. best practices post for setting up a Django project Let’s get to configuring the virtual environment. Create a new folder with: $ mkdir ~/.virtualenvs$ export WORKON_HOME=~/.virtualenvs Configure the virtual environment wrapper for easier access by running: $ nano ~/.bashrc and adding this line to the end of the file: . /usr/local/bin/virtualenvwrapper.sh Tip: use to scroll down faster, and to scroll up faster inside the nano editor. Ctrl+V Ctrl+Y Hit to save the file and to close it. In your terminal type: Ctrl+O Ctrl+X $ . .bashrc Now you should be able to create your new virtual environment for your Django project: $ mkvirtualenv virtual-env-name From within your virtual environment install: (virtual-env-name) $ pip install django gunicorn psycopg2 Tip: Useful command for working with your virtual environment: $ workon virtual-env-name # activate the virtual environment$ deactivate              # deactivate the virtual environment Pulling application from Git Start by creating a new user that will hold your Django application: $ adduser django$ cd /home/django$ git clone REPOSITORY_URL Assuming your code base is already located in a Git repository, just type your password and your repository will be cloned into your remote server. You might need to add permissions to execute by navigating into your project folder (the one you’ve just cloned) and type: manage.py $ chmod 755 ./manage.py In order to take the virtual environment one step further in terms of simplicity, copy the path of your project’s main folder to the virtual environment settings by typing: $ pwd > /root/.virtualenvs/virtual-env-name/.project Make sure to replace with the real name of your virtual environment. Now, once you use the command to activate your virtual environment, you’ll be navigated automatically to your project’s main path. virtual-env-name workon In order to setup the the environment variable properly, type: $ nano /root/.virtualenvs/virtual-env-name/bin/postactivate # replace virtual-env-name with the real name and add this line to the file: export DJANGO_SETTINGS_MODULE=app.settings Make sure to replace with the location of your settings module inside your Django app. Save and close the file. app.settings Assuming you’ve set up your as described in the , you’re now able to install all your requirements at once by navigating to the path of the file and run from within your virtual environment: requirements.txt file Django best practices post requirements.txt (virtual-env-name) $ pip install -r requirements.txt Setting up PostgreSQL Assuming you’ve set up your settings module as described in the , you should have by now a separation between the development and production settings files. Your settings file should contain PostgreSQL connection settings as well. If it doesn’t, add to the file: Django best practices post production.py DATABASES = {    'default': {        'ENGINE': 'django.db.backends.postgresql',        'NAME': 'app_db',        'USER': 'app_user',        'PASSWORD': 'password',        'HOST': 'localhost',        'PORT': '5432',    }} I highly recommend updating and pushing the file on your local machine and pulling it from the remote server using the repository we cloned. Let’s get to creating the production database. Inside the terminal, type: $ sudo -u postgres psql Now you should be inside PostgreSQL terminal. Create your DB and user with: > CREATE DATABASE app_db; Make sure your details here match the settings file DB configuration as described above. Exit the PostgreSQL shell by typing . production.py \q Now you should be ready to run migrations command on the new DB. Assuming all of your migrations folders are in the file, meaning they are not pushed into the repository, your migrations folders should be empty. Therefore, you can set up the DB by navigating to your main project path with: .gitignore (virtual-env-name) $ cdproject and then run: (virtual-env-name) $ python ./manage.py migrate (virtual-env-name) $ python ./manage.py makemigrations (virtual-env-name) $ python ./manage.py migrate Don’t forget to create yourself a superuser by typing: (virtual-env-name) $ python ./manage.py createsuperuser Configuring Gunicorn with Supervisor Now once the application is set up properly, it’s time to configure our gateway for sending requests to our Django application. We will use Gunicorn as our gateway, which is commonly used. Start by navigating to your project’s main path by typing: (virtual-env-name) $ cdproject First, we will test gunicorn by typing: (virtual-env-name) $ gunicorn --bind 0.0.0.0:8000 app.wsgi:application Make sure to replace with your app’s name. Once gunicorn is running your application, you should be able to access and see your application in action. app http://IP_ADDRESS:8000 When you’re finished testing, hit to stop gunicorn from running. Ctrl+C Now it’s time to operate gunicorn from a service to make sure it’s running continuously. Rather than setting up a service, we will use a more robust way with Supervisor. Supervisor, as the name suggests, is a great tool for monitoring and controlling processes. It helps you understand better how your processes operate. systemd To install supervisor, type outside of your virtual environment: $ sudo apt-get install supervisor Once supervisor is running, every file that is included in the path: .conf /etc/supervisor/conf.d represents a monitored process. Let’s add a new file to monitor gunicorn: .conf $ nano /etc/supervisor/conf.d/gunicorn.conf and add into the file: [program:gunicorn]

[group:guni] Make sure that all the references are properly configured. Save and close the file. Now let’s update supervisor to monitor the gunicorn process we’ve just created by running: $ supervisorctl reread In order to validate the process integrity, use this command: $ supervisorctl status By now, gunicorn operates as an internal process rather than a process that can be accessed by users outside the machine. In order to start sending traffic to gunicorn and then to your Django application, we will set up Nginx the serve as a web server. Configuring Nginx Nginx is one of the most popular web servers out there. The integration between Nginx and Gunicorn is seamless. In this section, we’re going to set up Nginx to send traffic to Gunicorn. In order to do that, we will create a new configuration file (make sure to replace with your own app name): app $ nano /etc/nginx/site-available/app then edit the file by adding: server { This configuration will proxy requests to the appropriate route in your server. Make sure to set all the references properly according to Gunicorn and to your app configurations. Initiate a link with: $ ln -s /etc/nginx/sites-available/app /etc/nginx/sites-enabled Check Nginx configuration by running: $ nginx -t Assuming all good, restart Nginx by running: $ systemctl restart nginx By now you should be able to access your server only by typing your IP in the browser because Nginx listens on port 80 which is the default port browsers use. Security Well done! You should have a deployed Django app by now! Now it’s time to secure the app to make sure it’s much difficult to hack it. In order to do that, we will use built-in Linux firewall. ufw ufw works by configuring rules. Rules tell the firewall which kind of traffic it should accept or decline. At this point, there are two kinds of traffic we want to accept, or in other words, two ports we want to open: port 80 for listening to incoming traffic via browsers port 22 to be able to connect to the server via SSH. Open the port by typing: $ ufw allow 80 then enable by typing: ufw $ ufw enable before closing the terminal, make sure you are able to connect via SSH from another terminal to so you’re not locked outside your droplet due to bad configurations of the firewall. Tip: What to do next? This post is the ultimate guide to deploy a Django app on a single server. In case you’re developing an app that should serve larger amounts of traffic, I suggest you look into highly scalable server architecture. You can start with my post about . how to design a high-availability server architecture Any comments with additional tips or new points of view would be highly appreciated! For more insightful posts for coders with entrepreneurial mindset, visit / https://codingstartups.com Published originally . here

Deploy Django app with Nginx, Gunicorn, PostgreSQL & Supervisor

After a long day trying to deploy my .NET Core microservice to IIS7(?), I finally persuaded my client to port our app to a linux server. In this post, I am going to create a simple React app that uses a .NET Core backend service, indeed, my goal is writing about tricky process of deployment. Here are the steps: Creating a sample React app & Getting the prod ready files Creating a sample .NET Core app Connect and send them to a Linux machine Installing Nginx & configuration — Deployment for client Supervisor installing & configuration — Deployment for server Creating a sample React application For creating a boilerplate code, I am going to use create-react-app, which is an official cli for react applications. I had zero problem with it. you can find further information Here Install it: $ npm install -g create-react-app 2. Create your project: $ create-react-app sample-react-app 3. Check that everything is ok. Application will be running at port 3000. $ cd sample-react-app$ npm start 4. Let’s prepare the build files for the deployment $ npm run build Now you have a folder in your project folder that contains and Cool, we have a sample front end base now. We will send this file to the remote server. build asset-manifest.json favicon.ico index.html static. Creating a sample .NET Core application I can use Visual Studio 2015 to create our backend base, but I prefer using Yeoman for those who have Mac or Linux. Install Yeoman: $ npm install -g yo 2. Install ASP.NET Generator: $ npm install -g generator-aspnet 3. Create your .NET Core Microservice: Choose: * Web Application Basic [without Membership and Authorization] * BootstrapName it: * SampleMicroservice $ yo aspnet Go inside the created folder and change the Index method of HomeController: public IActionResult Index(){return Json(new {Hello = "From the api"});} 4. Go inside and add after Program.cs .UseUrls("http://0.0.0.0:5000") .UseKestrel() Run the service and try hitting and be sure that the json appears on screen: [http://localhost:5000](http://localhost:5000) Connect and send them to a Linux machine For a cheap Linux VPS, I prefer . For your personal projects, you can use Raspberry PI + a static IP as your own server. DigitalOcean To send what we created so far, you can use a version controlling system like Github and SVN or use SFTP protocol. Reminder for SFTP: sftp username@remote_hostname_or_IP # Basic Commands:pwd -> remote working dirls -> remote lslpwd -> your working dirlls -> ls for your machineget remoteFile localFile -> Get the file to the local machineput localFile -> Send the file to the remote server # simply put l as a prefix to the commands for running them in local machine Installing Nginx & configuration — Deployment for client So, we are in our Linux server, connected via and our folder extracted by is in our workspace. ssh build npm run build SSH Reminder: $ ssh user_name@IPThen enter your pass Install Nginx: $ sudo apt-get install nginx Edit the configuration file by vim or nano: vim -or nano- /etc/nginx/sites-available/default In this file default configuration appears: server {listen 80 default_server;listen [::]:80 default_server; # Some comments...

    root /var/www/html;  **\# STATIC FILE LOCATION**

    # Some comments...  
    index index.html index.htm index.nginx-debian.html;

    server\_name \_;

    location / {  
            # Some comments...  
            try\_files $uri /index.html;   **\# ADD THIS**  
    }

    # Some comments... } At first, you should move all the files in your folder to the static file location, which is as default. build /var/www/html $ cp -a /your/build/folder/location/build/* /var/www/html/ Before you copy the files, you should add inside in the nginx configuration as you don’t want to give 404 not found error to the user trying to directly go to the different url in your single page app. You simply say here that “Any routes should match with the , Let’s give the responsibility to the React Router.” try_files $uri /index.html; location / index.html After restarting nginx, your homepage should run on the : http://your_ip $ sudo service nginx restart Supervisor installing & configuration — Deployment for server We are again in our Linux server and is in our workspace. our dotnet core project folder I prefer explaining this step by step: 1. Create a user to run your future daemon -service-. And login with that user: $ adduser sampleMicroService...$ usermod -aG sudo sampleMicroService$ su sampleMicroService 2. Install dotnet by these steps. 3. Prepare your dll files: Go into your project folder and extract the deployment files: $ dotnet restore $ dotnet run (optional to check that your app runs successfully) $ dotnet publish folder should be in publish /bin/Debug/netcoreapp1.x/ 4. Move the all the files in folder to somewhere in publish /var $ mv /your/publish/folder/location/publish/* /var/SampleMicroservice/ Note: The reason we are compiling and publishing our dll’s in our deployment server instead of our local machine is that we want folder to set it as an environment variable in supervisor configuration. Actually this is a bug in dotnet cli, if we don’t set an environment variable is thrown with message. is the bug details. /home/sampleMicroService/.nuget/packages ArgumentNullException path1 can't be null Here 5. Install supervisor to create your daemon: $ sudo apt-get install supervisor 6. Create supervisor configuration file by: $ sudo vim -or nano-  /etc/supervisor/conf.d/sampleMicroService.conf And put this inside: [program:sampleMicroService]command=/usr/bin/dotnet /var/sampleMicroService/SampleMicroService.dlldirectory=/var/sampleMicroService/autostart=trueautorestart=truestderr_logfile=/var/log/sampleMicroService.err.logstdout_logfile=/var/log/sampleMicroService.out.logenvironment=ASPNETCORE_ENVIRONMENT=Production,NUGET_PACKAGES="/home/anil/.nuget/packages"user=sampleMicroServicestopsignal=INT Be sure that you set NUGET_PACKAGES environment variable here. Stop supervisor, enable systemctl and start supervisor: $ sudo service supervisor stop$ sudo systemctl enable supervisor$ sudo service supervisor start From now on you can start/stop your service by: $ sudo supervisorctl start/stop/status sampleMicroService Check the error or out logs: $ tail -f /var/log/sampleMicroService/sampleMicroService.err.log$ tail -f /var/log/sampleMicroService/sampleMicroService.out.log That’s it, your micro service is running on http://YOUR_IP:5000 Ps. You can easily add your domain name by clicking “Add domain” to droplet menu if you use digitalocean like me. Thanks, Sometimes I tweet useful piece of information: @_skynyrd Btw, my references: Official documentation Hanselman’s post Cameronbwhite90’s post Stackoverflow

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In recent years, there’s been a lot of hype about artificial intelligence (AI) revolutionizing industries and making certain jobs obsolete. One of the most frequent predictions is that AI will take over call centers, automating customer service entirely and leaving human agents behind. But this narrative is far too simplistic. In fact, next time you hear someone say AI will replace call center workers, it might be time to run—because the reality is much more complicated, and in many cases, AI is far from ready to take over. Yes, AI is making strides in areas like chatbots, virtual assistants, and automated customer support systems. However, this technology still has major limitations, especially when it comes to the human aspects of customer service. And recent examples have shown that even the most advanced AI systems still require human oversight. The Human Touch Still Matters Customer service is fundamentally about human interaction. People need to feel heard, understood, and supported—especially when dealing with frustrating or sensitive issues. AI may be able to process large amounts of data, but it lacks the empathy and emotional intelligence that humans bring to customer service. For many customers, the ability to communicate with a real person who understands their situation is non-negotiable. Recent examples highlight just how crucial human intervention still is in AI-driven systems. For instance, Amazon Fresh promoted its AI-driven shopping experience as cutting-edge, but it turned out that much of the work was actually being done by humans. Specifically, an Indian business process outsourcing (BPO) team was watching customers via CCTV and stepping in when the AI couldn’t handle certain tasks. Far from replacing human workers, AI actually required their presence behind the scenes. AI Struggles with Complexity While AI systems can handle routine inquiries and straightforward tasks, they often fall short when problems become complex or unexpected. Human agents, on the other hand, excel in creative problem-solving and thinking outside the box, something that AI simply isn’t capable of doing. One recent blunder involved a GM chatbot that was fooled by a customer into offering a Chevrolet Tahoe for $1. The AI-powered chatbot was tricked into providing an outrageous discount—a glaring reminder that AI systems can be easily manipulated and are often incapable of detecting subtle deception. In scenarios like these, human agents are essential for catching errors and ensuring that transactions run smoothly. AI, as this incident proves, is still vulnerable to making costly mistakes. Cultural and Contextual Understanding Is Critical Customer service also requires an understanding of cultural nuances and local contexts, which AI struggles to interpret. Human agents, with their real-world experience, are far better equipped to handle culturally sensitive interactions. This is particularly important for global customer support, where understanding local customs and context is key to effective communication. We’ve also seen how AI falls short in language understanding. Del Taco, for example, introduced an AI-powered drive-through system that was supposed to streamline ordering. But it turned out that the AI wasn’t handling everything—human agents from a call center in the Philippines were managing the orders behind the scenes. Once again, the “AI solution” was actually relying heavily on human support. The Limits of AI in Language and Context AI may have made strides in natural language processing, but it’s still far from perfect. Accents, slang, and dialects often trip up AI systems, whereas human agents can adapt and respond flexibly. This limitation is particularly noticeable in regions with diverse languages and speaking styles, where human understanding and adaptability become essential. The Future Is Hybrid, Not Automated Instead of viewing AI as a replacement for human workers, we should be thinking about how AI can augment their capabilities. The most effective customer service systems will likely be hybrid models, where AI handles routine tasks, and humans step in for more complex or sensitive issues. This allows human agents to focus on higher-value interactions while letting AI handle the low-hanging fruit. Far from eliminating jobs, this hybrid model actually creates new opportunities for human workers. Customers Still Prefer Humans Despite all the advancements in AI, customer preferences tell a different story. Most people still want to speak to a real person when handling complex issues, like billing disputes or technical problems. AI simply can’t provide the emotional reassurance that a human agent can offer, and as long as that preference exists, call center workers will continue to be in demand. Ethical and Security Concerns Another key concern is trust. Many customers are reluctant to share sensitive information with an AI system, especially when it involves financial data or personal details. Humans, by contrast, can be held accountable for their actions, which provides an additional layer of security and comfort for customers. AI systems, on the other hand, can be prone to errors or manipulation, as demonstrated by the GM chatbot that was tricked into offering a car for a dollar. Call Center Jobs Are Changing, Not Disappearing The rise of AI doesn’t mean the end of call center jobs—it means those jobs are evolving. Rather than eliminating human agents, AI will likely change their roles, moving them toward more supervisory or quality assurance positions. Human agents will still be essential in overseeing AI systems, stepping in when needed, and providing the kind of nuanced, culturally aware, and emotionally intelligent service that AI can’t match. So, the next time someone tells you AI will replace call center workers, consider the examples of Amazon Fresh, Del Taco, and the GM chatbot incident. These cases make it clear that human workers are still the backbone of customer service, even when AI is involved. In the end, the future of customer service isn’t about replacing humans with machines—it’s about blending AI with human intelligence to provide the best possible experience for customers. AI may be good at handling basic queries, but when it comes to complex problems, cultural understanding, and emotional support, human agents are irreplaceable. Customer service is, and always will be, a people business. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com In recent years, there’s been a lot of hype about artificial intelligence (AI) revolutionizing industries and making certain jobs obsolete. One of the most frequent predictions is that AI will take over call centers, automating customer service entirely and leaving human agents behind. But this narrative is far too simplistic. In fact, next time you hear someone say AI will replace call center workers, it might be time to run—because the reality is much more complicated, and in many cases, AI is far from ready to take over. Yes, AI is making strides in areas like chatbots, virtual assistants, and automated customer support systems. However, this technology still has major limitations, especially when it comes to the human aspects of customer service. And recent examples have shown that even the most advanced AI systems still require human oversight. The Human Touch Still Matters The Human Touch Still Matters Customer service is fundamentally about human interaction. People need to feel heard, understood, and supported—especially when dealing with frustrating or sensitive issues. AI may be able to process large amounts of data, but it lacks the empathy and emotional intelligence that humans bring to customer service. For many customers, the ability to communicate with a real person who understands their situation is non-negotiable. Recent examples highlight just how crucial human intervention still is in AI-driven systems. For instance, Amazon Fresh promoted its AI-driven shopping experience as cutting-edge, but it turned out that much of the work was actually being done by humans . Specifically, an Indian business process outsourcing (BPO) team was watching customers via CCTV and stepping in when the AI couldn’t handle certain tasks. Far from replacing human workers, AI actually required their presence behind the scenes. much of the work was actually being done by humans AI Struggles with Complexity AI Struggles with Complexity While AI systems can handle routine inquiries and straightforward tasks, they often fall short when problems become complex or unexpected. Human agents, on the other hand, excel in creative problem-solving and thinking outside the box, something that AI simply isn’t capable of doing. One recent blunder involved a GM chatbot that was fooled by a customer into offering a Chevrolet Tahoe for $1. The AI-powered chatbot was tricked into providing an outrageous discount—a glaring reminder that AI systems can be easily manipulated and are often incapable of detecting subtle deception. In scenarios like these, human agents are essential for catching errors and ensuring that transactions run smoothly. AI, as this incident proves, is still vulnerable to making costly mistakes. was fooled by a customer Cultural and Contextual Understanding Is Critical Cultural and Contextual Understanding Is Critical Customer service also requires an understanding of cultural nuances and local contexts, which AI struggles to interpret. Human agents, with their real-world experience, are far better equipped to handle culturally sensitive interactions. This is particularly important for global customer support, where understanding local customs and context is key to effective communication. We’ve also seen how AI falls short in language understanding. Del Taco, for example, introduced an AI-powered drive-through system that was supposed to streamline ordering. But it turned out that the AI wasn’t handling everything—human agents from a call center in the Philippines were managing the orders behind the scenes. Once again, the “AI solution” was actually relying heavily on human support. AI-powered drive-through system The Limits of AI in Language and Context The Limits of AI in Language and Context AI may have made strides in natural language processing, but it’s still far from perfect. Accents, slang, and dialects often trip up AI systems, whereas human agents can adapt and respond flexibly. This limitation is particularly noticeable in regions with diverse languages and speaking styles, where human understanding and adaptability become essential. The Future Is Hybrid, Not Automated The Future Is Hybrid, Not Automated Instead of viewing AI as a replacement for human workers, we should be thinking about how AI can augment their capabilities. The most effective customer service systems will likely be hybrid models, where AI handles routine tasks, and humans step in for more complex or sensitive issues. This allows human agents to focus on higher-value interactions while letting AI handle the low-hanging fruit. Far from eliminating jobs, this hybrid model actually creates new opportunities for human workers. Customers Still Prefer Humans Customers Still Prefer Humans Despite all the advancements in AI, customer preferences tell a different story. Most people still want to speak to a real person when handling complex issues, like billing disputes or technical problems. AI simply can’t provide the emotional reassurance that a human agent can offer, and as long as that preference exists, call center workers will continue to be in demand. Ethical and Security Concerns Ethical and Security Concerns Another key concern is trust. Many customers are reluctant to share sensitive information with an AI system, especially when it involves financial data or personal details. Humans, by contrast, can be held accountable for their actions, which provides an additional layer of security and comfort for customers. AI systems, on the other hand, can be prone to errors or manipulation, as demonstrated by the GM chatbot that was tricked into offering a car for a dollar. Call Center Jobs Are Changing, Not Disappearing Call Center Jobs Are Changing, Not Disappearing The rise of AI doesn’t mean the end of call center jobs—it means those jobs are evolving. Rather than eliminating human agents, AI will likely change their roles, moving them toward more supervisory or quality assurance positions. Human agents will still be essential in overseeing AI systems, stepping in when needed, and providing the kind of nuanced, culturally aware, and emotionally intelligent service that AI can’t match. So, the next time someone tells you AI will replace call center workers, consider the examples of Amazon Fresh, Del Taco, and the GM chatbot incident. These cases make it clear that human workers are still the backbone of customer service, even when AI is involved. In the end, the future of customer service isn’t about replacing humans with machines—it’s about blending AI with human intelligence to provide the best possible experience for customers. AI may be good at handling basic queries, but when it comes to complex problems, cultural understanding, and emotional support, human agents are irreplaceable. Customer service is, and always will be, a people business. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com

Next Time You Hear Someone Say AI Will Replace Call Center Agents, Run

Table of Links Abstract and I. Introduction II. Preliminaries III. Proposed Design: IBis IV. Detailed Construction V. Implementation on DAML VI. Evaluation VII. Conclusion and References VII. CONCLUSION In this paper, we present IBIS, a blockchain-based data provenance, lineage, and copyright management system for AI models. IBIS provides evidence and limits power scope for iterative model retraining and fine-tuning processes by granting related licenses. We leverage blockchain-based multi-party signing capabilities to streamline the establishment of legally compliant licensing agreements between AI model owners and copyright holders. We also establish access control mechanisms to safeguard confidentiality by limiting access to authorized parties. Our system implementation is based on the Daml ledger model and Canton blockchain. Performance evaluations underscore the feasibility and scalability of IBIS across varying user, dataset, model, and license workloads. Potential future work includes exploring different on-chain data structures to optimize the performance of graph traversals, and extending IBIS to cover additional stages in AI lifecycle, such as data cleaning, model testing, and model explanation. REFERENCES [1] R. Bommasani et al., “On the opportunities and risks of foundation models,” arXiv preprint arXiv:2108.07258, 2021. [2] W. X. Zhao et al., “A survey of large language models,” arXiv preprint arXiv:2303.18223, 2023. [3] Y. Zhu et al., “Large language models for information retrieval: A survey,” arXiv preprint arXiv:2308.07107, 2023. [4] L. Bonifacio, H. Abonizio, M. Fadaee, and R. Nogueira, “Inpars: Data augmentation for information retrieval using large language models,” arXiv preprint arXiv:2202.05144, 2022. [5] J. Li et al., “Pretrained language models for text generation: A survey,” arXiv preprint arXiv:2201.05273, 2022. [6] J. Chen et al., “Benchmarking large language models in retrievalaugmented generation,” in AAAI, 2024. [7] N. Carlini et al., “Extracting training data from large language models,” in USENIX Security, 2021, pp. 2633–2650. [8] J. Hoffmann and thers, “An empirical analysis of compute-optimal large language model training,” NIPS, vol. 35, pp. 30 016–30 030, 2022. [9] T. Chu, Z. Song, and C. Yang, “How to protect copyright data in optimization of large language models?” in AAAI, vol. 38, no. 16, 2024, pp. 17 871–17 879. [10] N. Vyas, S. M. Kakade, and B. Barak, “On provable copyright protection for generative models,” in Int. Conf. on Machine Learning (ICML). PMLR, 2023, pp. 35 277–35 299. [11] Z. Yu, Y. Wu, N. Zhang, C. Wang, Y. Vorobeychik, and C. Xiao, “Codeipprompt: intellectual property infringement assessment of code language models,” in Int. Conf. on Machine Learning (ICML). PMLR, 2023, pp. 40 373–40 389. [12] Q. Lu et al., “Developing responsible chatbots for financial services: A pattern-oriented responsible AI engineering approach,” IEEE Intelligent Systems, 2023. [13] Q. Lu, L. Zhu, X. Xu, J. Whittle, D. Zowghi, and A. Jacquet, “Operationalizing responsible AI at scale: CSIRO data61’s pattern-oriented responsible AI engineering approach,” Communications of the ACM (CACM), vol. 66, no. 7, pp. 64–66, 2023. [14] A. Power, “Licensing agreements,” Miss. LJ, vol. 42, p. 169, 1970. [15] M. Benjamin, P. Gagnon, N. Rostamzadeh, C. Pal, Y. Bengio, and A. Shee, “Towards standardization of data licenses: The montreal data license,” arXiv preprint arXiv:1903.12262, 2019. [16] D. Contractor et al., “Behavioral use licensing for responsible AI,” in ACM Conf. on Fairness, Accountability, and Transparency, 2022, pp. 778–788. [17] D. Siddarth, D. Acemoglu, D. Allen, K. Crawford, J. Evans, M. Jordan, and E. Weyl, “How AI fails us,” arXiv preprint arXiv:2201.04200, 2021. [18] R. Li et al., “How do smart contracts benefit security protocols?” arXiv preprint arXiv:2202.08699, 2022. [19] L. T. Nguyen et al., “Blockchain-empowered trustworthy data sharing: Fundamentals, applications, and challenges,” arXiv preprint arXiv:2303.06546, 2023. [20] X. Xu, I. Weber, and M. Staples, Architecture for Blockchain Applications. Springer, 2019. [21] W. Zhang et al., “Blockchain-based distributed compliance in multinational corporations’ cross-border intercompany transactions,” in Advances in Information and Communication Networks. Cham: Springer Int. Publishing, 2019, pp. 304–320. [22] T. Scott, A. L. Post, J. Quick, and S. Rafiqi, “Evaluating feasibility of blockchain application for DSCSA compliance,” SMU Data Science Review, vol. 1, no. 2, 2018. [23] M. Allena, “Blockchain technology and regulatory compliance: Towards a cooperative supervisory model,” European Review of Digital Administration & Law, pp. 37–43, 2022. [24] O. Ural and K. Yoshigoe, “Survey on blockchain-enhanced machine learning,” IEEE Access, vol. 11, pp. 145 331–145 362, 2023. [25] A. A. Hussain and F. Al-Turjman, “Artificial intelligence and blockchain: A review,” ETT, vol. 32, no. 9, p. e4268, 2021. [26] Y. Liu, F. R. Yu, X. Li, H. Ji, and V. C. Leung, “Blockchain and machine learning for communications and networking systems,” IEEE Communications Surveys & Tutorials, 2020. [27] A. Bernauer et al., “Daml: A smart contract language for securely automating real-world multi-party business workflows,” 2023. [28] D. A. C. Team, “Canton: A Daml based ledger interoperability protocols,” Digital Asset, Tech. Rep., 2020. [Online]. Available: https:// www.digitalasset.com/hubfs/Canton/canton-whitepaper.pdf?hsLang=en [29] D. Kreuzberger, N. Kuhl, and S. Hirschl, “Machine learning operations ¨ (MLOps): Overview, definition, and architecture,” IEEE Access, vol. 11, pp. 31 866–31 879, 2023. [30] J. Litman, “What notice did,” Boston University Law Review, vol. 96, pp. 717–744, 2016. [31] Q. Wang, R. Li, Q. Wang, and S. Chen, “Non-fungible token (NFT): Overview, evaluation, opportunities and challenges,” arXiv preprint arXiv:2105.07447, 2021. [32] C. Meurisch and M. Muhlh ¨ auser, “Data protection in AI services: A ¨ survey,” ACM Computing Surveys, 2021. [33] B. Gedik and L. Liu, “Protecting location privacy with personalized k-anonymity: Architecture and algorithms,” IEEE Trans. on Mobile Computing (TMC), vol. 7, no. 1, pp. 1–18, 2007. [34] D. Xu, S. Yuan, and X. Wu, “Achieving differential privacy and fairness in logistic regression,” in Companion Proc. of The World Wide Web Conf. (WWW), 2019, pp. 594–599. [35] J. Zhang, Z. Gu, J. Jang, H. Wu, M. P. Stoecklin, H. Huang, and I. Molloy, “Protecting intellectual property of deep neural networks with watermarking,” in AsiaCCS, 2018, pp. 159–172. [36] R. Gilad-Bachrach et al., “Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy,” in ICML. PMLR, 2016, pp. 201–210. [37] P. Mohassel and Y. Zhang, “Secureml: A system for scalable privacypreserving machine learning,” in SP. IEEE, 2017, pp. 19–38. [38] B. D. Rouhani, M. S. Riazi, and F. Koushanfar, “Deepsecure: Scalable provably-secure deep learning,” in Proc. of the Annual Design Automation Conf. (DAC), 2018, pp. 1–6. [39] R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” in CCS, 2015, pp. 1310–1321. [40] S. Servia-Rodr´ıguez, L. Wang, J. R. Zhao, R. Mortier, and H. Haddadi, “Privacy-preserving personal model training,” in IEEE/ACM Int. Conf. on Internet-of-Things Design and Implementation (IoTDI). IEEE, 2018, pp. 153–164. [41] W. Liang et al., “Circuit copyright blockchain: Blockchain-based homomorphic encryption for IP circuit protection,” IEEE Trans. on Emerging Topics in Computing (TETC), vol. 9, no. 3, pp. 1410–1420, 2020. [42] Y. Liu, H. Du et al., “Blockchain-empowered lifecycle management for AI-generated content products in edge networks,” IEEE Wireless Communications, 2024. [43] A. Savelyev, “Copyright in the blockchain era: Promises and challenges,” Computer Law & Security Review, vol. 34, no. 3, pp. 550–561, 2018. [44] N. Jing, Q. Liu, and V. Sugumaran, “A blockchain-based code copyright management system,” Information Processing & Management, vol. 58, no. 3, p. 102518, 2021. [45] B. Wang et al., “Image copyright protection based on blockchain and zero-watermark,” TNSE, vol. 9, no. 4, pp. 2188–2199, 2022. [46] G. Yu et al., “Ironforge: An open, secure, fair, decentralized federated learning,” TNNLS, 2023. [47] A. Borzunov et al., “Distributed inference and fine-tuning of large language models over the internet,” NIPS, vol. 36, 2024. [48] X. Liu et al., “Decentralized federated unlearning on blockchain,” arXiv preprint arXiv:2402.16294, 2024. [49] Y. Gao, Z. Song, and J. Yin, “Gradientcoin: A peer-to-peer decentralized large language models,” arXiv.2308.10502, 2023. [50] E. Androulaki et al., “Hyperledger Fabric: A distributed operating system for permissioned blockchains,” in EuroSys, 2018, pp. 1–15. Authors:
(1) Yilin Sai, CSIRO Data61 and The University of New South Wales, Sydney, Australia;
(2) Qin Wang, CSIRO Data61 and The University of New South Wales, Sydney, Australia;
(3) Guangsheng Yu, CSIRO Data61;
(4) H.M.N. Dilum Bandara, CSIRO Data61 and The University of New South Wales, Sydney, Australia;
(5) Shiping Chen, CSIRO Data61 and The University of New South Wales, Sydney, Australia. This paper is available on arxiv under CC BY 4.0 DEED license. Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Preliminaries II. Preliminaries III. Proposed Design: IBis III. Proposed Design: IBis IV. Detailed Construction IV. Detailed Construction V. Implementation on DAML V. Implementation on DAML VI. Evaluation VI. Evaluation VII. Conclusion and References VII. Conclusion and References VII. CONCLUSION In this paper, we present IBIS, a blockchain-based data provenance, lineage, and copyright management system for AI models. IBIS provides evidence and limits power scope for iterative model retraining and fine-tuning processes by granting related licenses. We leverage blockchain-based multi-party signing capabilities to streamline the establishment of legally compliant licensing agreements between AI model owners and copyright holders. We also establish access control mechanisms to safeguard confidentiality by limiting access to authorized parties. Our system implementation is based on the Daml ledger model and Canton blockchain. Performance evaluations underscore the feasibility and scalability of IBIS across varying user, dataset, model, and license workloads. Potential future work includes exploring different on-chain data structures to optimize the performance of graph traversals, and extending IBIS to cover additional stages in AI lifecycle, such as data cleaning, model testing, and model explanation. REFERENCES [1] R. Bommasani et al., “On the opportunities and risks of foundation models,” arXiv preprint arXiv:2108.07258, 2021. [2] W. X. Zhao et al., “A survey of large language models,” arXiv preprint arXiv:2303.18223, 2023. [3] Y. Zhu et al., “Large language models for information retrieval: A survey,” arXiv preprint arXiv:2308.07107, 2023. [4] L. Bonifacio, H. Abonizio, M. Fadaee, and R. Nogueira, “Inpars: Data augmentation for information retrieval using large language models,” arXiv preprint arXiv:2202.05144, 2022. [5] J. Li et al., “Pretrained language models for text generation: A survey,” arXiv preprint arXiv:2201.05273, 2022. [6] J. Chen et al., “Benchmarking large language models in retrievalaugmented generation,” in AAAI, 2024. [7] N. Carlini et al., “Extracting training data from large language models,” in USENIX Security, 2021, pp. 2633–2650. [8] J. Hoffmann and thers, “An empirical analysis of compute-optimal large language model training,” NIPS, vol. 35, pp. 30 016–30 030, 2022. [9] T. Chu, Z. Song, and C. Yang, “How to protect copyright data in optimization of large language models?” in AAAI, vol. 38, no. 16, 2024, pp. 17 871–17 879. [10] N. Vyas, S. M. Kakade, and B. Barak, “On provable copyright protection for generative models,” in Int. Conf. on Machine Learning (ICML). PMLR, 2023, pp. 35 277–35 299. [11] Z. Yu, Y. Wu, N. Zhang, C. Wang, Y. Vorobeychik, and C. Xiao, “Codeipprompt: intellectual property infringement assessment of code language models,” in Int. Conf. on Machine Learning (ICML). PMLR, 2023, pp. 40 373–40 389. [12] Q. Lu et al., “Developing responsible chatbots for financial services: A pattern-oriented responsible AI engineering approach,” IEEE Intelligent Systems, 2023. [13] Q. Lu, L. Zhu, X. Xu, J. Whittle, D. Zowghi, and A. Jacquet, “Operationalizing responsible AI at scale: CSIRO data61’s pattern-oriented responsible AI engineering approach,” Communications of the ACM (CACM), vol. 66, no. 7, pp. 64–66, 2023. [14] A. Power, “Licensing agreements,” Miss. LJ, vol. 42, p. 169, 1970. [15] M. Benjamin, P. Gagnon, N. Rostamzadeh, C. Pal, Y. Bengio, and A. Shee, “Towards standardization of data licenses: The montreal data license,” arXiv preprint arXiv:1903.12262, 2019. [16] D. Contractor et al., “Behavioral use licensing for responsible AI,” in ACM Conf. on Fairness, Accountability, and Transparency, 2022, pp. 778–788. [17] D. Siddarth, D. Acemoglu, D. Allen, K. Crawford, J. Evans, M. Jordan, and E. Weyl, “How AI fails us,” arXiv preprint arXiv:2201.04200, 2021. [18] R. Li et al., “How do smart contracts benefit security protocols?” arXiv preprint arXiv:2202.08699, 2022. [19] L. T. Nguyen et al., “Blockchain-empowered trustworthy data sharing: Fundamentals, applications, and challenges,” arXiv preprint arXiv:2303.06546, 2023. [20] X. Xu, I. Weber, and M. Staples, Architecture for Blockchain Applications. Springer, 2019. [21] W. Zhang et al., “Blockchain-based distributed compliance in multinational corporations’ cross-border intercompany transactions,” in Advances in Information and Communication Networks. Cham: Springer Int. Publishing, 2019, pp. 304–320. [22] T. Scott, A. L. Post, J. Quick, and S. Rafiqi, “Evaluating feasibility of blockchain application for DSCSA compliance,” SMU Data Science Review, vol. 1, no. 2, 2018. [23] M. Allena, “Blockchain technology and regulatory compliance: Towards a cooperative supervisory model,” European Review of Digital Administration & Law, pp. 37–43, 2022. [24] O. Ural and K. Yoshigoe, “Survey on blockchain-enhanced machine learning,” IEEE Access, vol. 11, pp. 145 331–145 362, 2023. [25] A. A. Hussain and F. Al-Turjman, “Artificial intelligence and blockchain: A review,” ETT, vol. 32, no. 9, p. e4268, 2021. [26] Y. Liu, F. R. Yu, X. Li, H. Ji, and V. C. Leung, “Blockchain and machine learning for communications and networking systems,” IEEE Communications Surveys & Tutorials, 2020. [27] A. Bernauer et al., “Daml: A smart contract language for securely automating real-world multi-party business workflows,” 2023. [28] D. A. C. Team, “Canton: A Daml based ledger interoperability protocols,” Digital Asset, Tech. Rep., 2020. [Online]. Available: https:// www.digitalasset.com/hubfs/Canton/canton-whitepaper.pdf?hsLang=en [29] D. Kreuzberger, N. Kuhl, and S. Hirschl, “Machine learning operations ¨ (MLOps): Overview, definition, and architecture,” IEEE Access, vol. 11, pp. 31 866–31 879, 2023. [30] J. Litman, “What notice did,” Boston University Law Review, vol. 96, pp. 717–744, 2016. [31] Q. Wang, R. Li, Q. Wang, and S. Chen, “Non-fungible token (NFT): Overview, evaluation, opportunities and challenges,” arXiv preprint arXiv:2105.07447, 2021. [32] C. Meurisch and M. Muhlh ¨ auser, “Data protection in AI services: A ¨ survey,” ACM Computing Surveys, 2021. [33] B. Gedik and L. Liu, “Protecting location privacy with personalized k-anonymity: Architecture and algorithms,” IEEE Trans. on Mobile Computing (TMC), vol. 7, no. 1, pp. 1–18, 2007. [34] D. Xu, S. Yuan, and X. Wu, “Achieving differential privacy and fairness in logistic regression,” in Companion Proc. of The World Wide Web Conf. (WWW), 2019, pp. 594–599. [35] J. Zhang, Z. Gu, J. Jang, H. Wu, M. P. Stoecklin, H. Huang, and I. Molloy, “Protecting intellectual property of deep neural networks with watermarking,” in AsiaCCS, 2018, pp. 159–172. [36] R. Gilad-Bachrach et al., “Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy,” in ICML. PMLR, 2016, pp. 201–210. [37] P. Mohassel and Y. Zhang, “Secureml: A system for scalable privacypreserving machine learning,” in SP. IEEE, 2017, pp. 19–38. [38] B. D. Rouhani, M. S. Riazi, and F. Koushanfar, “Deepsecure: Scalable provably-secure deep learning,” in Proc. of the Annual Design Automation Conf. (DAC), 2018, pp. 1–6. [39] R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” in CCS, 2015, pp. 1310–1321. [40] S. Servia-Rodr´ıguez, L. Wang, J. R. Zhao, R. Mortier, and H. Haddadi, “Privacy-preserving personal model training,” in IEEE/ACM Int. Conf. on Internet-of-Things Design and Implementation (IoTDI). IEEE, 2018, pp. 153–164. [41] W. Liang et al., “Circuit copyright blockchain: Blockchain-based homomorphic encryption for IP circuit protection,” IEEE Trans. on Emerging Topics in Computing (TETC), vol. 9, no. 3, pp. 1410–1420, 2020. [42] Y. Liu, H. Du et al., “Blockchain-empowered lifecycle management for AI-generated content products in edge networks,” IEEE Wireless Communications, 2024. [43] A. Savelyev, “Copyright in the blockchain era: Promises and challenges,” Computer Law & Security Review, vol. 34, no. 3, pp. 550–561, 2018. [44] N. Jing, Q. Liu, and V. Sugumaran, “A blockchain-based code copyright management system,” Information Processing & Management, vol. 58, no. 3, p. 102518, 2021. [45] B. Wang et al., “Image copyright protection based on blockchain and zero-watermark,” TNSE, vol. 9, no. 4, pp. 2188–2199, 2022. [46] G. Yu et al., “Ironforge: An open, secure, fair, decentralized federated learning,” TNNLS, 2023. [47] A. Borzunov et al., “Distributed inference and fine-tuning of large language models over the internet,” NIPS, vol. 36, 2024. [48] X. Liu et al., “Decentralized federated unlearning on blockchain,” arXiv preprint arXiv:2402.16294, 2024. [49] Y. Gao, Z. Song, and J. Yin, “Gradientcoin: A peer-to-peer decentralized large language models,” arXiv.2308.10502, 2023. [50] E. Androulaki et al., “Hyperledger Fabric: A distributed operating system for permissioned blockchains,” in EuroSys, 2018, pp. 1–15. Authors: (1) Yilin Sai, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (2) Qin Wang, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (3) Guangsheng Yu, CSIRO Data61; (4) H.M.N. Dilum Bandara, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (5) Shiping Chen, CSIRO Data61 and The University of New South Wales, Sydney, Australia. Authors: Authors: (1) Yilin Sai, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (2) Qin Wang, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (3) Guangsheng Yu, CSIRO Data61; (4) H.M.N. Dilum Bandara, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (5) Shiping Chen, CSIRO Data61 and The University of New South Wales, Sydney, Australia. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

Introducing IBIS for Efficient Data Provenance and Licensing Management

Anti-Bitcoin: What Does BTC Need to Be to Actually Replace Fiat?

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Google’s TGIF meetings have long been a hallmark of open dialogue and transparency within the company. These monthly gatherings allowed employees to ask tough questions and hear straight answers from the top brass, including CEO Sundar Pichai. However, recent changes to these meetings are leaving many employees disappointed. Earlier this year, Google introduced a new AI tool called Ask, which streamlines the question-and-answer process at TGIF meetings. While the intention behind this move was to make things more efficient, many Googlers feel the AI is doing more than summarizing inquiries — it’s softening them. Employees are now questioning whether Ask was designed to avoid addressing the hard-hitting issues they want leadership to confront. Google’s AI Tool: A Shift in TGIF Meetings Google’s new AI tool has significantly changed how its TGIF meetings operate. Previously, employees could submit their queries through a platform called Dory, where others could upvote the most pressing or popular inquiries. Leadership would then respond to the most upvoted questions, even if they were on the more uncomfortable side. However, Google recently replaced Dory with Ask, an AI-powered system that now consolidates and summarizes employee topics for discussion. According to Google, it meant for this change to streamline the process, avoid repetitiveness and allow for leaders to address more inquiries. On paper, it seems like a win for efficiency. The company even claims that engagement has increased, with twice as many employees now participating in submitting and voting on questions. Not everyone is convinced. Some employees feel the Ask tool is doing more than making meetings run smoothly. The AI’s summaries soften the tone of more pointed questions, removing the bite from inquiries that were once direct and challenging. Critics argue that this allows leadership to sidestep issues, replacing the once-candid discussions with more controlled, sanitized responses. For many, the meetings have become less engaging. Some workers have even stopped attending, feeling that the opportunity to ask tough questions no longer exists. As a result, there is a growing apprehension that Google’s leadership is using Ask to manage the narrative. The Broader Implications of AI in Internal Communications The introduction of Google’s Ask tool raises an important question — could AI tools like this reshape internal communication in companies beyond Google? While Ask aims to improve efficiency, its ability to soften and filter doubts may be a potential risk for businesses everywhere. There’s no doubt that event technology is a huge time- and money-saver. Almost 90% of businesses using event tech save around 200 hours a year — a huge chunk of employees’ time that’s better spent on the more creative aspects of their role. However, there’s a real anxiety that they could use these tools to control employee voices. AI’s capacity to summarize and paraphrase queries can certainly help avoid redundancies. However, it also opens the door for leadership to sidestep challenging topics. By softening the language — these tools can make tough questions easier to answer — but that comes at the cost of transparency. In workplaces where employees rely on open forums to raise matters, AI could lead to more scripted dialogues. Workers may feel discouraged from asking difficult questions if they believe AI will water down their concerns. This could result in a breakdown of trust between team members and leadership, weakening the company culture. Moreover, organizations risk alienating their workforce as companies increasingly integrate AI to handle more aspects of communication. Workers may view these tools as a buffer to protect management rather than to foster more openness. With transparency crucial for employee engagement, the misuse of AI could create a culture of frustration and poor communication, costing establishmentsan average of $1.2 trillion every year. The Ethics of Rephrasing Employee Concerns Tools like Ask raise serious doubts about transparency and fairness in internal dialogue. The main concern with AI-driven systems is that they could keep team members from raising genuine concerns. By summarizing or rephrasing inquiries, these tools risk stripping away the urgency or tone of critical feedback. This could create an environment that shields leaders from the realities of employee sentiment, making it harder for real issues to surface. Research has supported these concerns. Studies have shown that some machine learning (ML) algorithms lack transparency and consistency — often known as “black box” algorithms — which prevent any detection of biases. This raises issues about the accountability and responsibility of companies that use such systems. In Google’s case, the Ask tool could prevent leadership from understanding the true depth of employee concerns, especially regarding sensitive questions. Moreover, a growing body of research indicates that organizations must be prepared to handle these ethical dilemmas. Studies found that 84% of HR executives need more direction to ensure that new AI tools are fair and unbiased. When leadership relies on AI to filter employee questions, is it truly engaging with its workforce? AI is an excellent tool for managing loads of communication. Yet, companies must avoid using it to distance themselves from the realities their employees face. How Businesses Can Use AI While Maintaining Transparent Internal Communication When using AI, businesses should prioritize transparency, ensuring that employees feel heard clearly without interference from algorithms that may skew their original intent. With that in mind, here are a few ways to maintain transparency in meetings when using AI tools like Ask. 1. Establish Channels for Feedback While AI revolutionizes how companies operate, employees should still have direct avenues to express concerns about how they are using these tools. Businesses should encourage workers to provide input on how AI systems impact communication. This strategy will help leaders realize the impact the technology is creating, whether enhancing transparency or eroding it. As such, they can make adjustments to address any lack of openness or fairness before AI tools compromise trust. 2. Implement Human Oversight Relying solely on AI to post employee queries can be risky, as 71% of surveyed workers cited they would lose trust in AI if it produced inaccurate outputs consistently. That is why human oversight is crucial. Business leaders should integrate a system where human supervisors review sensitive questions to ensure AI is not misrepresenting any important issues. They could also give employees the autonomy to escalate their queries if they feel the AI’s processes are inadequate. By enabling these options, executives can still take accountability in handling delicate matters. 3. Enable AI Tools to Provide Balanced Responses Organizational leaders should also ensure responses are complete and fair. Doing so prevents the perception that they are using these tools to dodge challenging topics. This tactic also guarantees more purposeful discussions as AI provides a balanced representation of the issues, resulting in more trust and open communication. Balancing Efficiency and Transparency As more businesses use AI tools to manage internal communications, it is vital to ensure these technologies do not come at the cost of transparency. AI may be able to streamline meetings, but companies must be careful not to let these systems dilute employees’ concerns. Instead, they should strike a balance to preserve workers’ trust and ensure the technology builds connection rather than division. Google’s TGIF meetings have long been a hallmark of open dialogue and transparency within the company. These monthly gatherings allowed employees to ask tough questions and hear straight answers from the top brass, including CEO Sundar Pichai. However, recent changes to these meetings are leaving many employees disappointed. Earlier this year, Google introduced a new AI tool called Ask, which streamlines the question-and-answer process at TGIF meetings. While the intention behind this move was to make things more efficient, many Googlers feel the AI is doing more than summarizing inquiries — it’s softening them. Employees are now questioning whether Ask was designed to avoid addressing the hard-hitting issues they want leadership to confront. Google’s AI Tool: A Shift in TGIF Meetings Google’s new AI tool has significantly changed how its TGIF meetings operate. Previously, employees could submit their queries through a platform called Dory, where others could upvote the most pressing or popular inquiries. Leadership would then respond to the most upvoted questions, even if they were on the more uncomfortable side. However, Google recently replaced Dory with Ask, an AI-powered system that now consolidates and summarizes employee topics for discussion. According to Google, it meant for this change to streamline the process, avoid repetitiveness and allow for leaders to address more inquiries. On paper, it seems like a win for efficiency. The company even claims that engagement has increased, with twice as many employees now participating in submitting and voting on questions. twice as many employees now participating twice as many employees now participating Not everyone is convinced. Some employees feel the Ask tool is doing more than making meetings run smoothly. The AI’s summaries soften the tone of more pointed questions, removing the bite from inquiries that were once direct and challenging. Critics argue that this allows leadership to sidestep issues, replacing the once-candid discussions with more controlled, sanitized responses. For many, the meetings have become less engaging. Some workers have even stopped attending, feeling that the opportunity to ask tough questions no longer exists. As a result, there is a growing apprehension that Google’s leadership is using Ask to manage the narrative. The Broader Implications of AI in Internal Communications The introduction of Google’s Ask tool raises an important question — could AI tools like this reshape internal communication in companies beyond Google? While Ask aims to improve efficiency, its ability to soften and filter doubts may be a potential risk for businesses everywhere. There’s no doubt that event technology is a huge time- and money-saver. Almost 90% of businesses using event tech save around 200 hours a year — a huge chunk of employees’ time that’s better spent on the more creative aspects of their role. However, there’s a real anxiety that they could use these tools to control employee voices. AI’s capacity to summarize and paraphrase queries can certainly help avoid redundancies. 90% of businesses using event tech 90% of businesses using event tech However, it also opens the door for leadership to sidestep challenging topics. By softening the language — these tools can make tough questions easier to answer — but that comes at the cost of transparency. In workplaces where employees rely on open forums to raise matters, AI could lead to more scripted dialogues. Workers may feel discouraged from asking difficult questions if they believe AI will water down their concerns. This could result in a breakdown of trust between team members and leadership, weakening the company culture. Moreover, organizations risk alienating their workforce as companies increasingly integrate AI to handle more aspects of communication. Workers may view these tools as a buffer to protect management rather than to foster more openness. With transparency crucial for employee engagement, the misuse of AI could create a culture of frustration and poor communication, costing establishments an average of $1.2 trillion every year. an average of $1.2 trillion an average of $1.2 trillion The Ethics of Rephrasing Employee Concerns The Ethics of Rephrasing Employee Concerns Tools like Ask raise serious doubts about transparency and fairness in internal dialogue. The main concern with AI-driven systems is that they could keep team members from raising genuine concerns. By summarizing or rephrasing inquiries, these tools risk stripping away the urgency or tone of critical feedback. This could create an environment that shields leaders from the realities of employee sentiment, making it harder for real issues to surface. Research has supported these concerns. Studies have shown that some machine learning (ML) algorithms lack transparency and consistency — often known as “black box” algorithms — which prevent any detection of biases. This raises issues about the accountability and responsibility of companies that use such systems. In Google’s case, the Ask tool could prevent leadership from understanding the true depth of employee concerns, especially regarding sensitive questions. from understanding the true depth from understanding the true depth Moreover, a growing body of research indicates that organizations must be prepared to handle these ethical dilemmas. Studies found that 84% of HR executives need more direction to ensure that new AI tools are fair and unbiased. 84% of HR executives 84% of HR executives When leadership relies on AI to filter employee questions, is it truly engaging with its workforce? AI is an excellent tool for managing loads of communication. Yet, companies must avoid using it to distance themselves from the realities their employees face. How Businesses Can Use AI While Maintaining Transparent Internal Communication How Businesses Can Use AI While Maintaining Transparent Internal Communication When using AI, businesses should prioritize transparency, ensuring that employees feel heard clearly without interference from algorithms that may skew their original intent. With that in mind, here are a few ways to maintain transparency in meetings when using AI tools like Ask. 1. Establish Channels for Feedback While AI revolutionizes how companies operate, employees should still have direct avenues to express concerns about how they are using these tools. Businesses should encourage workers to provide input on how AI systems impact communication. This strategy will help leaders realize the impact the technology is creating, whether enhancing transparency or eroding it. As such, they can make adjustments to address any lack of openness or fairness before AI tools compromise trust. 2. Implement Human Oversight 2. Implement Human Oversight Relying solely on AI to post employee queries can be risky, as 71% of surveyed workers cited they would lose trust in AI if it produced inaccurate outputs consistently. That is why human oversight is crucial. Business leaders should integrate a system where human supervisors review sensitive questions to ensure AI is not misrepresenting any important issues. 71% of surveyed workers 71% of surveyed workers They could also give employees the autonomy to escalate their queries if they feel the AI’s processes are inadequate. By enabling these options, executives can still take accountability in handling delicate matters. 3. Enable AI Tools to Provide Balanced Responses 3. Enable AI Tools to Provide Balanced Responses Organizational leaders should also ensure responses are complete and fair. Doing so prevents the perception that they are using these tools to dodge challenging topics. This tactic also guarantees more purposeful discussions as AI provides a balanced representation of the issues, resulting in more trust and open communication. Balancing Efficiency and Transparency Balancing Efficiency and Transparency As more businesses use AI tools to manage internal communications, it is vital to ensure these technologies do not come at the cost of transparency. AI may be able to streamline meetings, but companies must be careful not to let these systems dilute employees’ concerns. Instead, they should strike a balance to preserve workers’ trust and ensure the technology builds connection rather than division.

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