AI Reflection Project Cycle and Ethics Class 9 Notes: Important Points
AI Reflection Project Cycle and Ethics Class 9 Notes
This chapter explains the following topics
What is Artificial Intelligence?
Artificial intelligence is a technology that refers to the development of such machines which can perform such task that required Human Intelligence.
When a machine is Artificial Intelligent?
A machine is said to be artificial Intelligent when it
- Mimics human intelligence
- Can solve real-world problems
- Improves on its own from past experiences
- Can predict and make decisions on its own
How to make machine intelligent?
A machine can become intelligent with the help of Data and Algorithm. In other words we can say that a machine become intelligent by training with Data and Algorithm.
Understanding with FUN
Games are an integral part of our culture. People across the world participate in different kinds of games as a form of social interaction, competition, and enjoyment.
The basic principle of every game is rule-setting and following the rules.
Below are the list of three online resources to play games and experience the power of AI.
1. Rock, Paper & Scissors: A game based on Data for AI where the machine tries to predict the next move of the participant. It is a game where the machine tries to win ahead by learning from the participant’s previous moves.
Visit https://next.rockpaperscissors.ai/ to play the game online.
2. Semantris: A game based on Natural Language Processing is a set of word association games powered by natural language understanding technology. Each time you enter a clue, the AI looks at all the words in play and chooses the ones it thinks are most related.
Visit https://research.google.com/semantris/ to experience the magic online.
3. Quick, Draw: A game based on Computer Vision developed by Google that challenges players to draw a picture of an object or idea and then uses a neural network artificial intelligence to guess what the drawings represent.
Visit https://quickdraw.withgoogle.com/ to play the game online
Domains of AI
Depending on the type of data, we can divide AI into different domains:
Computer Vision, is an AI domain works with videos and images enabling machines to interpret and understand visual information.
Natural Language Processing (NLP) is an AI domain focused on textual data enabling machines to comprehend, generate, and manipulate human language.
Statistical Data refers to statistical techniques to analyze, interpret and draw insights from numerical/tabular data.
Some AI Applications
1. Face Lock in Smartphones
The front camera detects and captures the face and saves its features during initiation. Next time onwards, whenever the features match, the phone is unlocked.
2. Smart assistants
Smart assistants like Apple’s Siri and Amazon’s Alexa recognize and understand and then provide a useful response.
3. Fraud and Risk Detection
Finance companies were fed with bad debts and losses every year. They decided to bring in data scientists to rescue them from losses. Over the years, banking companies learned to divide and conquer data via customer profiling, past expenditures, and other essential variables to analyse the probabilities of risk and default.
4. Medical Imaging
The application is used to read and convert 2D scan images into interactive 3D models that enable medical professionals to gain a detailed understanding of a patient’s health condition.
AI Project Cycle
AI project cycle is the cyclical process followed to complete an AI project. AI project cycle takes us through different steps involved in a project. AI project cycle helps us:
- to create better AI projects easily
- to create AI projects faster
- to understand the process
Steps of AI Project Cycle
1. What is Problem Scoping?
Identifying a problem and having a vision to solve it, is called Problem Scoping. Scoping a problem is not that easy as we need to have a deeper understanding so that the picture becomes clearer while we are working to solve it. So we use the 4Ws Problem Canvas to understand the problem in a better way.
What is 4Ws Problem Canvas?
The 4Ws Problem canvas helps in identifying the key elements related to the problem. The 4Ws are :
- Who
- What
- Where
- Why
1. Who? : This block helps in analysing the people who are getting affected directly or indirectly due to a problem. Under this, we find out who are the ‘Stakeholders’ (those people who face this problem and would be benefitted with the solution) to this problem? Below are the questions that we need to discuss under this block.
- Who are the stakeholders?
- What do you know about them?
2. What? : This block helps to determine the nature of the problem. What is the problem and how do we know that it is a problem? Under this block, we also gather evidence to prove that the problem you have selected actually exists. Below are the questions that we need to discuss under this block.
- What is the problem?
- How do you know that it is a problem?
3. Where? : This block will help us to look into the situation in which the problem arises, the context of it, and the locations where it is prominent. Here is the Where Canvas:
- What is the context/situation in which the stakeholders experience the problem?
- Where is the problem located?
4. Why? : In the “Why” canvas, we think about the benefits which the stakeholders would get from the solution and how it will benefit them as well as the society. Below are the questions that we need to discuss under this block.
- Why will this solution be of value to the stakeholders?
- How will the solution improve their situation?
After filling the 4Ws Problem canvas, you now need to summarise all the cards into one template. The Problem Statement Template helps us to summarise all the key points into one single Template so that in future, whenever there is a need to look back at the basis of the problem, we can take a look at the Problem Statement Template and understand the key elements of it.
Problem Statement Template
2. Data Acquisition
This stage of AI Project Cycle is about acquiring data for the project.
What is Data?
Data can be a piece of information or facts and statistics collected together for reference or analysis. Whenever we want an AI project to be able to predict an output, we need to train it first using data.
For example, If we want to make an AI system which can predict the salary of any employee based on his previous salaries, you would feed the data of his previous salaries into the machine. This is the data with which the machine can be trained. Now, once it is ready, it will predict his next salary efficiently. The previous salary data here is known as Training Data while the next salary prediction data set is known as the Testing Data.
For better efficiency of an AI project, the Training data needs to be relevant and authentic.
Data Features
Data features refer to the type of data you want to collect. In our previous example, data features would be salary amount, increment percentage, increment period, bonus, etc.
Acquiring Data from reliable sources
After finding out the data features, we now need to acquire the same. There are various sources from where data can be acquired, some of them are given below:
- Surveys
- Web Scraping
- Sensors
- Cameras
- Observation
- API(Application Program Interface)
Sometimes, we use internet to acquire data for our project. Such data might not be authentic as its accuracy cannot be proved. So it is mandatory to find a reliable source of data from where some authentic information can be taken. At the same time, we should keep in mind that the data which we collect is open-sourced and not someone’s property. Extracting private data can be an offense. One of the most reliable and authentic sources of information are the open-sourced websites hosted by the government.
Some of the open-sourced Govt. portals are: data.gov.in, india.gov.in
List down ways of acquiring data for a project below:
- Collecting new data
- Converting/transforming legacy data
- Sharing/exchanging data
- Purchasing data.
System Maps
System Maps help us to find relationships between different elements of the problem which we have scoped. It helps us in strategizing the solution for achieving the goal of our project. Here is an example of a System very familiar to you – Water Cycle. The major elements of this system are mentioned here. Take a look at these elements and try to understand the System Map for this system. Also take a look at the relations between all the elements
We use system maps to understand complex issues with multiple factors that affect each other. In a system, every element is interconnected. In a system map, we try to represent that relationship through the use of arrows.
A system typically has several chains of causes and effects. You may notice that some arrows are longer than others. A longer arrow represents a longer time for a change to happen. We also call this a time delay.
Here is a sample System Map: The Water Cycle
The concept of Water cycle is very simple to understand and is known to all. It explains how water completes its cycle transforming from one form to another. It also adds other elements which affect the water cycle in some way.
The elements which define the Water cycle system are:
Let us draw the System Map for the Water Cycle now.
In this System Map, all the elements of the Water cycle are put in circles. The arrow- head depicts the direction of the effect and the sign (+ or -) shows their relationship. If the arrow goes from X to Y with a + sign, it means that both are directly related to each other. That is, If X increases, Y also increases and vice versa. On the other hand, If the arrow goes from X to Y with a – sign, it means that both the elements are inversely related to each other which means if X increases, Y would decrease and vice versa.
NOTE: We can also use this animated tool for drawing and understanding system maps: (https://ncase.me/loopy/)
3. Data Exploration
Data exploration means to analyze and understand the data by exploring different types of graphs and identify the trends and patterns out of it.
While acquiring data, you must have noticed that the data is a complex entity – it is full of numbers and if anyone wants to make some sense out of it, they have to work some patterns out of it.
Thus, to analyse the data, we need to visualise it in some user-friendly format so that we can:
● Quickly get a sense of the trends, relationships and patterns contained within the data.
● Define strategy for which model to use at a later stage.
● Communicate the same to others effectively.
To visualise data, we can use various types of visual representations like
- Bar Chart
- Line Chart
- Scatter Plot
- Tree Diagram
- Bubble Chart
- Venn Diagram
4. Modelling
In the previous module of Data Exploration, we explored the data that we had acquired at the Data Acquisition stage for the problem we scoped in the Problem Scoping stage. Now, we have visualised some trends and patterns out of the data which would help us to develop a strategy for our project. To build an AI based project, we need to work around Artificially Intelligent models or algorithms. This could be done either by designing our own model or by using the pre-existing AI models.
Before understanding this step let we understand the following terms
1. Artificial Intelligence, or AI, refers to any technique that enables computers to mimic human intelligence. The AI-enabled machines think algorithmically and execute what they have been asked for intelligently.
2. Machine Learning, or ML, enables machines to improve at tasks with experience. The machine learns from its mistakes and takes them into consideration in the next execution. It improvises itself using its own experiences.
3. Deep Learning, or DL, enables software to train itself to perform tasks with vast amounts of data. In deep learning, the machine is trained with huge amounts of data which helps it into training itself around the data. Such machines are intelligent enough to develop algorithms for themselves.
Deep Learning is the most advanced form of Artificial Intelligence out of these three. Then comes Machine Learning which is intermediately intelligent and Artificial Intelligence covers all the concepts and algorithms which, in some way or the other mimic human intelligence.
In general, there are two approaches taken by researchers when building AI models
1. Rule based approach: A Rule based approach is generally based on the data and rules fed to the machine, where the machine reacts accordingly to deliver the desired output.
2. Learning approach: In learning approach, the machine is fed with data and the desired output then the machine designs its own algorithm that can be used to predict the output for new, unseen data.
Generally, AI models can be classified as follows:
Rule Based Approach: Refers to the Al modelling where the rules are defined by the developer. The machine follows the rules or instructions mentioned by the developer and performs its task accordingly. In this approach, the machine doesn’t learn by itself.
For Example: If we want to train a machine who can tell that whether a child can go out to play Golf or not on the basis of certain parameters like
Outlook (Sunny, Rainy, Overcast)
Temperature (Hot, Normal, Cold)
Humidity (High, Normal)
Wind (Strong, Weak)
The machine trains on this data and now is ready to be tested. While testing the machine, we tell the machine that if Outlook = Overcast, Temperature = Normal, Humidity = Normal and Wind = Weak then the child can play Golf.
On the basis of this testing dataset, now the machine will be able to tell if the child can go out to play golf or not and will display the prediction to us. This is known as a rule-based approach because we fed the data along with rules to the machine and the machine after getting trained on them is now able to predict answers for the same.
NOTE: A drawback/feature for this approach is that the learning is static. The machine once trained, does not take into consideration any changes made in the original training dataset.
Learning Based Approach: It refers to the Al modelling where the machine learns by itself. This model gets trained on the data fed to it and then start making decision or predictions on new, unseen data.
For example, suppose you have a dataset comprising of 100 images of apples and bananas each. These images depict apples and bananas in various shapes and sizes. Now, the Al model is trained with this dataset and the model is programmed in such a way that it can distinguish between an apple image and a banana image. After training, the machine is now fed with testing data which might not have similar images as the ones on which the model has been trained. So, the model adapts to the features and start predicting that the image is of an apple or banana.
5. Evaluation
Evaluation is the process of understanding the reliability of any AI model, based on outputs by feeding test dataset into the model and comparing with actual answers. There can be different Evaluation techniques, depending of the type and purpose of the model.
Once a model has been made and trained, it needs to go through proper testing so that one can calculate the efficiency and performance of the model. Hence, the model is tested with the help of Testing Data (which was separated out of the acquired dataset at Data Acquisition stage) and the efficiency of the model is calculated on the basis of the parameters mentioned below:
- Accuracy
- Precision
- Recall
- F1 Score
Model Evaluation Terminologies: There are various new terminologies which come into the picture when we work on evaluating our model. Let’s explore them with an example of the Forest fire scenario.
Imagine that you have come up with an AI based prediction model which has been deployed in a forest which is prone to forest fires. Now, the objective of the model is to predict whether a forest fire has broken out in the forest or not. Now, to understand the efficiency of this model, we need to check if the predictions which it makes are correct or not.
The prediction is the output which is given by the machine and the reality is the real scenario in the forest.
Now let us look at various combinations that we can have with these two conditions.
Case 1: Is there a forest fire? Here, we can see in the picture that a forest fire has broken out in the forest.
Here, we can see in the picture that a forest fire has broken out in the forest. The model predicts a Yes which means there is a forest fire. The Prediction matches with the Reality. Hence, this condition is termed as True Positive.
Case 2: Is there a forest fire?
Case 3: Is there a forest fire?
Here the reality is that there is no forest fire. But the machine has incorrectly predicted that there is a forest fire. This case is termed as False Positive.
Case 4: Is there a forest fire?
Here, a forest fire has broken out in the forest because of which the Reality is Yes but the machine has incorrectly predicted it as a No which means the machine predicts that there is no Forest Fire. Therefore, this case becomes False Negative
6. Deployment
Deployment as the final stage in the AI project cycle where the AI model or solution is implemented in a real-world scenario.
The key steps involved in the deployment process are:
- Testing and validation of the AI model.
- Integration of the model with existing systems.
- Monitoring and maintenance of the deployed model.
Some examples of successful AI projects that have been deployed in various industries, such as self-driving cars, medical diagnosis systems, and chatbots.
Case Study: Preventable Blindness
Problem: Prevent loss of vision, and delay in report generation
●Approximately 537 million adults (20-79 years) are living with diabetes.
●Diabetes can lead to Diabetic Retinopathy It damages the blood vessels of the retina and can lead to blurred vision and blindness.
●Lack of qualified doctors and delay in reports increase the risk of Diabetic Retinopathy
One of the early symptoms of the defect is ‘Blurred vision’
How can we solve this problem with AI?
Solution: Using AI to detect Diabetic Retinopathy in pictures of eyes.
- An AI eye screening solution is developed in partnership with Google.
- AI models have achieved an accuracy of 98.6% in detecting diabetic retinopathy, on par with the performance of specialist eye doctors.
- Seventy-one vision centers in rural Tamil Nadu, India are using this solution.
- Trained technicians take pictures of patients’ eyes with cameras.
- The digital images are analyzed by AI for the presence of Diabetic Retinopathy.
- AI has made the detection of Diabetic Retinopathy quicker.
- Any technician can use this machine, even without a skilled doctor.
More and more patients can be treated at an early stage. Hence, early detection using AI can significantly benefit rural populations
Let us map this problem to AI project cycle
| Problem Scoping | Data Acquisition | Data Exploration | Modeling | Evaluation | Deployment |
| Detect Diabetic Retinopathy in pictures of eyes | Collecting data from patients from many clinics using retinal cameras. | Validating all the data to make sense out of it and come up with a model. | Creating an AI model to correctly diagnose Diabetic Retinopathy when given a retinal image as input. | Test the model for accuracy and then fine tune the model further to get the desired out -put. | Using the model in tools that can be used in clinics in even the remote and rural parts of the country. |
Ethics and Morality
What is Ethics?
Ethics refers to a set of principles or values that can help an individuals in determining what is right and wrong.
What are morals?
Morals are personal beliefs that help us to decide our actions.
Ethical Scenario – I
Imagine a situation that you are a student of class 9 and you have to submit the homework of different subjects. You discover an AI tool that can do homework for you.
Ethical Considerations:
Is it ethical to submit work generated by AI as your own?
Is it ethical to get an unfair advantage over classmates who had done their homework on their own?
Is your understanding about subject/topic can be enhanced by doing homework with AI tool?
Ethical Scenario – II
- Imagine a situation where you oversee burgers at a fast-food restaurant.
- It is a busy day with a lot of orders coming in fast.
- While cooking, you drop a burger on the dirty floor!
- Your boss passes by and says, “Just pick it up and serve it!”
What would you do?
Possible Actions:
- Refuse to Serve the Burger
- Prepare a New Burger
Examples of Ethical questions
•If a shopkeeper gives me back more money than what is due, is it better to return it? Or should I keep it with me?
•Is taking pens from a library considered stealing?
•Is taking extra paper napkins from a restaurant considered theft?
•You order a new dress from Amazon and after wearing it on your friends birthday party, you returned it stating the reason inappropriate fitting.
Examples of moral questions
•Is it OK to lie? If so, under what circumstances?
•If a family is hungry and has no other way to get food, is it OK to steal food from a rich store owner? Why or why not?
•Is a collective decision made by people, always, right? Or can it be wrong?
Moral vs Ethics
| Morals | Ethics |
| The beliefs dictated by our society. | The guiding principles to decide what is good or bad. |
| Morals are not fixed and can be different for different societies. | These are values that a person themselves chooses for their life. |
| Examples: ▪Always speak the truth ▪Always be loyal ▪Always be generous | Examples: ▪Is it good to speak the truth in all situations? ▪Is it good to be loyal under all circumstances? ▪Is it necessary to always be generous? |
Moral Machine is a platform for gathering a human perspective on moral decisions made by artificial intelligence, such as self -driving cars. At the end, you will be able to see how their responses compare with other people.
Ethics with Personal Data
▪There are around 5.34 billion smartphone users in the world as of July 2022, with their information available on the internet.
▪AI can help us find out data related to a particular person, from all the available data.
▪Such AI solutions are used by organizations to give us customized recommendations for products, songs, videos, etc.
▪In this way, AI can influence our decision-making at times
▪This calls for a need for ethical principles that govern AI and people who are creating AI.
AI Ethics Principles
The following principles in AI Ethics affect the quality of AI solutions
- Human Rights
- Bias
- Privacy
- Inclusion
1. Human Rights: When building AI solutions, we need to ensure that they follow human rights.
Here are a few things that you should take care of
▪Does your AI take away Freedom?
▪Does your AI discriminate against People?
▪Does your AI deprive people of jobs?
2. Bias: Bias (partiality or preference for one over the other) often comes from the collected data. The bias in training data also appears in the results.
Here are a few things that you should take care of
▪Does your data equally represent all the sections of the included populations?
▪Will your AI learn to discriminate against certain groups of people?
▪Does your AI exclude some people?
3. Privacy: We need to have rules which keep our individual and private data safe
Here are a few things that you should take care of
▪ Does your AI collect personal data from people?
▪ What does it do with the data?
▪ Does your AI let people know about the data that it is collecting for its use?
▪ Will your AI ensure a person’s safety? Or will it compromise it?
4. Inclusion: AI MUST NOT discriminate against a particular group of population, causing them any kind of
disadvantage.
Here are a few things you should take care of
▪ Does your AI leave out any person or a group?
▪ Is a rich person and a poor person benefited equally from your AI?
▪ How easy is it to use your AI?
▪ Who does your AI help?
Key Takeaways:
- Each AI problem can be mapped to the AI project cycle.
- AI project cycle simplifies the AI solution development process.
- Morality defines a set of beliefs dictated by society, culture, or tradition e.g., being truthful, loyal etc.
- Ethics defines the principles that decide what is good and what is bad e.g., is it right to speak the truth even if it threatens someone’s life?
- AI Ethics principles help us guide to create better and safer AI solutions.