These Class 9 AI Important Questions Chapter 1 AI Project Cycle & AI Ethics Class 9 Important Questions and Answers NCERT Solutions Pdf help in building a strong foundation in artificial intelligence.
AI Project Cycle & AI Ethics Class 9 Important Questions
Class 9 AI Project Cycle & AI Ethics Important Questions
Important Questions of AI Project Cycle & AI Ethics Class 9 – Class 9 AI Project Cycle & AI Ethics Important Questions
AI Project Cycle & AI Ethics Class 9 Very Short Answer Type Questions
Question 1.
Where can we see implementation of AI in real life?
Answer:
Driverless cars, Google Assistant, Smart speakers, Lenovo smart clock, Netflix, Cogito, Drones Chatbots, AI camera etc.
Question 2.
What is Evaluation?
Answer:
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.
Question 3.
What is Deployment?
Answer:
Deployment as the final stage in AI project cycle where the AI model or solution is implemented in a real-world scenario.
Question 4.
Define Data privacy?
Answer:
Data privacy refers to the protection of an individual’s personal information, ensuring that it is handled securely, ethically, and in accordance with legal and ethical standards.
Question 5.
What is the purpose of using AI in predictive maintenance?
Answer:
To predict when machinery will need maintenance based on data analysis.
Question 6.
How does AI’ help in personalized learning?
Answer:
By tailoring educational content to individual student needs.
Question 7.
What type of learning is often used in recommendation systems?
Answer:
Collaborative filtering.
Question 8.
What is Artificial Intelligence (AI)?
Answer:
AI is the ability of a machine to mimic Human intelligence, make decisions, predict the future, learn, and improve on its own.
Question 9.
Which AI domain is used for analyzing images and videos?
Answer:
Computer vision.
Question 10.
What is the main benefit of using AI in fraud detection?
Answer:
Analyzing and identifying patterns to detect fraudulent áctivities.
Question 11.
What does AI aim to mimic?
Answer:
Human intelligence
Question 12.
What does data acquisition involve in the AI project cycle?
Answer:
Collecting relevant data for analysis.
Question 13.
What are the three main capabilities of an AI system?
Answer:
Make decisions, predict the future, and learn from data.
Question 14.
What is the first stage of the AI project cycle?
Answer:
Problem Scoping
Question 15.
What does data acquisition involve in the AI project cycle?
Answer:
Collecting relevant data for analysis.
Question 16.
What is the purpose of the modelling stage in the AI project cycle?
Answer:
To create and train models using the acquired data.
Question 17.
What does the evaluation stage in the AI project cycle entail?
Answer:
Assessipg the model’s performance and accuracy.
Question 18.
What is a major ethical concern in AI regarding privacy?
Answer:
Data protection and user consent.
Question 19.
How can AI impact employment?
Answer:
AI can automate jobs, leading to job displacement but also creating new job opportunities.
Question 20.
What is one way to address the ethical concern of accountability in AI?
Answer:
Establishing clear guidelines on who is responsible for the outcomes of AI system.
AI Project Cycle & AI Ethics Class 9 Short Answer Type Questions
Question 1.
What are the challenges in AI?
Answer:
Some of the challenges are facing right now are as follows:
- Retain the facts as knowledge
- Recall the knowledge whenever a similar situation arise
- Think analyse and apply logic
- Make useful and accurate predictions
- Make decisions and upgrade their knowledge algorithms on their own
Question 2.
Explain pros and cons of AI
Answer:
| Pros of AI | Cons of AI |
| An AI machine doesn’t get tired | The AI technology is costly right now |
| An AI machine gives accurate and fast result | An AI machine lacks emotions and creativity |
| AI can be used in various fields with full efficiency | When misused, AI can lead to breach in security and privacy of individuals |
| AI machines can take decisions on their own | Legal malpractices may occur because of AI expert systems |
| AI machines can manage and process large | Humans can become totally dependent on AI |
| amount of data very easily | machines which may lead to disaster |
Question 3.
What are the types and techniques of AI.
Answer:
There are two types of Artificial Intelligence they are as follows:
| Based on Complexity | Based on Functionality |
| Artificial narrow intelligence | Reactive machines |
| Artificial generalised intelligence | Limited memory machines |
| Artificial super intelligence | Theory of mind machines |
| Self-awareness machines |
Question 4.
Differentiate between Ethics and Moral with suitable examples. in five sentences
Answer:
Ethics are principles and rules provided by external sources, such as professional standards or societal norms, guiding behavior in specific contexts. For example, a doctor adhering to the Hippocratic Oath is following medical ethics. Morals, on the other hand, are personal beliefs about right and wrong, shaped by individual, cultural, or religious influences. For instance, a persbn refraining from lying because of personal or religious convictions is acting based on their morals. Essentially, ethics are externally imposed guidelines, while morals are internally held beliefs.
Question 5.
What are the career opportunities in AI?
Answer:
Below are the following where opportunities exist in AI :
- Research and development
- Engineering
- Health care
- Space exploration
- Military and defence
- Banking and finance
- Marketing
- Robotics
- Game developers
- Customer support
Question 6.
What according you does problem scoping mean? write in your words below
Answer:
Problem scoping involves clearly defining and understanding the boundaries, objectives, and constraints of a problem or project before initiating any solution-seeking activities. It’s like setting the boundaries of a playground before starting to play a game.
It includes identifying the key stakeholders, understanding their needs and priorities, defining success criteria, and outlining the resources available. Essentially, problem scoping helps ensure that everyone involved has a shared understanding of what needs to be achieved and how it will be measured, which lays the foundation for effective problem-solving and decision-making processes.
Question 7.
Why is there a need to use a Problem Statement Template during problem scoping?
Answer:
Using a Problem Statement Template during problem scoping ensures that all pertinent aspects of the problem are systematically addressed and documented. It provides a structured framework for defining the problem, including its scope, objectives, constraints, and desired outcomes. The template promotes clarity and consistency in communication among stakeholders, fostering a shared understanding of the problem.
It helps align the problem statement with the overall goals and priorities of the project or organization, facilitating more effective decision-making and solution development. Additionally, the template serves as a valuable reference for documenting key insights, decisions, and assumptions made during problem scoping, aiding in future project phases.
Question 8.
What is the significance of Data Exploration after you have acquired the data for the problem scoped? Explain with examples.
Answer:
Data Exploration, also known as Exploratory Data Analysis (EDA), is a critical step after data acquisition in an AI project cycle. It involves examining and understanding the data to uncover patterns, spot anomalies, test hypotheses, and check assumptions. This step is essential for preparing the data for further analysis and modeling.
Question 9.
What do you think is the relevance of Data Visualization in AI?
Answer:
Data Visualization is crucial in AI for enhancing the understanding and interpretation of complex data, making it easier to identify patterns, trends, and anomalies. It plays a key role in exploratory data analysis, helping to prepare data for modeling by revealing important insights.
Visualization aids in evaluating model performance, such as using ROC curves for classification models or residual plots for regression. It effectively communicates results to stakeholders, ensuring transparency and facilitating data-driven decision-making. Additionally, visualization tools monitor AI models in production, ensuring they maintain accuracy and reliability over time.
Question 10.
List any five graphs used for data visualization.
Answer:
- Bar Chart: Used to compare the frequency or value of different categories.
- Line Chart: Ideal for showing trends over time.
- Scatter Plot: Displays the relationship between two numerical variables.
- Histogram: Shows the distribution of a single numerical variable.
- Heatmap: Uses color to represent data values in a matrix, showing the intensity of data points.
Question 11.
How can AI be used as a tool to transform the world into a better place?
Answer:
AI can transform the world into a better place by revolutionizing healthcare through early diagnosis, personalized treatment plans, and drug discovery. It can optimize resource usage, improve energy efficiency, and monitor environmental changes, leading to greater sustainability.
AI-driven automation can enhance productivity across industries, fostering economic growth and job creation. By analyzing real-time data, AI can improve public safety, aid in crisis response efforts, and mitigate risks. Through responsible and ethical deployment, AI has the potential to address pressing societal challenges and create a more equitable and sustainable future for all.
Question 12.
Can you list down a few applications in your smartphone that widely make use of computer vision?
Answer:
Several smartphone applications extensively utilize, computer vision technology. Instagram employs computer vision for automatic tagging of objects and people in photos, as well as for augmented reality (AR) filters and effects.
Snapehat uses computer vision for its popular AR lenses, which overlay animated graphics onto users’ faces in real-time. Google Photos utilizes computer vision for organizing and searching photos based on their content, as well as for automatic photo enhancement.
Pinterest employs computer vision for its visual search feature, allowing users to search for similar items by taking a photo or uploading an image. Lastly, Amazon Shopping uses computer vision for its “AR View” feature, enabling users to visualize products in their homes using augmented reality.
Question 13.
Draw out the difference between the three domains of AI with respect to the types of data they use.
Answer:
The three dornains of AI-Narrow AI, General AI, and Super intelligent AI-differ in the types of data they utilize. Narrow AI, designed for specific tasks, typically relies on structured data such as numerical values or categorical labels.
In contrast, General AI aims to mimic human-level intelligence across a wide range of tasks, requiring a broader set of data types including structured and unstructured data like text, images, audio, and video.
Super intelligent AI, a hypothetical level of AI surpassing human intelligence, would presumably have the capability to process any form of data, regardless of structure or complexity, reflecting its unprecedented level of understanding and analysis.
AI Project Cycle & AI Ethics Class 9 Long Answer Type Questions
Question 1.
Define applications of AI in everyday life.
Answer:
AI has been increasingly vital to our life over the last few years. Google maps, Google search recognition, Google lens, online shopping, reading updates on social media accounts, and flying drones for defence are a few examples of the areas where AI is being employed.
Below are the details of various applications of AI use in day to day life:
AI in Education and Training- Artificial intelligence (AI) systems are used for a variety of tasks, including performance prediction, curriculum design, smart assessments, helping teachers with their daily tasks, e-learning, educational research, automated training systems, virtual reality-based training, 3D learning robot assisted teaching and training, etc.
AI in customer support systems- Smart searches and Natural language processing (NLP) technology have revolutionised customer support systems and interactive voice response systems (IVRS).
AI in Service Oriented Businesses- Data is the main asset of service-oriented industries like banking, education, and tourism. AI systems accept the data, process it, and assist their users in producing profitable outcomes for the company.
AI in E-Commerce and Retail Businesses- Several well-known e-commerce sites, including Flipkart, Amazon, and Walmart, employ AI to attract customers and improve the shopping experience. On these platforms, AI algorithms gather a sizable quantity of information that is used to analyse the most popular products, compare products, analyse user experiences, gather comments, etc.
AI in Social Media-Another area where large amounts of data are constantly produced is social media. The advertising of goods and services, improvement of user experiences, and other uses are all possible for this mass data.
AI in Healthcare- AI has a wide range of applications in healthcare, including the ability to support surgery. Moreover, AI systems can aid in diagnostics, emergency medical decision-making, hospital management and safety, patient rehabilitation, research and analysis, etc.
AI in Research and Development-AI has enormous potential for study and development in a wide range of industries, including health, transportation, population, the environment, defence, and many more.
Question 2.
Define Domains of AI.
Answer:
The main aim of AI is to develop machines with human like intelligence. As we know that human intelligence is made up of domains like vision, perception, linguistics, learning and reasoning, similarly AI is composed of the following three domains-
- Data
- Computer Vision
- Natural Language Processing (NLP)
DATA
- Text
- Audio
- Video
COMPUTER VISION
- Face Recognition
- CBIR
- Smart Interactions
NATURAL LANGUAGE PROCESSING
- National Language Understanding (NLU)
- Natural Language Generation (NLG)
Data-Of of the three AI domains, data is regarded as the most important. Data, which can take any form-simple text, audiovisuals, big data with predictions, insights, projections, choices, etc.-is essentially a collection of raw facts and numbers.
Computer Vision (CV)-Computer vision refers to that domain of AI which enables a machine to analyse constant feed of huge amount of visual data, understanding various patterns in it and finally making decisions based on the findings.
Computer vision (CV) is used in the following areas:
- Face Recognition- AI systems use face detection algorithms to recognise faces in the images.
- Image Retrieval- AI systems use content based search for retrieving relevant images.
- Smart Cars- in self-driving smart cars computer’ vision is used to detect traffic signals, signs and lights for smooth movement of the vehicle.
Question 3.
What are the various stages of AI project cycle? Can you explain each with an example?
Answer:
The AI project cycle encompasses several key stages:
Problem Identification: This stage involves identifying a specific problem or opportunity that can be addressed using AI. For example, a retail company might aim to improve customer satisfaction by developing a recommendation system for personalized product suggestions.
Data Collection and Preparation: Here, relevant data is gathered from various sources and preprocessed to ensure quality and consistency. In the retail example, this could involve collecting customer transaction history, product details, and demographic information, and cleaning the data to remove errors and inconsistencies.
Data Analysis and Exploration: Data is analyzed to gain insights and understand patterns that may inform the development of AI models. For instance, in the retail project, exploratory data analysis might reveal correlations between customer demographics and purchasing behavior.
Model Selection and Development: AI models are selected based on the problem at hand and developed using appropriate algorithms and techniques. In the retail scenario, machine learning algorithms like collaborative filtering or matrix factorization might be used to build the recommendation system.
Model Training and Evaluation: The selected models are trained on the prepared data and evaluated using metrics to assess their performance. In the retail example, the recommendation system would be trained on historical data and evaluated based on metrics like accuracy or precision.
Model Deployment: Once a satisfactory model is developed, it is deployed into production to make predictions or provide recommendations in real-world scenarios. For the retail project, the recommendation system would be integrated into the company’s e-commerce platform for use by customers.
Monitoring and Maintenance: Deployed models are continuously monitored to ensure they perform as expected and remain effective over time. In the retail example, monitoring mechanisms would track the system’s performance and user feedback to identify any issues or areas for improvement.
Feedback and Iteration: Based on monitoring results and user feedback, models’ are iterated upon to improve their performance or address any shortcomings. For instance, in the retail project, the recommendation system might be refined based on customer feedback and updated with new data.
Scaling and Optimization: As the AI project matures, efforts may focus on scaling the solution to handle larger datasets or higher volumes of users. Optimization techniques may also be applied to improve the efficiency and speed of model inference.
Knowledge Transfer and Documentation: Finally, knowledge gained throughout the project cycle is documented and transferred to relevant stakeholders for future reference. This includes documenting model architecture, training procedures, and any lessons learned during the project’s execution.
Question 4
Explain the 4 Ws problem canvas in problem scoping.
Answer:
The 4 Ws problem canvas is a framework used in problem scoping to systematically analyze and understand the key aspects of a problem. The 4 Ws represent four questions: What, Why, Who, and When.
What:
This question focuses on identifying the specific problem or opportunity that needs to be addressed. It involves clearly defining the scope and boundaries of the problem, as well as understanding the desired outcomes or objectives.
Example: What is the problem we are trying to solve? What are the main challenges or pain points associated with this problem?
Why:
This question delves into the underlying reasons or motivations behind the problem. It helps uncover the root causes or driving forces behind the problem, as well as the potential benefits of solving it.
Example: Why is this problem important or relevant? Why does it need to be addressed now?
Who:
This question focuses on identifying the stakeholders or individuals who are impacted by the problem or have a vested interest in its resolution. It involves understanding their roles, perspectives, and needs.
Example: Who are the primary stakeholders affected by this problem? Who are the key decision-makers or influencers?
When:
This question pertains to the timing and urgency of the problem. It helps determine the timeline for addressing the problem and any specific deadlines or milestones associated with it.
Example: When did the problem first arise? When do we need to implement solutions by? When are key events or milestones related to the problem expected to occur?
Question 5.
What is problem scoping? What are the steps of problem scoping?
Answer:
Problem scoping is the process of defining and understanding the boundaries, objectives, and constraints of a problem or project before initiating any solution-seeking activities. It involves clarifying the scope of the problem, identifying key stakeholders, understanding their needs and priorities, and defining success criteria. Problem scoping lays the foundation for effective problem-solving by ensuring that everyone involved has a shared understanding of what needs to be achieved and how it will be measured.
The steps of problem scoping typically include:
Identifying the Problem: Clearly define the problem statement or opportunity that the project aims to address. This involves understanding the context, background, and specific challenges or pain points.
Defining Objectives: Determine the goals and objectives of the project. What outcomes are you seeking to achieve? What specific results do you hope to deliver?
Identifying Stakeholders: Identify the individuals or groups who are impacted by the problem or have a vested interest in its resolution. This includes both internal and external stakeholders, such as team members, clients, customers, and regulatory bodies.
Understanding Constraints: Identify any constraints or limitations that may affect the project, such as budgetary constraints, time constraints, technical limitations, or regulatory requirements.
Setting Success Criteria: Define criteria for evaluating the success of the project. What metrics will be used to measure progress and outcomes? How will success be determined?
Gathering Information: Collect relevant data, information, and insights related to the problem. This may involve conducting research, analyzing existing data, interviewing stakeholders, and gathering feedback.
Analyzing Risks and Opportunities: Identify potential risks, challenges, and opportunities associated with the problem and its proposed solutions. Assess the likelihood and impact of these factors on the project’s success.
Documenting Findings: Document all findings, insights, and decisions made during the problem scoping process. This serves as a reference for future project phases and ensures that everyone involved has access to the same information.
Question 6.
Who are the stakeholders in the problem scoping stage?
Answer:
In the problem scoping stage, stakeholders are individuals or groups who have an interest in or are impacted by the problem or project being scoped. Stakeholders typically include:
Project Team: This includes individuals directly involved in scoping and solving the problem, such as project managers, analysts, engineers, and subject matter experts.
Client or Customer: The client or customer is often the entity for whom the problem is being addressed or who will benefit from the project’s outcomes. Their needs, requirements, and expectations are critical considerations in problem scoping.
End Users: End users are the individuals or groups who will interact with the solution once it is implemented. Understanding their needs, preferences, and pain points is essential for developing a solution that meets their requirements.
Management and Leadership: Management and leadership within the organization overseeing the project play a crucial role in problem scoping. They provide strategic direction, allocate resources, and make key decisions about project priorities and objectives.
Regulatory Bodies: If the problem or project is subject to regulatory requirements or compliance standards, regulatory bodies may be stakeholders. Their guidelines and regulations must be considered and adhered to during problem scoping.
Other Departments or Teams: Depending on the nature of the problem or project, other departments or teams within the organization may be stakeholders. Their perspectives, expertise, and resources may be valuable in scoping and solving the problem.
Suppliers and Partners: Suppliers and partners who provide goods, services, or support related to the project may also be stakeholders. Their input and collaboration may be necessary for successful problem scoping and solution development.
Community or Society: In some cases, the broader community or society may be stakeholders, particularly if the problem or project has social, environmental, or ethical implications. Understanding their concerns and impacts is essential for responsible problem ‘scoping.
Project Example: A behavioral study on how children interact with educational toys in a classroom setting. Observations provide direct data on actions and interactions in a natural environment.
Experiments:
Project Example: A psychological study testing the effects of different types of stimuli on memory recall. Experiments can establish causality and measure responses under controlled conditions.
Secondary Data Analysis:
Project Example: An economic research project analyzing existing government data to identify trends in employment rates over the last decade. This method leverages existing data sets for new analyses and insights.
Case Studies:
Project Example: An in-depth analysis of a successful community-led renewable energy project to identify best practices and potential for replication in other areas. Case studies provide comprehensive details about specific instances or entities.
Content Analysis:
Project Example: A media study analyzing news coverage on climate change across different countries. Content analysis can systematically quantify and analyze the presence of certain words, themes, or concepts within textual data.
Ethnography:
Project Example: An anthropological study of the social and cultural practices of an indigenous community. Ethnography involves immersive observation and participation to gain a deep understanding of a group’s way of life.
Online Data Collection:
Project Example: A social media analytics project studying public sentiment on various political issues by collecting and analyzing data from Twitter posts. Online data collection can harness vast amounts of data from digital platforms.
Sensors and IoT Devices:
Project Example: An environmental monitoring project using sensors to track air quality in urban areas. Sensors provide real-time, precise data that can be used for detailed analyses and modeling.
Geospatial Data Collection:
Project Example: An urban planning project using GIS (Geographic Information Systems) to map and analyze land use patterns and infrastructure development. Geospatial data collection supports detailed spatial analyses.
Question 9.
Define principles of AI
The principles of AI refer to foundational guidelines designed to ensure the development and deployment of AI systems that are ethical, fair, and beneficial to society. Here are some key principles of AI:
Transparency
- Definition: AI systems should be understandable and their decision-making processes should be clear.
- Importance: Ensures that users can trust AI systems and understand how decisions are made.
- Example: Providing explanations for AI-driven decisions in healtheare diagnostics.
Fairness
- Definition: AI should be unbiased and should not discriminate against any individuals or groups.
- Importance: Promotes equality and prevents harm caused by biased AI systems.
- Example: Ensuring that a hiring algorithm does not favor candidates based on gender or race.
Accountability
- Definition: There should be mechanisms in place to hold AI systems and their creators responsible for their actions and decisions.
- Importance: Encourages responsible use and development of AI technologies.
- Example: Clear attribution of responsibility when an autonomous vehicle is involved in an accident.
Privacy
- Definition: AI systems should protect the privacy of individuals and handle data responsibly.
- Importance: Safeguards personal information and builds user trust.
- Example: Implementing strong data encryption and anonymization techniques in-AI systems.
Safety
- Definition: AI systems should operate reliably and safely, minimizing risks and poțential harm.
- Importance: Ensures the well-being of users and the environment.
- Example: Rigorous testing of AI algorithms in critical applications like medical devices or autonomous driving.
Beneficence
- Definition: AI should be designed to promote the well-being and benefit of individuals and society.
- Importance: Ensures that AI contributes positively to societal progress.
- Example: Using AI to improve disaster response and management.
Inclusiveness
- Definition: AI systems should be accessible and beneficial to all segments of society.
- Importance: Promotes diversity and equal access to AI technologies.
- Example: Developing AI solutions that are accessible to people with disabilities.
Sustainability
- Definition: AI development should consider environmental impacts and strive for sustainability.
- Importance: Ensures that AI technologies contribute to environmental conservation and do not cause harm.
- Example: Creating energy-efficient AI algorithms and using AI to monitor and mitigate climate change.
Human-Centered Design
- Definition: AI systems should be designed with the needs and values of humans at the forefront.
- Importance: Ensures that AI technologies enhance human capabilities and experiences.
- Example: Developing user-friendly interfaces and ensuring AI systems assist rather than replace human workers.
Question 10.
Write major issues around AI Ethics
Several major issues surround AI ethics, reflecting concerns about the responsible development, deployment, and use of artificial intelligence technologies. Here are some of the key issues:
Bias and Fairness:
AI systems can perpetuate or even exacerbate biases present in training data, leading to unfair outcomes for certain groups.
Examples include discriminatory hiring algorithms or facial recognition systems that are less accurate for specific demographic groups.
Transparency and Explainability:
Many AI algorithms operate as “black boxes,” making it difficult to understand how they arrive at their decisions.
Lack of transparency raises concerns about accountability and trust, particularly in highstakes applications like healthcare or criminal justice.
Privacy and Data Protection:
AI often relies on large amounts of personal data, raising concerns about data privacy and the potential for unauthorized access or misuse.
Issues include data breaches, unauthorized surveillance, and the re-identification of individuals from anonymized data.
Safety and Security:
Malicious actors could exploit vulnerabilities in AI systems, leading to cyber security threats, data breaches, or manipulation of AI-generated content (e.g., deep fakes).
Safety concerns arise in applications like autonomous vehicles or medical devices, where AI errors could have life-threatening consequences.
Accountability and Liability:
Determining responsibility for AI decisions is complex, especially in cases where multiple parties are involved in the development and deployment of AI systems.
Questions arise about who should be held accountable when AI systems cause harm or make erroneous decisions.
Inequality and Job Displacement:
AI technologies have the potential to exacerbate existing inequalities by displacing workers in certain industries or favoring those with access to advanced technology.
Ensuring equitable access to AI benefits and mitigating job displacement are significant ethical challenges.
Ethical Governance and Regulation:
There is a netd for robust ethical governance frameworks and regulations to ensure that AI technologies are developed and deployed in accordance with ethical principles and societal values.
Balancing innovation with ethical considerations poses a challenge for policymakers and regulators.
Autonomy and Human Rights:
AI applications raise questions about individual autonomy and human rights, particularly in contexts like surveillance, predictive policing, or social credit systems.
Safeguarding human rights, such as freedom of expression and privacy, in the face of AIdriven technologies is a critical ethical concern.
Environmental Impact:
The energy-intensive nature of AI training and computation contributes to environmental concerns, including carbon emissions and resource consumption.
Ensuring sustainable AI development and minimizing environmental impact are ethical imperatives.
Case-Based Questions:
Question 1.
AI Reflection
Case: A tech company deployed a customer service AI chatbot. After several months, they noticed the chatbot occasionally gives incorrect or irrelevant responses, leading to customer dissatisfaction.
Question:
How should the company reflect on the AI’s performance and what steps can be taken to improve it?
Answer:
The company should conduct a thorough reflection by analyzing the chatbots performance metrics, such as response accuracy, customer satisfaction scores, and response time. They should gather feedback from users and identify common issues or patterns in incorrect responses. Based on this reflection, the company can retrain the chatbot using updated datasets, improve the natural language processing algorithms, and incorporate feedback loops for continuous learning. Regularly scheduled performance reviews and updates should be part of the ongoing maintenance to ensure the chatbot’s effectiveness.
Question 2.
Project Cycle – Initiation Phase
Case: A healthcare organization plans to implement an AI-driven diagnostic tool to assist doctors in identifying diseases from medical images.
Question:
What steps should be taken during the initiation phase of the project cycle to ensure its success?
Answer:
During the initiation phase, the healthcare organization should define the projects objectives, scope, and deliverables. Key stakeholders, including medical professionals, IT staff, and AI experts, should be identified and engaged. A feasibility study should be conducted to assess the technical, economic, and operational viability of the project. Additionally, potential risks and ethical considerations, such as patient privacy and data security, should be identified. A project charter should be created to outline the projects goals, timeline, budget, and resource requirements, and to obtain formal approval to proceed to the planning phase.
Question 3.
Ethics – Bias in AI
Case: A recruitment company uses an AI system to screen job applications. It was discovered that the AI system exhibits bias against female candidates, leading to a disproportionate number of male applicants being selected for interviews.
Question:
What ethical issues arise from this situation, and how can the company address them?
Answer:
The ethical issues include discrimination, fairness, and equal opportunity. The bias in the AI system could perpetuate gender inequality and potentially violate antidiscrimination laws. To address this, the company should audit the AI system to identify the sources of bias, such as biased training data or biased algorithms.
They should then take steps to mitigate bias by diversifying the training data, using fairness-aware algorithms, and implementing regular bias assessments. Transparency in the AI decision-making process and providing avenues for candidates to contest AI-driven decisions are also essential to uphold ethical standards.
Question 4.
Project Cycle – Execution Phase
Case: A retail company is executing a project to implement AI-powered inventory management to optimize stock levels and reduce waste.
Question:
What activities and best practices should be followed during the execution phase to ensure the project stays on track?
Answer:
During the execution phase, the company should follow the project plan, monitor progress, and ensure that all tasks are completed on time and within budget. Key activities include:
- Resource Allocation: Ensure that the necessary resources, including personnel, technology, and data, are available and utilized effectively.
- Communication: Maintain clear and regular communication among the project team and stakeholders to keep everyone informed and aligned.
- Quality Control: Implement quality assurance processes to verify that the AI system meets the required standards and performs as expected.
- Risk Management: Continuously monitor and address potential risks and issues that may arise during the project.
- Documentation: Keep detailed records of all project activities, decisions, and changes to ensure transparency and accountability.
Regular progress reviews and adjustments to the project plan, if necessary, are crucial to accommodate apy changes or unforeseen challenges.
Question 5.
Ethics – Data Privacy
Case: A social media company plans to use AI to analyze user data for targeted advertising. However, users are concerned about their privacy and the potential misuse of their personal information.
Question:
What ethical considerations should the company take into account, and how can they address user concerns?
Answer:
The ethical considerations include user consent, data privacy, and transparency. The company should ensure that user data is collected and used in compliance with privacy laws and regulations, such as GDPR. They should provide clear and transparent information about how user data will be used and obtain explicit consent from users before collecting their data.
To address user concerns, the company can implement the following measures:
- Data Anonymization: Anonymize user data to protect individual identities.
- Opt-In/Opt-Out Options: Give users the choice to opt-in or opt-out of data collection and targeted advertising.
- Security Measures: Implement robust data security measures to protect user data from breaches and unauthorized access.
- Transparency Reports: Regularly publish transparency reports detailing data usage, privacy practices, and any incidents of data misuse.
- User Control: Provide users with tools to manage their data and privacy settings easily.
Question 6.
A school decided to implement an AI project to develop a smart attendance system. The project aimed to use facial recognition technology to mark student attendance automatically as they entered the classroom. The AI team, comprising teachers and students, followed a project cycle to plan, design, develop, and test the system. During the testing phase, they encountered several issues, including misidentification of students, privacy concerns, and ethical questions about data usage. The team conducted reflections and discussions to address these challenges. They decided to refine the AI model, implement robust data protection measures, and create clear guidelines for ethical use of the technology.
Questions:
Question 1.
Describe the steps involved in the project cycle followed by the AI team.
Answer:
Steps in the Project Cycle:
- Planning: Identifying the problem (manual attendance) and setting objectives (developing a smart attendance system).
- Designing: Creating a blueprint for the facial recognition system, including hardware and software requirements.
- Developing: Building the system by coding the AI model and integrating it with cameras and other hardware.
- Testing: Running trials to ensure the system works correctly, identifying and fixing issues.
- Deployment: Implementing the system in classrooms for daily use.
- Maintenance: Regular updates and improvements based on feedback.
Question 2.
What issues did the AI team encounter during the testing phase?
Answer:
Issues Encountered:
- Misidentification of students.
- Privacy concerns regarding facial data.
- Ethical questions about the use and storage of personal data.
Question 3.
How did the team address the problem of misidentification in the smart attendance system?
Answer:
Addressing Misidentification:
- Refined the AI model by training it on a larger and more diverse dataset.
- Implemented multiple verification steps to cross-check identities.
- Improved the camera setup for better image capture.
Question 4.
Explain the ethical concerns related to the use of facial recognition technology in schools.
Answer:
Ethical Concerns:
- Privacy: Unauthorized access and misuse of facial data.
- Bias: Potential for AI algorithms to have biases, leading to unfair treatment.
- Consent: Ensuring students and parents are aware of and agree to the use of their data.
- Surveillance: Impact on the freedom and autonomy of students.
Question 5.
What measures did the AI team take to ensure the ethical use of the smart attendance system?
Answer:
Ethical Measures Taken:
- Implemented strict data protection protocols, including encryption and access control.
- Developed and shared a clear privacy policy outlining data usage.
- Obtained informed consent from students and parents before collecting facial data.
- Regularly reviewed the system for biases and took corrective actions.
AI Project Cycle & AI Ethics Class 9 Notes
Meaning of Intelligence-Intelligence is the ability to learn from experience, to recognize problems and to solve problems. According to Sternberg and Sternberg- ” Intelligence is the capacity to learn from experience, using met cognitive process to enhance learning, and the ability to the surrounding environment.”
Introduction of AI- Artificial Intelligence (AI) is a branch of computer science that simulate human intelligence into machines. The term Artificial Intelligence is made up of two words: Artificial and Intelligence.
Types of AI-AI can be classified into three categories namely Weak AI, Strong AI, Super AI.
AI Around us: Smartphone’s, Email Spam Filters, Virtual Assistants, Social Media, Music and Media Streaming Services, Video Games, Navigation, Security and Surveillance and Social Media Filters.
Importance of AI-It can reduce time and increased accuracy, Making of Robotics, Automation and Boon for differently abled individuals.
Applications of AI in Real-Life- Healthcare for Assisting Doctors, AI in Education for Automated Grading System, Business for Smoothening Overall Process, AI in Agriculture, AI in Banking and Finance, AI in Gaming Industry, AI in Space Exploration, Autonomous Vehicles for Advanced Features, AI in Assistance, Social Media for Serving Personalised Experience, AI in Artificial Creativity.
Careers in AI-Data Scientist, Machine Learning Engineer, Robotics Scientiest, AI Research Scientiests, AI Architests, Big Data Engineer.
Artificial Intelligence covers a broad range of domains and applications and is expected to impact every field in the future.
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.