Unit 1: AI Project Cycle and Ethical Frameworks Study Guide

This study guide provides a comprehensive review of the AI Project Cycle, the primary domains of Artificial Intelligence, and the ethical frameworks required to develop responsible AI solutions. It is designed to reinforce understanding of how structured processes and moral principles intersect in technology development.

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Part 1: Short-Answer Quiz

Instructions: Answer the following ten questions in 2–3 sentences, ensuring all information is derived from the source context.

What is the primary purpose of the Problem Scoping stage in the AI Project Cycle?
How does the Data Exploration stage contribute to the development of an AI model?
Explain the role of the deployment stage in a real-world AI solution.
How is the Statistical Data domain of AI used in price comparison websites?
What is the objective of Computer Vision (CV) in the context of agricultural monitoring?
Define Natural Language Processing (NLP) and its ultimate goal regarding human communication.
Why are ethical frameworks necessary when building AI decision-making tools?
Distinguish between sector-based and value-based ethical frameworks.
What does the principle of "Respect for Autonomy" entail within the Bioethics framework?
In the healthcare case study provided, why did the algorithm inadvertently cause a bias against patients from the Western region?

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Part 2: Quiz Answer Key

Problem Scoping Answer: Problem Scoping is the initial stage where the goal of the AI project is set by stating the specific problem to be solved. During this phase, various parameters affecting the problem are identified to create a clearer picture of the project's requirements.
Data Exploration Answer: Data Exploration involves creating visual representations of acquired data, such as graphs, maps, or flow charts, to interpret patterns. These insights help the developer decide which type of model is most suitable to achieve the project's goals.
Deployment Answer: The deployment stage is crucial for ensuring the successful integration and operation of AI solutions within real-world environments. This allows the AI to deliver actual value and impact to users and stakeholders.
Statistical Data Answer: In price comparison websites, the AI system collects large datasets from multiple vendors to allow users to compare products in one place. The extracted information helps the system derive meaning and allows the user to make an informed purchasing decision.
Computer Vision Answer: In agriculture, Computer Vision uses drones and cameras to capture aerial images of farmland for crop monitoring and pest detection. These images are analyzed to assess crop health, optimize farming practices, and estimate yields.
NLP Answer: NLP is a branch of AI that deals with the interaction between computers and humans using spoken or written natural language. Its ultimate objective is to enable machines to read, decipher, and make sense of human languages in a valuable way.
Ethical Frameworks Answer: Ethical frameworks are necessary because AI is often used as a decision-making tool that can carry hidden biases, such as those seen in hiring algorithms. These frameworks provide step-by-step guidance to ensure AI makes morally acceptable choices and avoids unintended harm.
Framework Classification Answer: Sector-based frameworks are tailored to specific industries, such as Bioethics for healthcare, while value-based frameworks focus on fundamental moral philosophies. Value-based frameworks are further divided into rights-based, utility-based, and virtue-based approaches.
Respect for Autonomy Answer: This principle ensures that users are fully aware of how an AI algorithm functions and makes decisions. In a healthcare context, this means that the data used to train models should be reproducible and accessible to the patients.
Case Study Answer: The algorithm used healthcare expense data as a proxy for health metrics instead of actual physical illness. Because less money was historically spent on healthcare for patients in the Western region, the algorithm incorrectly categorized them as having lower health risks despite having more severe conditions.

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Part 3: Essay Questions

Instructions: Use the source context to develop detailed responses to the following prompts. (Answers not provided).

The Lifecycle of an AI Project: Describe the transition from the Modelling stage to the Evaluation stage. Why is it necessary to test the model on newly fetched data before moving to deployment?
AI Domains and Data Types: Compare and contrast the types of data used in Statistical Data, Computer Vision, and Natural Language Processing. Provide one unique real-world application for each.
The Impact of Internal Bias: Discuss the various factors—such as culture, religion, and intuition—that influence human decision-making. How does identifying these personal biases help in the creation of a more robust ethical framework for AI?
Applying Bioethics to AI: Analyze the four principles of Bioethics (Autonomy, Non-maleficence, Beneficence, and Justice). How can these principles be practically applied to prevent a "maleficence" outcome in a technological solution?
Critical Analysis of Data Selection: Using the provided healthcare case study, explain why "unbiased data" is essential for justice in AI. What social determinants should developers consider to avoid exacerbating issues like racism or sexism?

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Part 4: Glossary of Key Terms

Term	Definition
AI Project Cycle	The cyclical process followed to complete an AI project, consisting of six stages: Problem Scoping, Data Acquisition, Data Exploration, Modelling, Evaluation, and Deployment.
Beneficence	The ethical principle of promoting and maximizing the well-being of individuals and society to ensure positive outcomes for all stakeholders.
Bioethics	An ethical framework used in healthcare and life sciences to address issues like patient privacy, data security, and the ethical use of AI in medicine.
Computer Vision (CV)	A domain of AI that enables machines to analyze visual information (photographs, videos) and predict decisions based on that content.
Ethical Framework	A set of step-by-step guidelines that help ensure decisions do not cause unintended harm and align with moral values.
Justice	The principle that all benefits and burdens of a choice must be distributed fairly across people, regardless of their background or social status.
Maleficence	The concept of intentionally causing harm or wrongdoing.
Natural Language Processing (NLP)	A branch of AI focused on the interaction between computers and humans using natural spoken or written language.
Non-maleficence	The ethical obligation to avoid causing harm and to minimize negative consequences as much as possible.
Problem Scoping	The first stage of the AI Project Cycle, where the project goal is defined and the parameters of the problem are identified.
Statistical Data	A domain of AI related to data systems and processes where the system collects and analyzes datasets to derive meaning.
Utility-based Framework	An ethical approach that evaluates actions based on maximizing the overall good and minimizing harm for the greatest number of people.

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