7. Artificial Intelligence

Artificial intelligence is becoming a practical layer in information systems. This chapter introduces AI concepts, generative AI, and governance issues such as bias, privacy, accountability, and organizational risk.

Video

What Are AI, ML, DL, and Generative AI? (about 10 minutes) by IBM Technology

This video is embedded from YouTube for course reference. It remains hosted by YouTube and belongs to its creator or rightsholder.

Learning Objectives

By the end of this chapter, you will be able to:

1. Distinguish models, training, inference, prediction, and classification in a business workflow.

2. Evaluate a model using appropriate data and the consequences of different errors.

3. Explain prompts, context, retrieval, generation, and tool-using agents.

4. Design a governance rationale covering bias, privacy, security, oversight, and accountability.

Concept Map

Artificial Intelligence

AI Concepts

• Learning
• Prediction
• Automation

Generative AI

• Prompts
• Outputs
• Limits

AI Governance

• Bias
• Privacy
• Accountability

Glossary

#	Term	Explanation
1	Artificial intelligence	Computer systems that perform tasks associated with perception, language, prediction, reasoning, or action.
2	Machine learning	An AI approach in which models improve performance by learning patterns from data.
3	Training data	Examples used to fit or adjust a model.
4	Model	A learned or designed representation that maps inputs to outputs.
5	Prediction	A model output estimating a likely value, class, event, or next action.
6	Classification	Assigning an input to one of several categories.
7	Generative AI	AI that creates text, images, code, audio, or other content from learned patterns.
8	Large language model	A model trained on large amounts of text to generate and analyze language.
9	Prompt	The instruction, question, or context given to an AI system.
10	Token	A small text unit processed by a language model.
11	Embedding	A numeric representation of meaning used for search, comparison, or model input.
12	Agent	An AI system that can pursue a goal by using tools, memory, or multi-step workflows.
13	Automation	Using technology to perform tasks with limited human intervention.
14	Human oversight	Review and control by people over AI outputs, decisions, and consequences.
15	Bias	Systematic distortion or unfairness in data, model behavior, or system outcomes.
16	Hallucination	An AI output that appears plausible but is unsupported, false, or fabricated.
17	Explainability	The ability to give useful reasons for how a model produced an output.
18	AI governance	Policies, roles, controls, and monitoring used to manage AI value and risk.
19	Responsible AI	The design and use of AI with attention to fairness, safety, transparency, accountability, and human impact.
20	Model risk	The possibility that a model produces harmful, unreliable, biased, or misused results.

Lecture Slides

Chapter 7 Slides

These lecture drafts follow the authored sections and aligned assignments.