Course Outline of Record

Item	Value
Eff Term	Fall 2026
Curriculum Committee Approval Date	11/14/2025
Top Code	070200 - Computer Information Systems (CTE)
Units	0 Total Units
Hours	27 Total Hours (Lecture Hours 27)
Total Outside of Class Hours	0
Total Student Learning Hours	27
Course Credit Status	Noncredit (N)
Material Fee	No
Basic Skills	Not Basic Skills (N)
Repeatable	Yes; Repeat Limit 99
Open Entry/Open Exit	No
Grading Policy	P/NP/SP Non-Credit (D),  Letter Non-Credit (L)

Course Description

This course examines how bias and privacy concerns arise in artificial intelligence systems and explores strategies to mitigate these risks. Students will learn to recognize biased outputs, understand data privacy implications, and evaluate AI tools for fairness and transparency. This course emphasizes responsible use, user consent, and safeguarding personal information when interacting with AI technologies. Not Transferable.

Course Level Student Learning Outcome(s)

1. Identify examples of bias and discrimination in AI systems and explain how they impact individuals and communities.
2. Describe key privacy risks associated with the use of AI tools and how personal data may be collected, used, or exposed.
3. Evaluate AI tools and practices for fairness, transparency, and compliance with basic data protection principles.

Course Objectives

• 1. Define key terms related to AI bias and privacy, including algorithmic discrimination, data anonymization, and informed consent.
• 2. Identify common sources of bias in AI systems, such as imbalanced training data or flawed assumptions in model design.
• 3. Explain how privacy can be compromised by AI tools that collect, process, or generate personal information.
• 4. Describe the social and ethical impacts of biased and privacy-invasive AI tools in areas like hiring, healthcare, and law enforcement.
• 5. Explore methods to reduce bias in AI systems, including inclusive data practices and human oversight.
• 6. Discuss data protection strategies such as encryption, de-identification, and limiting data access in AI applications.
• 7. Evaluate AI tools for fairness and transparency, using simple frameworks or checklists to assess ethical risks.

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Lecture Content

1. Understanding Bias in AI
2. Case Studies of Bias in AI Systems
3. Detecting and Reducing Bias
4. Introduction to Privacy in AI
5. Privacy Risks in AI Tools
6. Informed Consent and Data Protection
7. Government and Legal Frameworks
8. Ethical Design and Human Oversight
9. Evaluating AI Tools for Bias and Privacy
10. Social Impacts of Unchecked AI
11. Building a Responsible AI Toolkit

Method(s) of Instruction

• Enhanced NC Lect (NC1)
• Live Online Enhanced NC Lect (NC9)
• Online Enhanced NC Lect (NC5)

Instructional Techniques

This course will utilize a combination of lecture, system simulators, classroom discussion with student interactions, problem-solving techniques, quizzes, exams, and troubleshooting assignments to achieve the goals and objectives of this course. All instructional methods are consistent across all modalities.

Reading Assignments

Read open educational resource materials, journal articles and corporate reports, news articles, interactive career websites, and privacy policies.

Writing Assignments

Analyze examples of bias and privacy concerns in AI systems, propose strategies to mitigate these risks, and reflect on the ethical responsibilities of developers and users in protecting fairness and data integrity.

Out-of-class Assignments

Research real-world incidents of AI bias or privacy violations, evaluate the effectiveness of existing data protection measures, and complete short written reflections or analyses on strategies to promote fairness, transparency, and responsible AI use.

Methods of Student Evaluation

• Short Quizzes
• Written Assignments
• Projects (Individual/Group)
• Problem Solving Exercises
• Skills Demonstration

Demonstration of Critical Thinking

Evaluate AI systems for fairness and privacy risks, interpret complex ethical issues, and propose balanced, evidence-based solutions to promote responsible and transparent AI practices.

Required Writing, Problem Solving, Skills Demonstration

Identify and assess instances of bias or privacy risks in AI systems, propose practical mitigation strategies, and communicate ethical solutions through clear and well-supported analyses.

Resources Subscreen

• Open Education Resource: J.J. Sylvia IV. The Data Renaissance: Analyzing the Disciplinary Effects of Big Data, Artificial Intelligence, and Beyond. (September 2025).

Eligible Discipline(s)

• Computer information systems (computer network installation, microcomputer technology, computer applications): Any bachelor’s degree and two years of professional experience, or any associate degree and six years of professional experience.
• Computer service technology: Any bachelor’s degree and two years of professional experience, or any associate degree and six years of professional experience.
• Computer science: Master’s degree in computer science or computer engineering OR bachelor’s degree in either of the above AND master’s degree in mathematics, cybernetics, business administration, accounting or engineering OR bachelor’s degree in engineering AND master’s degree in cybernetics, engineering mathematics, or business administration OR bachelor’s degree in mathematics AND master’s degree in cybernetics, engineering mathematics, or business administration OR bachelor’s degree in any of the above AND a master’s degree in information science, computer information systems, or information systems OR the equivalent. Note: Courses in the use of computer programs for application to a particular discipline may be classified, for the minimum qualification purposes, under the discipline of the application. Master's degree required.