MARA 284-11 (2 Units)
Seminar in Archives & Records Management
Topic: AI Ethics for Archivists and Records and Information Management Professionals
Spring 2022 Syllabus
Dr. Norman Mooradian
E-mail
Office Location: Claremont, California
Office Hours: By appointment
| Syllabus Links Textbooks CLOs Competencies Prerequisites |
Resources Canvas Login and Tutorials iSchool eBookstore |
This course will be available beginning March 7, 2022, at 6 am PT. This course is worth 2 units and runs from Runs from March 7th - May 6th.
You will be enrolled in the Canvas site automatically.
Course Description
This course reviews principle issues and ethical frameworks in artificial intelligence ethics and relates these issues to the fields of information management and governance. Topics covered include AI ethics frameworks; algorithmic bias and due process, accountability; explainable AI (XAI) and records, AI and data privacy; automation and work, including work in the RIM fields, and AI and intellectual property..
Course Requirements
Assignments
Discussion Posts
Each week, one discussion prompt will be posted.
Each student will write a response to the post(s).
The response should be approximately two (2) full paragraphs.
It will be evaluated based on clarity of writing and relevance to the question.
Participation
Each week, students will have an opportunity to earn two (2) participation points by replying to two other students’ post (1 point for each reply). The replies can be in the form of a clarification, question, critical observation, expansion, etc. It should be thoughtful and constructive. Again, the student will reply to two other students.
Writing Pieces
- A writing piece will be required every two weeks. It will cover the material for two modules. There will be four (4) in total.
- It should be 6 pages, double spaced, 12 pt. font, following APA style.
- It will consist of an explanation, analysis, or argument in response to one prompt. A choice of at least two prompts will be provided.
- The writing piece will be submitted to the instructor and returned to the student with comments.
- It will be evaluated based on clarity of writing, logical organization, relevance to the question, and effectiveness in explaining or supporting its central ideas
Reflection Piece
- At the end of the course, you will write a brief reflection describing something learned in the course that you find applicable or relevant to your work, career plans, etc.
Grading
|
Assignment |
Value |
Quantity |
Total |
CLOs |
|
Discussion Posts |
5 |
8 |
40 |
CLO1, CLO2, CLO3 |
|
Participation |
2 |
8 |
16 |
CLO1, CLO2, CLO3 |
|
Writing pieces |
15 |
3 |
45 |
CLO1, CLO2, CLO3 |
|
Reflection piece: |
5 |
1 |
5 |
CLO1 |
Course Schedule
|
Module 1: Monday 3/7 to Sunday 3/13 |
||
|
Topics |
Introduction to AI, Ethics and Ethical Issues in AI |
Due Dates |
|
Readings |
This module introduces concepts in AI, ethics, and AI ethics. It reviews different types of AI (e.g., symbolic, machine learning) and ethical issues associated with them. It also provides a general ethical framework used across applied ethics fields (medical ethics, professional ethics, engineering ethics, etc.) |
|
|
Assignments |
Discussion Post: Write Personal introduction; describe your vision of how knowledge in information ethics will be integrated into your career plans/path. |
Friday, 3/11 |
|
|
Participation: Replies to posts |
Sunday, 3/13 |
|
Module 2: Monday 3/13 to Sunday 3/20 |
||
|
Topics |
AI Ethical Issues and Ethical Frameworks |
|
|
Readings |
This module introduces methods of ethical reasoning and ethical frameworks developed for AI. It connects these ethical frameworks and reasoning methods with the main ethics frameworks and with key problems of AI ethics. |
|
|
Assignments |
Discussion Post |
Friday, 3/18 |
|
|
Writing Piece |
Saturday, 3/19 |
|
|
Participation: Replies to posts |
Sunday, 3/20 |
|
Module 3: Monday 3/21 to Sunday 3/27 |
||
|
Topics |
Bias/Discrimination/Due Process |
|
|
Readings |
This module examines bias, discrimination, and justice issues that arise within AI. It looks at how bias arises from machine learning based in big data as well as bias that arises from lack of diversity within the technology companies that develop AI systems. Finally, it will look at dues process and fairness issues that are connected to automated decision-making that impacts human and social rights. |
|
|
Assignments |
Discussion Post |
Friday, 3/25 |
|
|
Participation: Replies to posts |
Sunday, 3/27 |
|
Module 4: Monday 3/28 to Sunday 4/3 |
||
|
Topics |
Accountability, explainable AI, liability, and records |
|
|
Readings |
This module examines accountability within the context of AI systems that distribute and/or automate decisions and actions that impact rights. It looks at the concept of explainable AI (XAI) and its relation to accountability. It examines concepts of liability for “autonomous” systems; and finally, it connects explainability and liability with core records concepts. |
|
|
Assignments |
Discussion Post |
Friday, 4/1 |
|
|
Writing Piece |
Saturday, 4/2 |
|
|
Participation: Replies to posts |
Sunday, 4/3 |
|
SPRING RECESS 4/4 to 4/10 |
||
|
Module 5: Monday 4/11 to Sunday 4/17 |
||
|
Topics |
Automation and Knowledge Work |
|
|
Readings |
This module examines ethical issues relating to employment and how they are impacted by “hyper-automation”. It reviews technologies such as robotic process automation (RPA) and business process automation and how their integration with AI impacts employment and working conditions. The module also looks at concepts of knowledge representation and knowledge management and explores how they may supplant or enhance knowledge work. |
|
|
Assignments |
Discussion Post: |
Friday, 4/15 |
|
|
Participation: Replies to posts |
Sunday, 4/17 |
|
Module 6: Monday 4/18 to Sunday 4/24 |
||
|
Topics |
Intellectual Property |
|
|
Readings |
This module examines special issues that AI poses for copyright and patent concepts. In particular, it looks at debates as to who is the rights holder where the creations of AI depend on data and environments provided by other parties than the developers and where the “autonomy” of AI systems either creates the semblance of a competing claimant (the AI system) or weakens the claim of the creator. |
|
|
Assignments |
Discussion Post |
Friday, 4/22 |
|
|
Writing Piece |
Saturday, 4/23 |
|
|
Participation: Replies to posts |
Sunday, 4/24 |
|
Module 7: Monday 4/25 to Sunday 5/1 |
||
|
Topics |
AI and Data Privacy |
|
|
Readings |
This module explores how issues in data privacy, which already constitute a major issue area in digital ethics, are extended and exacerbated by AI technologies that automate surveillance and that generate inferences beyond the data they use. It looks at the role of personal data in AI and how privacy rules apply to and affect the development and deployment of AI systems. |
|
|
Assignments |
Discussion Post |
Friday, 4/29 |
|
|
Participation: Replies to posts |
Sunday, 5/1 |
|
Module 8: Monday 5/2 to Sunday 5/8 |
||
|
Topics |
AI Governance |
|
|
Readings |
This module reviews general governance and regulatory frameworks being developed to mitigate potential harms caused by AI while reaping its potential benefits. It also looks at how to operationalize principles and rules within organizations. |
|
|
Assignments |
Discussion Post |
Friday, 5/6 |
|
|
Writing Piece |
Saturday, 5/7 |
|
|
Participation: Replies to posts |
Sunday, 5/8 |
Course Workload Expectations
Success in this course is based on the expectation that students will spend, for each unit of credit, a minimum of forty-five hours over the length of the course (normally 3 hours per unit per week with 1 of the hours used for lecture) for instruction or preparation/studying or course related activities including but not limited to internships, labs, clinical practica. Other course structures will have equivalent workload expectations as described in the syllabus.
Instructional time may include but is not limited to:
Working on posted modules or lessons prepared by the instructor; discussion forum interactions with the instructor and/or other students; making presentations and getting feedback from the instructor; attending office hours or other synchronous sessions with the instructor.
Student time outside of class:
In any seven-day period, a student is expected to be academically engaged through submitting an academic assignment; taking an exam or an interactive tutorial, or computer-assisted instruction; building websites, blogs, databases, social media presentations; attending a study group;contributing to an academic online discussion; writing papers; reading articles; conducting research; engaging in small group work.
Course Prerequisites
MARA 284 - Archives and AI has no prerequisite requirements.
Course Learning Outcomes
Upon successful completion of the course, students will be able to:
- Upon successful completion of this course, students will be able to identify ethical dimensions of artificial intelligence and systematically analyze issues using ethical concepts and principles.
- Upon successful completion of this course, students will be able to competently explain ethical risks associated with artificial intelligence within business organizations and articulate and defend policy positions and strategies that address these risks, particularly as applied to records and information management.
- Upon successful completion of this course, students will be able to identify and apply ethical frameworks and legal authorities to artificial intelligence technologies and use cases typical in commercial and public sector organizations.
Core Competencies (Program Learning Outcomes)
MARA 284 supports the following core competencies:
- A Articulate the ethics and values of archivists, records managers, and/or information professionals and discuss their role in social memory and organizational accountability.
- G Describe the legal requirements and ethical principles involved in managing physical and digital information assets and the information professional#s role in institutional compliance and risk management.
Textbooks
Recommended Textbooks:
- Mooradian, N. (2018). Ethics for records and information management. ALA. Available as Free eBook through King Library
Grading Scale
The standard SJSU School of Information Grading Scale is utilized for all iSchool courses:
| 97 to 100 | A |
| 94 to 96 | A minus |
| 91 to 93 | B plus |
| 88 to 90 | B |
| 85 to 87 | B minus |
| 82 to 84 | C plus |
| 79 to 81 | C |
| 76 to 78 | C minus |
| 73 to 75 | D plus |
| 70 to 72 | D |
| 67 to 69 | D minus |
| Below 67 | F |
In order to provide consistent guidelines for assessment for graduate level work in the School, these terms are applied to letter grades:
- C represents Adequate work; a grade of "C" counts for credit for the course;
- B represents Good work; a grade of "B" clearly meets the standards for graduate level work or undergraduate (for BS-ISDA);
For core courses in the MLIS program (not MARA, Informatics, BS-ISDA) — INFO 200, INFO 202, INFO 204 — the iSchool requires that students earn a B in the course. If the grade is less than B (B- or lower) after the first attempt you will be placed on administrative probation. You must repeat the class if you wish to stay in the program. If - on the second attempt - you do not pass the class with a grade of B or better (not B- but B) you will be disqualified. - A represents Exceptional work; a grade of "A" will be assigned for outstanding work only.
Graduate Students are advised that it is their responsibility to maintain a 3.0 Grade Point Average (GPA). Undergraduates must maintain a 2.0 Grade Point Average (GPA).
University Policies
Per University Policy S16-9, university-wide policy information relevant to all courses, such as academic integrity, accommodations, etc. will be available on Office of Graduate and Undergraduate Programs' Syllabus Information web page at: https://www.sjsu.edu/curriculum/courses/syllabus-info.php. Make sure to visit this page, review and be familiar with these university policies and resources.
In order to request an accommodation in a class please contact the Accessible Education Center and register via the MyAEC portal.
Download Adobe Acrobat Reader to access PDF files.
More accessibility resources.