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JOHN CABOT UNIVERSITY

COURSE CODE: "CMS 375"
COURSE NAME: "AI and Critical Art Practices: Ethics, Aesthetics, Labor"
SEMESTER & YEAR: Spring 2024
SYLLABUS

INSTRUCTOR: Donatella Della Ratta
EMAIL: [email protected]
 HOURS: TTH 10:00 AM 11:15 AM
TOTAL NO. OF CONTACT HOURS: 45
CREDITS: 3
PREREQUISITES: Prerequisites: COM 311 or permission of the instructor
OFFICE HOURS:
COURSE DESCRIPTION:
This course explores the latest developments in the field of artificial intelligence (AI) through critical artistic practices. By looking at different modes of cutting-edge research-based work from artists, scholars, and activists from across the planet, the course reflects upon the implications of AI in transforming traditional notions of creativity, authorship, and labor in general. Such critical works will be used to shed light on the materialities of this technological innovation, its impact on the environment, and the processes of extraction and exploitation that are embedded within the very practice of compiling a dataset and training Large Language Models (LLMs) upon which generative AI works. The course takes on a decolonial approach, considering how technology has been historically used as a tool of colonialism, and how contemporary advancements in the field of AI continue to perpetuate the colonial power dynamics of extraction and exploitation. It also considers how a de-colonial standpoint can offer alternative perspectives for understanding and critiquing the impact of AI on society, culture, and politics.
SUMMARY OF COURSE CONTENT:
This course explores the latest developments in the field of artificial intelligence (AI) through critical artistic practices. By looking at different modes of cutting-edge research-based work from artists, scholars, and activists from across the planet, the course reflects upon the implications of AI in transforming traditional notions of creativity, authorship, and labor in general. Such critical works will be used to shed light on the materialities of this technological innovation, its impact on the environment, and the processes of extraction and exploitation that are embedded within the very practice of compiling a dataset and training Large Language Models (LLMs) upon which generative AI works. The course takes on a decolonial approach, considering how technology has been historically used as a tool of colonialism, and how contemporary advancements in the field of AI continue to perpetuate the colonial power dynamics of extraction and exploitation. It also considers how a de-colonial standpoint can offer alternative perspectives for understanding and critiquing the impact of AI on society, culture, and politics.
LEARNING OUTCOMES:

This course explores the latest developments in the field of artificial intelligence (AI) through critical artistic practices. By looking at different modes of cutting-edge research-based work from artists, scholars, and activists from across the planet, the course reflects upon the implications of AI in transforming traditional notions of creativity, authorship, and labor in general. Such critical works will be used to shed light on the materialities of this technological innovation, its impact on the environment, and the processes of extraction and exploitation that are embedded within the very practice of compiling a dataset and training Large Language Models (LLMs) upon which generative AI works. The course takes on a decolonial approach, considering how technology has been historically used as a tool of colonialism, and how contemporary advancements in the field of AI continue to perpetuate the colonial power dynamics of extraction and exploitation. It also considers how a de-colonial standpoint can offer alternative perspectives for understanding and critiquing the impact of AI on society, culture, and politics.

 
TEXTBOOK:
Book TitleAuthorPublisherISBN numberLibrary Call NumberCommentsFormatLocal BookstoreOnline Purchase
Atlas of AIKate CrawfordYale University Press9780300252392, 0300252390 pls purchase a hard copy for my personal use thanksHard Copy  
REQUIRED RESERVED READING:
NONE

RECOMMENDED RESERVED READING:
NONE
GRADING POLICY
-ASSESSMENT METHODS:
AssignmentGuidelinesWeight
Attendance and participationParticipation means doing the assigned readings and actively contributing to class discussions. Each student has to lead at least a group discussion based on readings & weekly assignments during the semester. For attendance policy pls read attendance requirements. 15%
reflecting and experimenting with AI A series of creative assignments & reflection bits connected to the weekly topics. Detailed guidelines will be provided. 30%
midterm presentationdetailed guidelines will be provided20%
Curating AIFinal project. Detailed guidelines will be provided25%
participation to DDD lectures calendar will be provided at the beginning of the semester5%
DDD workshopStudents will have to attend one of the workshops offered by the DDD lecture series and write a short reflection on it. Calendar will be provided at the beginning of the semester.5%

-ASSESSMENT CRITERIA:
AWork of this quality directly addresses the question or problem raised and provides a coherent argument displaying an extensive knowledge of relevant information or content. This type of work demonstrates the ability to critically evaluate concepts and theory and has an element of novelty and originality. There is clear evidence of a significant amount of reading beyond that required for the course.
BThis is highly competent level of performance and directly addresses the question or problem raised.There is a demonstration of some ability to critically evaluatetheory and concepts and relate them to practice. Discussions reflect the student’s own arguments and are not simply a repetition of standard lecture andreference material. The work does not suffer from any major errors or omissions and provides evidence of reading beyond the required assignments.
CThis is an acceptable level of performance and provides answers that are clear but limited, reflecting the information offered in the lectures and reference readings.
DThis level of performances demonstrates that the student lacks a coherent grasp of the material.Important information is omitted and irrelevant points included.In effect, the student has barely done enough to persuade the instructor that s/he should not fail.
FThis work fails to show any knowledge or understanding of the issues raised in the question. Most of the material in the answer is irrelevant.
-ATTENDANCE REQUIREMENTS:
-ATTENDANCE REQUIREMENTS: 

Class procedure:  Use of cell phones is strictly forbidden during class. Please make sure that your cell phone is turned off (and not just muted) when class starts. Kindly note that any infringement of such policy shall automatically result in a F grade in participation. 

Laptops are allowed for note-taking and for class-related purposes, such as experimenting with machine learning, etc. 

Please consider that more than 3 absences will automatically result in lowering your participation grade by one letter grade for each absence. Anything above 6 absences will result in failing the course.

If you have a serious problem which causes you to miss classes more than allowed here, please contact the Dean's Office.

Lateness: If unexcused, students more than 10 minutes late are marked as absent. Late arrival (less than 10 minutes) is marked as such, and 3 late arrivals are counted as one absence. 
ACADEMIC HONESTY
As stated in the university catalog, any student who commits an act of academic dishonesty will receive a failing grade on the work in which the dishonesty occurred. In addition, acts of academic dishonesty, irrespective of the weight of the assignment, may result in the student receiving a failing grade in the course. Instances of academic dishonesty will be reported to the Dean of Academic Affairs. A student who is reported twice for academic dishonesty is subject to summary dismissal from the University. In such a case, the Academic Council will then make a recommendation to the President, who will make the final decision.
STUDENTS WITH LEARNING OR OTHER DISABILITIES
John Cabot University does not discriminate on the basis of disability or handicap. Students with approved accommodations must inform their professors at the beginning of the term. Please see the website for the complete policy.
SCHEDULE

Draft course outline

Week 1: Introduction to the study of artificial intelligence and machine learning. Historical overview up to the current debate on generative pre-trained transformer chatbots (ChatGPT), A.I. Image generators (Midjourney, Dall-e, etc.), etc.

  

Week 2: Critiques of A.I. in artistic and activist practices. An overview of the most interesting practices focusing on critiquing different aspects of A.I. through art and activism.

 

Week 3: A.I. and changing notions of authorship, ownership, creativity. Focus on how A.I. reformulates ideas of creativity and originality.

  

Week 4: Ethics of the dataset. How datasets are created, and what are the ethical implications of 'grabbing' data in the wild. How notions of privacy and consent are impacted by the creation of large datasets to train A.I.s.

  

Week 5: Politics of the dataset. Discussing on the inherent biases that get embedded in datasets, and how to make them apparent and object of accountability. Discussing the idea of 'auditing' datasets.

  

Week 6: midterm

  

Week 7: Materialities of A.I.s: infrastructure and environmental impact. Demystifying A.I. and technology in general as 'eco-friendly', and showing instead the dramatic environmental cost of using such tools. 

 

Week 8: Materialities of A.I.s: labor and exploitation. Discussing the exploitation of cheap and unskilled labor, most likely located in the non-West, to build and maintain A.I.s.

  

Week 9: Extractivism and data colonialism. An emerging face of colonialism and extractivist practices inherently connected to the implementation of A.I. technology.

  

Week 10: Gender biases. Discussing implicit gender bias in A.Is. 

  

Week 11: Racial biases. Discussing implicit racial bias in A.Is. 

  

Week 12: A.I. image generators and the question of 'truth'. How Midjourney, Dall-e, and the likes get to reframe the question of 'truth' and that of indexical value of an image. 

  

Week 13: Synthetic images in the post-evidentiary turn. How A.I.generated images change the notion of 'evidence' and of 'bearing witness'.

  

Week 14: recap and wrap up