MTM Selected Issues 1:
Sensors, Algorithmic News, and Artificial Intelligence in Journalism

Master - SS '23

C. Loebbecke

2 SWS, 6CP

Fridays, 12:30 - 5:30 pm, max. 6 sessions!
Start: March 31, '23

Location: 100 HS XVIII (ground floor, main building)

Held in English

Pre-Assignment Deadline: March 21, '23, 11:00 am, via eMail from your sMail account (see below)

Opportunity:
You can do most of the required self-studying by now! The earlier and the more carefully you will watch the seven video presentations (four videos) and the three chapters, the much faster can oyu finalize the assignments and the better will be your performance throughout the semester. we look forward to guided discussions, no exam, no learning by heart, no memorizing!

Held in English

Overview

This is a master level discussion course! We will understand and reflect upon a portfolio of presentations on 'Artificial Intelligence (AI) and Management' from the Academy of Management Conference (August 2020) and put them in perspective against the respective presenter's academic vita. We will then select relevant discussion elements that we can examine and discuss them in light of publishing journalistic content. Similarly, we will get practitioner input (presentation, visit) and, again, reflect and discuss it in perspective against our earlier insights. The focus is on the impact of deploying Sensors, Algorithmic News, Artificial Intelligence, and Deepfakes in journalism. You will not need any technological experience, but should be open to think through and discuss openly.

As we will offer a broad range of considerations, both for AI and management and for journalistic production, students can shape the focus of our discussion after having read and heard the material. During the course sessions, we will have a grounded discussion among students on the cutting edge topics covered in the material and transfer their arguments to journalism. The idea is to develop and fine-tune one's arguments and line of thinking while being aware of rather contrasting insights and thoughts. The idea is NOT to know who said what and repeat anybody's text or words.

Hence, the main learning goals are (1) using academic research to understand contrasting views on AI in journalism, (2) transfer cutting edge technological developments to academic lines of arguments (here: around journalistic content); and (3) develop one's own thoughts / opinions (not technical solutions!) and back those up with academic arguments.

You already find the tasks for the the Intermediate Assignments and the Final Assignment below. This allows you to manage your own time. You can watch the videos and read the texts whenever you want. We suggest that you hand in the Intermediates and the Final after you have participated in the course discussions. Beyond the "watching and reading", the course sessions should be very helpful for accomplishing the required tasks. All Assignments intertwine with the oral participation. The Final Assignment is a guided integration of previously covered course material - the more you got out of the course sessions, the easier and faster it will be.

Please note that we changed not the references, but the tasks compared to the previous semester(s).

Dates (max. 5)

March 31 (online via Zoom), Apr. 14, Apr. 21, May 5, May 19, May 26, Jun. 09, Jun. 16, Jun. 30 (classroom HS XVIII) - all '23

Starting on March 31, allows us organizing the last course session before Pentecost ('Pfingsten'), avoiding too many 'Brückentage' and finalizing all grading elements latest by mid June.

Pre-Assignment due March 21, '23, 11:00 am, via eMail from your sMail account.

Watch four video clips*, each <15 minutes and each entailing a short presentation on an 'AI and Management' topic and thoughts provided by a pre-assigned discussant (except for Henfridsson), whose job is to challenge / question the given presentation. Note that these are NOT research presentations, but summaries of thoughts for entering a (here: "your") discussion; i.e., there is no research method, etc. This gives you in total seven persons providing their views.

1)  Google all seven academic scholars (four speakers and three discussants). Go to any website, BUT NOT their current employer's. Find a CV that the scholar has uploaded. Tell us where they are from, when and where they got their PhD, and at which academic institutions (including city and country) they were and are employed. Find out if they had significant academic roles, e.g., journal editorial roles or positions in the Association for Information Systems (AIS). Do NOT present their research topics, major publications or anything like that  [7 x ca. 50 words, 7 bullet point lists would be perfect].

2)  For each of the seven presentations tell us a major message / issue / punch line that they cover with regard to AI and management. It should be each a short punch line that you can remember throughout the course; you will need them in the next task of the pre-assignment. Use YOUR words. Do not repeat the presentation title or a slide title. Make sure that each punch line is different from the other six ones [7 x 30-50 words].

3)  Choose three of your seven punch lines (see task no. 2), and for those three tell us how you see its relevance in the field of journalism (press / TV or radio news) – not blogs, not entertainment. Again, make sure that you have three different lines of thought (FYI: during the course, we will do this for all seven video presentations).

[4) Be prepared to present (NOT read!) all your answers in the discussion and to comment on your fellow students' contributions, "know your seven new buddies and their punch lines" - graded as class participation, not as Pre-Assignment.]

*Henfridsson, O., AI Capability for Data Network Effects, duration: 09:05, size: 50 MB (Download)

*Keil, M., AI – Are we asking the right questions?, Discussant: Beck, R. - duration: 13:26, size: 16 MB (Download)

*Rai, A., How Will the AI Genie Behave?, Discussant Tuunainen, V. - duration: 14:53, size: 104 MB (Download)

*Ramaprasad, J., AI & Decision-Making: Programming, Biases, and Moral Decision-Making, Discussant: Mooney, J. - duration: 14:41, size: 96 MB (Download)

Formal requirements
- State your name, matr.-number, sMail address, and study program and its start date on top of the first page; then continue typing, no cover sheet!

- Have only your name in the header of each page.
- Have an empty line before the task, copy the task, and in the next line start the answer. After a task, have one empty line before typing the next task and the answer.

- Do not start a new page for every new task.
- Times New Roman (TNR) 12, single-spaced.

- Have 2 points (2 pts.) before and after each (1) paragraph.
- Scientific writing style - no jokes, no slang, hardly any passive voice.
- References IN THE TEXT (no footnotes), no 'ibid.' - s. some Anglo-American academic management journals.
- NO author first names, NO repetition of reference titles in the assignment text.
- For formatting the reference list, see our website.
- Consistent format (including spacing, etc.).
- Page numbers of references only for word-by-word citations.
- Complete reference list formatted appropriately with all required information per file (see mtm.uni-koeln.de) - even if it is only one source.

Delivery
Please send an eMail from your sMail account to claudia.loebbecke<at>uni-koeln.de and irina.boboschko<at>uni-koeln.de; attach a non-protected word file (.doc or .docx)
- Subject line: MTM-Issues1-Pre-Lastname (your lastname only, no accent, etc.)
- File name: MTM-Issues1-Pre-Lastname.doc(x) (your lastname only, no accent, etc.)
 

1st Intermediate Assignment - due Apr. 12, '23 (later deadline to be communicated in the 1st session)

Read Chapters 1-3 of Diakopoulos, N. (2019) Automating the News: How Algorithms Are Rewriting the Media, Harvard University Press, Cambridge, MA, US.* and for

*Diakopoulos (2019), Ch1 - summarize relevant points (not the examples, etc.) [100-150 words] and relate it to the pre-assignment videos by Keil and Ramaprasad [2 x 50-100 words]

*Diakopoulos (2019), Ch2 - summarize relevant points (not the examples, etc.) [100-150 words] and relate it to the pre-assignment videos by Henfridsson and Beck [2 x 50-100 words]

*Diakopoulos (2019), Ch3 (ONLY up to 'The Business Case', s. yellow highlighting) [100-150 words] - summarize relevant points (not the examples, etc.) and relate it to the pre-assignment videos by Rai and Tuunainen

 [2 x 50-100 words]
Notes to the selected chapter above 

For the complete book, we refer to www.hup.harvard.edu/catalog.php?isbn=9780674976986.

Formal requirements – see Pre-Assignment

Delivery
Please send an eMail from your sMail account to claudia.loebbecke<at>uni-koeln.de and irina.boboschko<at>uni-koeln.de; attach a non-protected word file (.doc or .docx)
- Subject line: MTM-Issues1-Inter1-Lastname (your lastname only, no accent, etc.)
- File name: MTM-Issues1-Inter1-Lastname.doc(x) (your lastname only, no accent, etc.)

2nd Intermediate Assignment - due April 19, '23

Read Tarafdar et al. '22 (DOI: 10.1111/isj.12389) carefully and

1)  Summarize its main findings (not suggestions, background, or assumptions!) Make sure you precisely define and delineate terms in your summary taken from the paper (e.g., human–algorithm interactions, algorithmic work) [150- 200 words]

2)  Transfer the research approach / line of arguments to journalism: Who would you interview to find out what -- and why so? [150-200 words]

3)  Outline, which insights of the paper would you argue to be relevant in our discussions on AI in journalism; which insights do not seem insightful when focusing on journalism only -- and why so?  [150- 200 words]

Formal requirements – see Pre-Assignment

Delivery
Please send an eMail from your sMail account to claudia.loebbecke<at>uni-koeln.de and irina.boboschko<at>uni-koeln.de; attach a non-protected word file (.doc or .docx)
- Subject line: MTM-Issues1-Inter2-Lastname (your lastname only, no accent, etc.)
- File name: MTM-Issues1-Inter2-Lastname.doc(x) (your lastname only, no accent, etc.)

Final Assignment - 1st complete draft for class discussion due May 03, '23

Details to be determined during the course. Building a guided argument along the three earlier assignments (four AoM videos, Diakopoulos chapters and Tarafdar et al.'s essence).

Formal requirements – see Pre-Assignment

Delivery
Please send an eMail from your sMail account to claudia.loebbecke<at>uni-koeln.de and irina.boboschko<at>uni-koeln.de; attach a non-protected word file (.doc or .docx)
- Subject line: MTM-Issues1-Final1-Lastname (your lastname only, no accent, etc.)
- File name: MTM-Issues1-Final1-Lastname.doc(x) (your lastname only, no accent, etc.)

Course Grading

- 25%: Pre-Assignment

- 25%, individual: Intermediate Assignments
- 30%, individual: Active participation throughout the sessions - building on assignments / having digested the material

- 20%, individual: Final Assignment and presentation
It is required to at least 'pass' (grade 4.0 or better) each grading element for passing the course.
'Alle Prüfungselemente müssen mindestens bestanden sein.'

Required Course Registration:

(1) Hand in Pre-Assignment by March 21, '23, 11 am (minimum passing that grading element). All who will hand in the Pre-Assignment by the deadline March 21, '23, will be admitted to / registered for the course.
On March 31, '23, we will list you as course participant in KLIPS and THEREUPON you must
(2) Register for the exam on KLIPS by Apr. 11, '23.

Voluntary, but very helpful: If you are interested in taking the course, please send an eMail from your sMail account to three persons: claudia.loebbecke<at>uni-koeln.de, astrid.obeng-antwi<at>uni-koeln.de, and irina.boboschko<at>uni-koeln.de. The eMail must list the course, your first name, your last name, your matr.-number, and your study program. We suggest that you also add a phone number so that we can help on short notice.
 

For any course related questions, please contact claudia.loebbecke<at>uni-koeln.de from your sMail account.

© Department of Media and Technology Management