Hi! I prepared slides for each chapter of my book 'Algorithmic gatekeeping for professional communicators - power, trust and legitimacy'. (OPEN ACCESS: https://doi.org/10.4324/9781003375258) These are the slides for chapter 2: algorithm aversion. The slides can be used in teaching, since they provide: -summary of the main points of the chapter -additional graphs not available in the book -discussion questions -suggestions for further reading (open access resources)

1. Available Open Access: https://doi.org/10.4324/9781003375258
Chapter 2:
Algorithm Aversion

2. Van Dalen, Arjen (2023). Algorithmic Gatekeeping for Professional Communicators: Power, Trust and Legitimacy.
Making sense of news algorithms
 Mental models of unknown technologies
-Operational and abstract theories
 Anthropomorphism and machine heuristics
Trust and approval of algorithms depend on
Mechanical vs human tasks
Mistake made
Connotations with term algorithm
Generational differences

3. Van Dalen, Arjen (2023). Algorithmic Gatekeeping for Professional Communicators: Power, Trust and Legitimacy.
Research questions and method
 How do people perceive the strengths and weaknesses of news algorithms compared to human
journalists?
 Do people trust and approve of news algorithms?
Representative surveys in Denmark
Two survey-embedded experiments
-one preregistered https://aspredicted.org/blind.php?x=mr8s63

4. Van Dalen, Arjen (2023). Algorithmic Gatekeeping for Professional Communicators: Power, Trust and Legitimacy.
Low trust compared to journalists and algorithms
4
1 2 3 4 5
Influencers on social media
news written by computer algorithms
news selected by computer algorithms
news on social media
robots
computer algorithms
artificial intelligence
people I meet for the first time
Danish journalists
The Danish news media
DR Nyheder
Note: mean score on a scale from 1 (low trust) to 5 (high trust)

5. Van Dalen, Arjen (2023). Algorithmic Gatekeeping for Professional Communicators: Power, Trust and Legitimacy.
Low trust not due to the term ‘algorithm’
• Note: mean score on a scale from 1 (low trust) to 5 (high trust)
5
1 1.5 2 2.5 3 3.5 4 4.5 5
trust in news
selected by…
trust in news
written by…
algorithms automated computer systems

6. Van Dalen, Arjen (2023). Algorithmic Gatekeeping for Professional Communicators: Power, Trust and Legitimacy.
Who is best at selecting the following type of
news?
6
0 10 20 30 40 50 60 70 80 90 100
trustworthy content
news that offers different perspectives
content that is surprising
balanced content
objective content
neutral content
content that has personal relevance for me
computer algorithms equally well journalists do not know

7. Van Dalen, Arjen (2023). Algorithmic Gatekeeping for Professional Communicators: Power, Trust and Legitimacy.
Table 2.1 here

8. Van Dalen, Arjen (2023). Algorithmic Gatekeeping for Professional Communicators: Power, Trust and Legitimacy.
Who should make decisions in the following areas
of public life?
6/2/2023
8
0 10 20 30 40 50 60 70 80 90 100
matching unemployed people with firms
accounting
the news i receive
targeted political advertisement
speeding tickets
allocation of public funds
hospital patient prioritization
Job hiring decisions
Parole (who should get it and when you are eligible)
Triage for nursing home
Who gets elected to the local city council
court cases
decisions on moral dillemas (like euthanasia)
placing children outside of their family
Humans alone Humans and algorithms together Algorithms alone

9. Van Dalen, Arjen (2023). Algorithmic Gatekeeping for Professional Communicators: Power, Trust and Legitimacy.
Let journalists and algorithms work together
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Boomers
Generation X
Millenials
Generation Z
Alle generationer
Who should decide which news I receive?
Humans alone Humans and algorithms together Algorithms alone

10. Van Dalen, Arjen (2023). Algorithmic Gatekeeping for Professional Communicators: Power, Trust and Legitimacy.
Conclusion:
Trust and approve of news algorithms
 Distrust
Low trust in news algorithms
Concerns about objectivity and viewpoint diversity
Erring human journalist prefered to news algorithm
 Algorithm approval
Personalization
Younger generations
More approving of algorithms in news than algorithms other areas of public life

11. Van Dalen, Arjen (2023). Algorithmic Gatekeeping for Professional Communicators: Power, Trust and Legitimacy.
Discussion questions
 Think about public debate around generative AI, such as ChatGPT and its influence on journalism.
Can you see examples of anthropomorphism,Hollywood Robot Syndrom or Frankenstein complex?
 Can you explain why computer algorithms are seen as better at selecting news that is neutral
compared to news that is objective, balanced or trustworthy? Use the terms ‘machine heuristic’ and
‘human’ and ‘mechanical tasks’ in your answer.
 Can you see any patterns in where people want algorithms to play a role in public life and where not
(Figure 2.2)? What do these patterns say about what kind of task people think ‘selecting the news I
receive’ is?
 Contrary to expectations, approval does not decrease more when an automated computer system
makes a journalistic error than when a journalist makes a journalistic error (See Table 2.1). Can you
think of explanations why that might be the case?
 Among Generation Z, 27% believes that algorithms alone should decide which news they receive.
Do you think that they will continue to think so as they grow older? What could distinguish the
people belonging to this 27% from the rest of Generation Z? Think for example of their media use,
education, or interests.
 How could journalists and algorithms work together in practice to select the news?

12. Van Dalen, Arjen (2023). Algorithmic Gatekeeping for Professional Communicators: Power, Trust and Legitimacy.
Recommended reading (open access)
Lee, M. K. (2018). Understanding perception of algorithmic decisions: Fairness, trust, and
emotion in response to algorithmic management. Big Data & Society, 5(1): 1–
16. (https://journals.sagepub.com/doi/epub/10.1177/2053951718756684)
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People
erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology:
General, 144(1): 114–126.
(https://repository.upenn.edu/cgi/viewcontent.cgi?article=1392&context=fnce_papers)
Thurman, N., Moeller, J., Helberger, N., & Trilling, D. (2019). My Friends, editors,
algorithms, and I: Examining audience attitudes to news selection. Digital Journalism, 7(4):
447–469. (https://www.tandfonline.com/doi/full/10.1080/21670811.2018.1493936)
Fletcher, R., & Nielsen, R. K. (2019). Generalised scepticism: How people nav-igate news
on social media. Information, Communication & Society, 22(12): 1751–1769.
(https://ora.ox.ac.uk/objects/uuid:345f1f65-c6e1-4021-b8d2-
a76dee98817d/download_file?safe_filename=generalised%2Bscepticism.pdf&file_format=a
pplication%2Fpdf&type_of_work=Journal+article)

13. Van Dalen, Arjen (2023). Algorithmic Gatekeeping for Professional Communicators: Power, Trust and Legitimacy.
Available Open Access: https://doi.org/10.4324/9781003375258
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