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)
Algorithmic Gatekeeping for Professional Communicators Power Trust and Legiti...Arjen Van Dalen
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 5 discussing ways towards trusted and legitimate algorithmic gatekeeping.
The slides can be used in teaching, since they provide:
-summary of the main points of the chapter
-discussion questions
-suggestions for further reading (open access resources)
Algorithmic Gatekeeping for Professional Communicators Power Trust and Legiti...Arjen Van Dalen
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 1.
The slides can be used in teaching, since they provide:
-summary of the main points of the chapter
-discussion questions
-suggestions for further reading (open access resources)
Algorithmic Gatekeeping for Professional Communicators Power Trust and Legiti...Arjen Van Dalen
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 4: .
The chapter describes the power balance between Influencers and Instagram's algorithms and analyzes the legitimacy of the power of these algorithms.
The slides can be used in teaching, since they provide:
-summary of the main points of the chapter
-discussion questions
-suggestions for further reading (open access resources)
Ethical Issues in Machine Learning Algorithms (Part 2)Vladimir Kanchev
The presentation deals with types of biases found in AI/ML systems - data bias, algorithmic bias, and lack of interpretability. Reasons for their appearances are given, and major approaches for their reduction.
Slides from International Journalism Festival 2023, AI and Disinformation panel. Here the video https://www.journalismfestival.com/programme/2023/ai-and-disinformation
Algorithmic Gatekeeping for Professional Communicators Power Trust and Legiti...Arjen Van Dalen
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 5 discussing ways towards trusted and legitimate algorithmic gatekeeping.
The slides can be used in teaching, since they provide:
-summary of the main points of the chapter
-discussion questions
-suggestions for further reading (open access resources)
Algorithmic Gatekeeping for Professional Communicators Power Trust and Legiti...Arjen Van Dalen
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 1.
The slides can be used in teaching, since they provide:
-summary of the main points of the chapter
-discussion questions
-suggestions for further reading (open access resources)
Algorithmic Gatekeeping for Professional Communicators Power Trust and Legiti...Arjen Van Dalen
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 4: .
The chapter describes the power balance between Influencers and Instagram's algorithms and analyzes the legitimacy of the power of these algorithms.
The slides can be used in teaching, since they provide:
-summary of the main points of the chapter
-discussion questions
-suggestions for further reading (open access resources)
Ethical Issues in Machine Learning Algorithms (Part 2)Vladimir Kanchev
The presentation deals with types of biases found in AI/ML systems - data bias, algorithmic bias, and lack of interpretability. Reasons for their appearances are given, and major approaches for their reduction.
Slides from International Journalism Festival 2023, AI and Disinformation panel. Here the video https://www.journalismfestival.com/programme/2023/ai-and-disinformation
Explainable AI (XAI) stands to address all these challenges and focuses on developing methods and techniques that bring transparency and comprehensibility to AI systems. Its primary objective is to empower users with a clear understanding of the reasoning and logic behind AI algorithms’ decisions.
A REVIEW OF THE ETHICS OF ARTIFICIAL INTELLIGENCE AND ITS APPLICATIONS IN THE...IJCI JOURNAL
This study is focused on the ethics of Artificial Intelligence and its application in the United States, the
paper highlights the impact AI has in every sector of the US economy and multiple facets of the
technological space and the resultant effect on entities spanning businesses, government, academia, and
civil society. There is a need for ethical considerations as these entities are beginning to depend on AI for
delivering various crucial tasks, which immensely influence their operations, decision-making, and
interactions with each other. The adoption of ethical principles, guidelines, and standards of work is
therefore required throughout the entire process of AI development, deployment, and usage to ensure
responsible and ethical AI practices. Our discussion explores eleven fundamental 'ethical principles'
structured as overarching themes. These encompass Transparency, Justice, Fairness, Equity, NonMaleficence, Responsibility, Accountability, Privacy, Beneficence, Freedom, Autonomy, Trust, Dignity,
Sustainability, and Solidarity. These principles collectively serve as a guiding framework, directing the
ethical path for the responsible development, deployment, and utilization of artificial intelligence (AI)
technologies across diverse sectors and entities within the United States. The paper also discusses the
revolutionary impact of AI applications, such as Machine Learning, and explores various approaches used
to implement AI ethics. This examination is crucial to address the growing concerns surrounding the
inherent risks associated with the widespread use of artificial intelligence.
Oxford Internet Institute 19 Sept 2019: Disinformation – Platform, publisher ...Chris Marsden
With the move to a more digital, mobile, and platform-dominated media environment people increasingly find and access news and information via platforms like search engines and social media. These have empowered citizens in many ways and are important drivers of attention to established publishers but have also enabled the distribution of disinformation from a range of different actors. In a context where citizens are often increasingly sceptical of both platforms, publishers, and public authorities, what do we know about the scale and scope of disinformation problems and what can different actors do to counter the problems we face?
https://www.scl.org/articles/10662-interoperability-an-answer-to-regulating-ai-and-social-media-platforms
La inteligencia artificial (IA) está demostrando ser una espada de doble filo. Si bien esto se puede decir de la mayoría de las nuevas tecnologías, ambos lados de la hoja de IA son mucho más nítidos, y ninguno de los dos es bien entendido.
Este artículo busca ayudar ilustrando primero una gama de trampas fáciles de pasar por alto. A continuación, presenta marcos que ayudarán a los líderes a identificar sus mayores riesgos e implementar la amplitud y profundidad de los controles matizados necesarios para eludirlos. Por último, ofrece una visión temprana de algunos esfuerzos del mundo real que se están llevando a cabo actualmente para hacer frente a los riesgos de IA mediante la aplicación de estos enfoques.
Beyond-Accuracy Perspectives: Explainability and FairnessErasmo Purificato
Talk @ ISACT 2022: International Autumn School on Situation Awareness in Cognitive Technologies, co-located with ICHMS 2022, hosted by the University of Florida, November 16-19, 2022, Orlando, Florida, USA
In den letzten fünf Jahren ist das Ökosystem der auf KI basierten Anwendungen explodiert. Die Anwendungen haben jetzt schon einen grösseren Einfluss auf unser Leben, als den meisten Menschen bewusst ist. Mit den neuen Technologien sind Chancen und Risiken verbunden. Im Gegensatz zu den apokalyptischen Szenarien einer auf KI basierten Superintelligenz gibt es ganz reale Probleme mit diesen Systemen. Dieser Vortrag zeigt auf, wo diese Probleme liegen und warum es nötig ist, dass ein Diskurs darüber in der Politik und in der Öffentlichkeit immer dringlicher wird.
HUMAN RIGHTS IN THE AGE OF ARTIFICIAL INTELLIGENCE. This report is a product of Access Now. We thank lead author Lindsey Andersen for her
significant contributions. If you have questions about this report or you would like more information, you can contact info@accessnow.org.
In this presentation on "Advances in Artificial Intelligence and Machine Learning," we explore the transformative journey of AI and ML. We begin with an overview, defining AI and ML and highlighting their current significance. Next, we delve into the historical evolution of these technologies, tracing their path from their origins to present-day innovations.
Deep learning and neural networks take center stage, with a focus on their impact on applications like image recognition and natural language processing. We move on to discuss reinforcement learning and its role in creating self-learning systems, with examples from autonomous vehicles and game-playing AI.
Natural Language Processing (NLP) is showcased for its contributions to chatbots, sentiment analysis, and language translation. We then highlight computer vision's progress, including facial recognition and object detection.
AI's role in healthcare is discussed, featuring disease diagnosis, drug discovery, and personalized treatment plans. We address the ethical considerations of AI, emphasizing the importance of addressing bias and data privacy.
The presentation concludes by projecting the future of AI and ML, highlighting emerging trends such as quantum computing and AI ethics. We encourage questions and engagement from the audience to facilitate further discussion on this dynamic and ever-evolving field.
Van Dalen 2023 Algorithmic gatekeeping for professional communicators Power t...Arjen Van Dalen
Hi! This is my book 'Algorithmic gatekeeping for professional communicators - power, trust and legitimacy'. (OPEN ACCESS: https://doi.org/10.4324/9781003375258).
The book demonstrates the power of algorithms over public information and discusses ways towards legitimate and trusted algorithmic gatekeeping.
The book introduces central concepts and new empirical findings in this field in a way that is accessible for journalism researchers and students and social science students with an interest in social media and the influence of automation on news and public debate.
This book comes with 5 sets of powerpoint slides which can be used in teaching since they provide:
-summary of the main points of each chapter
-discussion questions
-suggestions for further reading (open access resources)
These slides are available from my slideshare page
Algorithmic Gatekeeping for Professional Communicators Power Trust and Legiti...Arjen Van Dalen
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 3: The mediating power of algorithms.
The chapter describes explores the role of YouTube's algorithms in the spread and limitation of misinformation about Autism.
The slides can be used in teaching, since they provide:
-summary of the main points of the chapter
-discussion questions
-suggestions for further reading (open access resources)
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Explainable AI (XAI) stands to address all these challenges and focuses on developing methods and techniques that bring transparency and comprehensibility to AI systems. Its primary objective is to empower users with a clear understanding of the reasoning and logic behind AI algorithms’ decisions.
A REVIEW OF THE ETHICS OF ARTIFICIAL INTELLIGENCE AND ITS APPLICATIONS IN THE...IJCI JOURNAL
This study is focused on the ethics of Artificial Intelligence and its application in the United States, the
paper highlights the impact AI has in every sector of the US economy and multiple facets of the
technological space and the resultant effect on entities spanning businesses, government, academia, and
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delivering various crucial tasks, which immensely influence their operations, decision-making, and
interactions with each other. The adoption of ethical principles, guidelines, and standards of work is
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structured as overarching themes. These encompass Transparency, Justice, Fairness, Equity, NonMaleficence, Responsibility, Accountability, Privacy, Beneficence, Freedom, Autonomy, Trust, Dignity,
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ethical path for the responsible development, deployment, and utilization of artificial intelligence (AI)
technologies across diverse sectors and entities within the United States. The paper also discusses the
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With the move to a more digital, mobile, and platform-dominated media environment people increasingly find and access news and information via platforms like search engines and social media. These have empowered citizens in many ways and are important drivers of attention to established publishers but have also enabled the distribution of disinformation from a range of different actors. In a context where citizens are often increasingly sceptical of both platforms, publishers, and public authorities, what do we know about the scale and scope of disinformation problems and what can different actors do to counter the problems we face?
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Este artículo busca ayudar ilustrando primero una gama de trampas fáciles de pasar por alto. A continuación, presenta marcos que ayudarán a los líderes a identificar sus mayores riesgos e implementar la amplitud y profundidad de los controles matizados necesarios para eludirlos. Por último, ofrece una visión temprana de algunos esfuerzos del mundo real que se están llevando a cabo actualmente para hacer frente a los riesgos de IA mediante la aplicación de estos enfoques.
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-discussion questions
-suggestions for further reading (open access resources)
These slides are available from my slideshare page
Algorithmic Gatekeeping for Professional Communicators Power Trust and Legiti...Arjen Van Dalen
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Algorithmic Gatekeeping for Professional Communicators Power Trust and Legitimacy - Chapter 2.pptx
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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