Algorithmic Transparency in the News Media

By Nicholas A. Diakopoulos, Michael Koliska. 2017

421 citations recorded by the source corpus.

Subfields: Law, Safety Research, Sociology and Political Science

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Citations in the corpus

Ethical Implications and Accountability of Algorithms (2018, background): “Researchers and reporters run afoul of the CFAA, the Computer Fraud and Abuse Act, when performing simple tests to identify unjust biases in algorithms (Diakopoulos 2015; Diakopoulos and Koliska 2017).”
Experiencing Algorithms: How Young People Understand, Feel About, and Engage With Algorithmic News Selection on Social Media (2021, background): “Young people suggested that increasing algorithmic transparency (Diakopoulos & Koliska, 2017) might be a way for news organizations to overcome this.”
Explanations as Mechanisms for Supporting Algorithmic Transparency (2018, background): “Accountability is often discussed as the ultimate goal of transparency; it is thought to be a means of shifting the balance of power [1, 14] via increased scrutiny [13, 56].”
Do You Trust an AI-journalist? A Credibility Analysis of News Content with AI-Authorship (2021, background): “Transparency is not only ethically desirable (Diakopoulos and Koliska, 2016; Mittelstadt, 2016; Brendel et al. , 2021), it can also increase credibility in journalistic articles (Meier and Reimer, 2018).”
The Illusion of Control: Placebo Effects of Control Settings (2018, background): “Relatedly, explorations of the ethics of misrepresentative control settings might fit well within emerging conversations about the ethics of algorithmic systems [5, 11, 13, 44].”
Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry (2020, methodology): “Regarding aversion to algorithm-generated text, research within digital journalism has assessed people ’ s perceptions of news generated by algorithms (Carlson, 2015; Diakopoulos & Koliska, 2017).”
Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda (2018, background): “For instance, Diakopoulis looks at this from a computational journalism perspective [47] and curates a list of newsworthy algorithms used by the U.”
ORES (2019, neutral): “There are repeated calls to address power dynamics and bias through transparency and accountability of the algorithms that govern public life and access to resources [9, 36].”
Communicating Algorithmic Process in Online Behavioral Advertising (2018, background): “Increased process communication has also been recommended for social matching systems [28], team formation tools [26], rating platforms [15], and algorithmic journalism [12] to build informed and trustworthy interactions between users and systems; and yet effectively revealing an algorithmic process”
How Platform-User Power Relations Shape Algorithmic Accountability: A Case Study of Instant Loan Platforms and Financially Stressed Users in India (2022, background): “Technical and organizational opacity are viewed as primary barriers to fostering accountability [27, 98], suggesting the need for trans-parency from technology providers [35–37].”
User Perceptions of Algorithmic Decisions in the Personalized AI System:Perceptual Evaluation of Fairness, Accountability, Transparency, and Explainability (2020, background): “Taken together, FATE brings up key considerations in the design and development of algorithm services (Diakopoulos & Koliska, 2016; Shin et al., 2020).”