Late Additions
Asynchronous Session
A Design Framework For Auditing AI View Digital Media
Paper Presentation in a Themed Session Ayse Arslan
Rising concern for the societal implications of artificial intelligence systems has inspired a wave of academic and journalistic literature in which deployed systems are audited for harm by investigators from outside the organizations deploying the algorithms. However, it remains challenging for practitioners to identify the harmful repercussions of their own systems prior to deployment, and, once deployed, emergent issues can become difficult or impossible to trace back to their source. This research study introduces a framework for algorithmic auditing that supports artificial intelligence system development end-to-end, to be applied throughout the internal organization development lifecycle. Each stage of the audit yields a set of documents that together form an overall audit report, drawing on an organization’s values or principles to assess the fit of decisions made throughout the process. The proposed auditing framework is intended to contribute to closing the accountability gap in the development and deployment of large-scale artificial intelligence systems by embedding a robust process to ensure audit integrity.
News Media Trust at Different Levels of Analysis: Impact on Civic Engagement View Digital Media
Paper Presentation in a Themed Session Dina Ali, Hesham Dinana
Trust has long been deemed a critical element that influences audiences' relationship with the news media. Particularly after the percentage of online users is rapidly expanding around the world during the COVID-19 outbreak. New technologies and social media have the power to polarize, manipulate, and entrench public opinion. With high-choice media ecosystems in today's world, news trust and its relationship with civic engagement have taken on new significance. The novelty of this research is recognizing and highlighting the news trust at different levels of analysis in the Egyptian context and understanding the rationale for that trust (why). This study holds a new perspective by testing a developed conceptual model. The developed model has tested the news trust in general. Therefore, the study's objective is focused review of illustrating the relationship between the news trust at different levels of analysis and the civic engagement. A mixed-method approach was applied (quantitative and qualitative). In-depth interviews with professionals and audiences were carried out to examine the sequence of the developed model and what is practically implemented by the news organizations. Findings from these interviews show that building news trust takes time; however, news trust is established when the audience has a level of awareness. An online survey was also conducted using a non-probability sampling (441 responses). Results illustrate that audiences’ propensity to trust and the news at different levels of analysis are correlated to civic engagement.
Artificial Intelligence, QAnon, and the Gamification of Alienation: Regulation, Repurposing, and Resistance to Roboprocesses
Paper Presentation in a Themed Session Chris Hables Gray, Angel Gordo López
While there is no consensus definition of artificial intelligence (AI), machine learning using data mining for refining pattern recognition algorithms is considered AI by most of the public and all the marketers involved. It also fits many of the definitions used by practitioners in the field. Machine learning has proven very effective for maximizing profits for social media companies and also propagating extreme ideologies such as QAnon. The distributed aspects of digital communication curated by social media machine learning and framed as a military psychological operation (psyop) has produced the current QAnon phenomena. Considering the exponential improvements pattern recognition approaches are undergoing and their expansion into many more domains, not just text and art creation but a whole range of what have been termed roboprocesses. It seems inevitable that many more new forms of political and social disruptions driven by machine learning pattern recognition AI will profoundly shape society in the near future.
