Editorial Note for Special Issue on Al and Fake News, Mis(dis)information, and Algorithmic Bias

Artificial intelligence (AI) continues to shape the lives of media users today (Wölker & Powell, 2021). Search engines, social media, and other over-thetop service platforms are fueled by data automated and organized through AI and algorithms, which in turn control users and markets. Similarly, the platformization of news and journalism is a growing trend (Dijck et al., 2018). The process of platformization is increasingly facilitating the economic, organizational, and social extensions of digital platforms into online and media ecosystems, fundamentally changing the operations of media industries and journalistic practices. Recently, platformization accelerated due to the drastic breakthroughs in machine learning. Specifically, it is machine learning algorithms that enable different sets of automated processes that transform input data into desired output (Dijck et al., 2018). Algorithms play a key role in curating what information is considered most relevant to users. While popular and effective in practice, these features come with the risk of systematic discrimination, limited transparency, and vague accountability (Moller et al., 2018). While algorithmic filtering may lead to more impartial, thus possibly fairer, processes than those controlled by humans, the process of algorithmic recommendation has been criticized for the tendency to amplify and/or reproduce biases, distort facts, generate information asymmetry, and reinforce process opacity (Ananny & Crawford, 2018). Simply put, algorithmic biases may further compound the algorithmic injustice that machine learning automates and perpetuates. AI-powered platforms have markedly contributed to the rapid diffusion of fake news, mis(dis)information, and deepfakes, which are the detrimental byproducts of platformization (Dan et al., 2021). Misinformation spreads more rapidly and broadly than reliable information does, jeopardizing the credibility of algorithmic journalism. Issues regarding how to safeguard the goals, values, and automated processes of platformization, how to counter fake news, how to discern misinformation, and how to regain media trust in a world of AI remain controversial (Shin, 2023). At the root of these questions are concerns about how to mitigate biases and discriminations in data, JOURNAL OF BROADCASTING & ELECTRONIC MEDIA 2023, VOL. 67, NO. 3, 241–245 https://doi.org/10.1080/08838151.2023.2225665