Unit 2 Reflection

 For this piece, my intended audience consisted of media scholars, journalists, and individuals interested in the intersection of technology and public discourse. Specifically, I would be targeting college students, in particular those who study at Newhouse or at Maxwell, as their respective fields of communications and policy studies may benefit from this research. Given the complexity of algorithmic influence within journalism, I tried to balance accessibility with depth. I made deliberate rhetorical choices to ensure that the writing remained engaging, yet intellectually rigorous. For example, in the opening paragraph, I emphasized the significance of algorithms in shaping public narratives by saying that ‘as gatekeepers of information, algorithms can help curate personalized news feeds—but this often leads to the creation of echo chambers that reinforce existing beliefs and limit exposure to diverse viewpoints’. This framing established a critical (but measured tone), which I aimed to appeal to readers who might already be aware of algorithmic influence, but want to seek a deeper understanding of its implications. Additionally, by integrating specific case studies, such as the Los Angeles Times’ ‘Insights’ system, I was able to illustrate some of the real-world consequences of algorithmic decision making. This strategy helped enhance my credibility as a writer, while also making an abstract topic more tangible and digestible for readers.

When doing the research aspects of this piece, I was posed with several challenges, particularly in navigating the rapidly evolving discourse between AI and journalism. The conversation around these topics are dynamic, with new developments emerging frequently. I aimed to seek out a variety of different sources, from educational YouTube videos and  numerous social science journals, to reports by leading news platforms. However, I very quickly realized that, while there is significant attention on the ethical concerns of AI in journalism, a lot of the current discourse does not provide any solutions—and if they do, these remain underdeveloped and largely theoretical. In assessing the quality of research available, I found that, while there are extensive discussions on bias, misinformation, and economic implications, there is less focus on long-term structural changes needed within journalism. Academic studies (such as those created by Reuters) provide robust data on algorithmic influence, but much of the industry discourse remains anecdotal. Journalists express concerns, but lack empirical backing. 

In order to try to resolve this issue, I collected primary data from my target audience via a short questionnaire asking about their experience with algorithm-based journalism on social media and beyond. Overall, many seemed to agree that, in order to improve the current landscape, measures must be taken to eliminate filter bubbles and provide readers with more control and agency over how this news is actually dispersed. I conducted this study as an extension/follow-up of my Pitch HW, as during class, I noticed that I received mixed engagement. While I did receive responses expressing interest in the topic, there was also skepticism regarding whether solutions to algorithmic biases are truly feasible. A key takeaway from the pitch experience was that framing is crucial. Initially, my focus was on how algorithms distort journalism, but shifting towards potential solutions (such as public service algorithms or hybrid human/AI editorial oversight) made the pitch more compelling.

The writing and research methods I employed over the course of this project can definitely be applied to other disciplines, particularly in my own field of architecture. Just as algorithms shape news consumption, they also influence urban development. Through predictive policing, smart city planning, and algorithm-driven resource allocation, its uses are boundless. Therefore, understanding them in journalism provides me with a framework for examining similar events in spatial design. For example, within my architecture studies, I could apply similar forms of critical analysis to explore how algorithmic urbanism impacts marginalized communities. Each of the methods I used in this research project, from case study analyses to critical evaluation of existing discourse, could inform a future project I may explore on digital urban infrastructures. This project allowed me to gain a deeper awareness of the AI’s implications beyond media, reinforcing the importance of transparency and ethical design.