Essay on AI Transforming Newsrooms: Automation & Streamlining of Journalism

Technological advancements have led to changes in various sectors, and newsroom has not been left behind. Technology is fast changing how newsrooms are conducting their daily businesses. In particular, Artificial Intelligence (AI), spearheaded by the technological giants such as Durham, Google, and Apple Inc., among others, is giving newsrooms new rooms automation strengths (Ali & Hassoun, 2019). Applications of the AI technologies in the field of journalism include streamlining of workflows, mining, and analyzing more data, and applying the data to generate more output in terms of news articles and voice broadcasts. Although the application of AI technology has been said to have the ability of reducing the operating costs of the newsrooms, automating more tasks hence boosting the performance and accuracy in news coverage, and promoting more diverse coverage without the effect of human feelings, some fears have been created by it among the stakeholders in the sector. The fact that many human journalists will lose jobs, the complexity, and costs of developing the AI news algorithms, ethical considerations, and the susceptibility of the systems to external interference through hacking presents a challenge. Both ways, AI is here to stay, and the major assistance we can accord ourselves is a deep understanding of how the framework functions in the field of journalism. Therefore, the paper aims at shedding more light on AI and augmented journalism, the relevant technologies that promote efficiency in journalism, and the impacts of the identified technologies on the news.

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To begin with, a deeper understanding of how the technology works and what it can do in the field of journalism not only helps in appreciating its need but also avoiding the workplace disruptions that might come with its applications. Augmented journalism refers greatly to the technologies that promote automation of some tasks hence enhancing the speed and accuracy of newsroom production. The technology enables the mining and organizing of large chunks of data within short periods and applying the same to come up with the news summaries, articles, or even voice broadcasts in the natural language (Ali & Hassoun, 2019). However, amid the evident good returns and efficiency from investments on augmented journalism technologies, some society leaders are still hesitant to clear unchecked application of the technology in the news field, as is in line with the Socrates school of thought. They raise three general concerns that they warn might disrupt the norm of the field (Whittaker, 2019). Firstly, there are several risks associated with the unchecked algorithmic news generation. Concerning this, the overreliance of the AI systems on numbers without the capability to discern the meaning of such numbers in real-life situations has come up several. The experts warn that if the news centers will not recruit competent human supervisors to keep a full-time check on the suitability of the decisions arrived at by the AI algorithms, and then chances of damage will be very high. Secondly, just like any other technologies, fears of constant disruptions in the workflows are eminent. Such a situation can result from the failure of the sensors and components of the system or just the reliance of the staff on the traditional system amid the advancements (Broussard, 2015). Lastly, there is a fear of the looming gap in the skillset of the human person in implementing augmented journalism.

Several AI technologies are relevant to the field of journalism. The most popular one is Machine Learning (ML) that allows the systems to be intelligent and self-adjust to fit into the prevailing situation. It allows for the supervised and unsupervised learning of the machine by the use of data sets to predict trends. Secondly, there is the concept of Natural Language that is applied by the algorithms to output the news from given data set inputs. The augmented systems perform tasks like generation and processing of the natural language. While generation involves turning the structured, template data into digestible written narrative, processing involves understanding and contextualization of the generated text (French & Poole, 2020). Thirdly, there are speech-related technologies. The speech to text technologies usually converts the pre-written text into audio while the exact opposite technology known as speech to text technologies, which convert audio into text. Next, there are vision technologies that enable real-time recording of what the human eye can see. Such technology has revolutionized investigative news reporting. Finally, numerous automated or robotics hardware such as the remote cameras enable the journalists to take photos and gather a large amount of data for their reports within short periods (Galily, 2018). Additional robotics hardware includes the Raspberry Pis, earth TV, and drones, among many others.

The embracement of augmented journalism technologies has elicited numerous concerns among different groups of persons regarding the posterity of the systems. The PESTEL analysis of augmented journalism reveals several technological, social, economic, and legal implications to the organizations that participate in the news business (Liu, 2017). Firstly, embracing the various aspects of augmented journalism allows the news firms to be competitive as far as technological advancement is concerned. Therefore, they can produce accurate news with fewer labor demands. Secondly, owing to the cutting of redundant costs associated with the implementation of the AI technologies, the new businesses are likely to enjoy good economic conditions when they can disseminate information via many channels within short periods and with minimal labor demands. Besides, the social business environment is positively impacted. The AI algorithms allow the news centers to analyze the demands of the communities as far as news consumption is concerned. With the wider outreach, the society remains informed hence the news businesses will have contributed positively towards the right to free circulation of information (Newman, 2018). Lastly, augmented journalism companies have to cope with the various changes in their legal environment. Essentially, the AI news systems are prone to many breakdowns and even legal breaches. The quality of the news released to the public might be low due to several reasons, such as wrong or inaccurate data input to the machine (Pavlik, 2016). It, therefore, becomes clear that if no standardized quality measures are laid down to be performed by competent human personnel, then the news agencies will be frequently at loggerheads with the law enforcement agencies due to the fake news circulation. Most importantly, companies seeking to apply the augmented journalism principles should evaluate their operating environment to ensure compliance and reaping of ultimate benefits.

In summary, the application of AI-related technologies in the news provides a great turning point in the sector. However, it should be noted that the AI could not address all the problems in the journalism field. It solves a variety of problems such as speed and the required labor to generate and process the news amid shortcomings. As a result, journalists should obtain appropriate skills on AI and the associated technologies to be able to enjoy the benefits. Further, the ethical considerations inherent in the application of AI in journalism should be adhered to with the aim of avoiding being on the wrong side of the law.

References

Ali, W., & Hassoun, M. (2019). Artificial Intelligence and Automated Journalism: Contemporary Challenges and New Opportunities. Int J Media Journal Mass Commun, 5(1), 40-49. Retrieved from https://pdfs.semanticscholar.org/880a/5eef74b89e5beadc0eb106643864820cd659.pdf

Broussard, M. (2015). Artificial Intelligence for Investigative Reporting: Using an Expert System to enhance Journalists' ability to discover Original Public Affairs Stories. Digital Journalism, 3(6), 814-831. Retrieved from https://www.academia.edu/download/42575920/Artificial-Intelligence-For-Investigative-Reporting.pdf

French, L., & Poole, M. (2020). New competencies for Media and Communication in an AI era. Humanistic Futures of Learning: Perspectives from UNESCO Chairs and UNITWIN Networks, 136. Retrieved from http://www.ipvc.pt/sites/default/files/HumanisticfuturesUNESCO2020.pdf#page=133

Galily, Y. (2018). Artificial Intelligence and Sports Journalism: Is it a sweeping change? Technology in Society, 54, 47-51. Retrieved from https://www.researchgate.net/profile/Yair_Galily/publication/323826816_Artificial_intelligence_and_sports_journalism_Is_it_a_sweeping_change/links/5aafc9df458515ecebea0948/Artificial-intelligence-and-sports-journalism-Is-it-a-sweeping-change.pdf

Liu, F. (2017). The Innovation of Communication Planning Idea--Based on the Background of the Application of Artificial Intelligence in Media. International Journal of Intelligent Information Systems, 6(6), 85. Retrieved from https://pdfs.semanticscholar.org/5b5c/49cd8bbb7ba1da4664862cc7040f94b5acda.pdf

Newman, N. (2018). Journalism, Media, and Technology Trends, and Predictions 2018. Retrieved from https://ora.ox.ac.uk/objects/uuid:45381ce5-19d7-4d1c-ba5e-3f2d0e923b32/download_file?safe_filename=Newman%2BPredictions%2B2018%2BFINAL.pdf&file_format=application%2Fpdf&type_of_work=Report

Pavlik, J. (2016). Cognitive computing and Journalism: Implications of Algorithms, Artificial Intelligence, and Data for the News Media and Society. Brazilian Journal of Technology, Communication, and Cognitive Science, 4(2), 1-14. Retrieved from http://www.revista.tecccog.net/index.php/revista_tecccog/article/download/75/83

Whittaker, J., & Whittaker, J. P. (2019). Tech Giants, Artificial Intelligence, and the Future of Journalism. Routledge. Retrieved from https://www.oapen.org/download?type=document&docid=1004204