AI is changing the norms of journalism and data collection. Can it give new insights, make news unbiased and responsible remains to be seen
In the wake of the increasing use of artificial intelligence (AI) in journalism, data journalism and the nature of storytelling are taking shifts which have further invited deliberations on news values, the political economy of news media and media ethics. There is no doubt that AI tools are immensely contributing to the news industry. Subsequently, there are paradigm shifts in the realm of journalism, as a form of art and profession. However, excessive and unstructured use of technologies in newsrooms has plummeted to various ailments in the media industries. Inculcated forays of AI into newsrooms have accumulated pointless pitfalls.
Due to the bottlenecks that such complicated and opaque systems pose for undermining accountability, decision-making, and professional judgment, this intelligibility issue is especially acute for public service media. Unravelling the dichotomy between the Global North and Global South has further worsened the feasibility of using AI tools in data journalism. Inclusive journalism has become a distant dream. Moreover, there are other several unidentified factors plaguing the news industry, that are to be scientifically probed.
Tellingly, journalism is the process of gathering, analysing, producing, and presenting news and information. History demonstrates that society tends to have more news and information the more democratic it is. Journalism as a means of craft and business remains a dynamic field at the same time. With the pace of time and demand, data journalism is increasingly significant these days. And subsequently, this approach to storytelling is getting apparent in newsrooms across the globe.
The practice of reporting on facts while using structured data as the central element of the narrative and managing it impartially is known as data journalism. It covers an expanding range of storytelling tools, methods, and approaches. It can include everything from conventional computer-assisted reporting to the most cutting-edge news applications and data visualization. The overarching objective is a journalistic one: disseminating data and analysis to help us all learn more about pressing current affairs.
Digital tools are used to streamline data collection in data journalism, which is news-making that is fuelled by quicker data collection and visualization and first emerged in the US in the 1950s. Probability and computation play a large role in data journalism. Big data has the potential to produce news more quickly and with greater depth than ever before. Like the internet, big data is an infinite reservoir of ever-expanding data that belongs to no one in particular. Big data can be used for a wide range of intricate tasks with the right equipment and personnel, including improving public healthcare in smart cities and personality-based psychometric profiling.
Applications with AI that use big data from the media can search through millions of forums and social media posts. By analysing the tone of social media posts, forum posts, emails to government agencies, and other sources of information, sentiment-based big data in media can reveal the underlying problems in a particular area. In these posts, elements like the use of negative or positive sentences, word choice, length and readability of posts, and the characteristics of images or other media within the posts are evaluated to ascertain the mood of the public in a specific area. News networks can use technology to bring the issues of citizens to the attention of the ruling government once it has developed sufficiently and is more practical to use for journalism.
AI tools have become instrumental in mitigating the biases in mainstream journalism. Because of the polarization of news coverage, viewers enjoy picking sides and demonizing people who disagree with their way of thinking and opinions. Even though from a TRP standpoint this may be profitable for news channels, if it is not addressed in the beginning it can eventually cause a fractured society.
News outlets can publish and curate content using machine learning and AI to make data journalism impartial and unbiased. Machine learning involves finding distinctive and subtle patterns in vast amounts of data. This enables AI-based applications to distinguish between authentic data and fake data with great clarity. Data journalists can produce factually accurate articles and news reports once the non-factual data has been separated from it. News production becomes more moral when using this method to develop new campaigns.
The impact of AI extends beyond the newsrooms of a specific news network. Media companies can more effectively manage their financial operations thanks to technology, big data, and NLP. Through data-driven financial management and expense control, these technologies enable media businesses to make money by simplifying the analysis of financial documentation. In addition to identifying instances of fraud and errors in financial statements, AI-based accounting systems can also lower the number of false positives in fraud detection. False positives are decreased, which lowers the costs associated with financial fraud investigations that would otherwise be incurred. News networks are safe when it comes to complying with financial regulations thanks to a well-calibrated financial management framework guaranteed by AI and big data in media and journalism.
AI, big data analytics, and machine learning will support human journalists and remove human error from newsgathering, making journalism more integrity-driven. To be fair, the news media is regarded as the fourth pillar of any democracy. Data journalism must uphold this goal to strengthen society and preserve a nation's democracy. Big data's emergence in journalism-related media is expected to achieve this goal. The news media cannot escape bias because it is a global problem. But because AI's machine learning algorithms are trained to take accuracy into account, it helps to reduce the subjective interpretation of data by humans.
The use of AI tools in data journalism has developed certain flaws in newsrooms across the globe. Certain newsrooms are not equipped with AI tools and their orientation towards technological implications in Global South compared to Global North remains lethargic. The tendency of AI algorithms to reproduce existing bias is one of the main issues with their growing use. At the moment, AI-written articles can only be found on straightforward, formulaic subjects like stock market data and sports scores.
Considering how quickly technology is developing, data journalism will play a significant role in the way we tell and visualize stories going forward. Additionally, audiences are becoming more engaged and will anticipate seeing more journalism and articles that focus on the power of the data. However, the future of AI in the domain of data journalism remains undecided. Some newsrooms already use artificial intelligence to mine data, develop algorithms, and produce content automatically. Journalists face new issues as a result of routinely using this technology. Some experts assert that we are in a transitional period and must decide how this technology will be used in the media going forward, particularly in data journalism. It's difficult to know exactly what all of the limitations may be because we are still in the early stage of the adoption of AI techniques in journalism.
(Biswal is an Associate Professor and Head of the Department of Journalism and Mass Communication, at Rama Devi Women's University, Bhubaneswar. Kulkarni is a Research Professor and Associate Director of the Institute of Artificial Intelligence, at MIT World Peace University, Pune)
