Artificial Intelligence a diligent aid to Journalism

With the ever-increasing integration of Cortana, Siri, and OK GOOGLE in our lives, we are getting comfortable with the inclusion of artificial intelligence (AI) as our aid. Artificial intelligence is the capacity of computers/machines to perform cognitive functions like learning, reasoning, perceiving, problem-solving etc. The basic logic behind these machines’ working is acquiring knowledge by finding out patterns in data provided to these algorithms as input. These patterns blend to result in knowledge that capacitates the machines to have an intellect. The more the quantity of input data, the more accurate the artificial intelligence machines’ output. As Yuval Noah Harari writes in his renowned book Sapiens, the cognitive revolution is the prime reason why sapiens thrived as superiors from other socializing animals. The cognitive revolution was the sole reason for the formation of humongous thriving human communities. This patent function of human beings was and is being bestowed upon these machines to make them our companions in all walks of our lives, just like Jarvis and Iron man (Tony Stark) of the famous comics, The Marvel Universe. In the 21st century, artificial intelligence has found its way into functions like speech recognition, image recognition, real-time recommendations, stock trading, household robots, journalism and whatnot!

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How is artificial intelligence used in journalism?


The fourth estate of democracy, the watchdog of society, the field of journalism is also taking advantage of these humane machines to increase efficiency. Many news organizations worldwide are integrating advanced AI systems or robots into their workplaces One of the leading media publishing houses, Bloomberg News, combines journalism and AI seamlessly. They use automated technology to publish one-third of their news content. The Washington Post employs an AI bot called Heliograph to report, write, and publish news.. It helped the website to report on the Rio Olympics 2016 successfully. Condé Nast, a global media publication, uses an artificial intelligence machine called Spire, which helps them identify their customer purchase pattern and enhance the readers’ experience. Media houses like Quint, BBC, and Vox also depend on AI services for news aggregation content extraction. BBC uses an API called juicer to tag their stories in a way favoured by journalists so that they can search the stories effortlessly. The New York Times has also launched a tool named Editor to tag articles for reporters’ use. The Xinhua news agency in China has added a robotic team member name JiaJia to their team of news anchors. In China, media agencies use AI for content creation. Their advanced AI software reports and writes stories related to science and technology.


The AI machines get their intellect from recognizing patterns from a training data set as inputs. These patterns accumulate in the knowledge that enables the machines to have cognitive capabilities. With the examples of leading media houses, it is irrefutable that there is a massive list of tasks in journalism for these AI Machines. Although, will AI take over content creation, remains to be seen.


AI for content recommendation


AI can be used for personalized content recommendations. The consumption pattern of the audience provided as the training data set to AI machines results in definite descriptions of audiences’ likes and dislikes and their usage pattern of media content. This simplifies recommending the right content to the right user at the right time.  


AI for data analysis and predictions


AI can be helpful in the analysis of past reports to make logic-based predictions about future events. As an incredible example of using AI for data analysis, it can be made to analyze past election reports, stories, editorials, and media reportage and predict the election results.


AI for fact-checking and fake news recognition, plagiarism


AI software is handy when it comes to plagiarism detection. The use of AI tools for data analysis is a familiar trend now. Advanced AI systems may help provide suggestions, grammatical and semantic errors, and suggest correct vocabulary for the right emotion. Due to the ability of AI machines to collate and process a massive amount of data, AI machines can also be used for fact-checking and detecting fake news. In the era of user-generated content, the trouble of fake news in written, audio, or audio-visual formats is increasing day by day, and AI systems may be helpful to stop their propagation.


AI for transcription and translation


AI system shares the role of transcription and transcribing the audio and A/V interviews. This laborious task becomes gruesome for reporters and can be delegated to their AI counterparts.


How can artificial intelligence be used for online journalism?


AI systems integrated into reporting systems provide news alerts by analyzing and filtering the Big Data created by news and media organizations worldwide. These news alerts are provided based on the audiences’ preferences, company policies, national/regional importance of news, and various news values.


Can AI summarize an article?


Nowadays, people need bite-sized news to keep them up to date without wasting time reading a lengthy news report or watching a special report on TV. AI summarizes all these long articles into bite-sized news that can be consumed within a few minutes. Once the AI gets trained with the format of the articles and its ‘intellect’ starts successfully recognizing the essential components of a news story, there is no looking back.


Experts have confirmed that though AI seems to be a boon to journalism with its ever-increasing use cases, there are definite challenges in implementing AI in Indian journalism.


A piece of ideal news is always objective and unbiased, but AI cannot be trusted with curating unbiased news for publications. And so, artificial intelligence news reports fail relentlessly in obeying basic journalistic ethics. Experts from Cardiff University and MIT have stated in a recent study that artificially intelligent robots have a possibility of developing biases and prejudices on their own.


The second most important challenge in implementing AI in India is the vast number of Indian Regional languages. In India, newspapers are published in all 22 scheduled languages, and many other languages are spoken throughout the country. In this case, the AI may face a shortage of training data sets. Not all regional language newspapers will have large copies of sophisticated papers that can be provided as training data. Adding to that, the variation in dialects is also another challenge.


Implementation of AI in any field of work requires huge funding. In India, the reality is that some news organizations are so localized, small-sized, and deprived of vital funds that their daily operation has become a herculean task. Moreover, the gap between big media houses and much smaller news agencies upholds the discrepancies in the availability of funds.


Can AI replace journalists?


In a country like India, where access to manual labour is the cheapest, the implementation of AI as a substitute may imply that some job opportunities are gone. Another challenge is maintaining the authenticity of news produced by AI. The authenticity depends on the quality of the training data set provided to the AI machine. The better the data set, the greater the authenticity. For Indian professionals, formal documentation of processes, data etc., is still a newer method. Furthermore, the lack of ideal documentation standards is the biggest challenge in providing quality training data sets to AI.


Some of the other challenges include producing only a high quantity of news and not quality material, difficulties in understanding unstructured data, redefining copyright issues, and ensuring corporate accountability. This makes accomplishing AI’s participation in Indian journalism an arduous task.


Though the path of using AI to aid journalism in India seems blurred through all these challenges, the fact that the path exists is a ray of hope for the journalists. Overcoming the challenges mentioned above, like better distribution of funds, removing inconsistency in documentation, and focussing on variation, can help Indian journalism and AI grow in leaps and bounds.

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Written by: Dr. Sukhada Khandge

Last updated: 12-03-2024

Dr. Sukhada Khandge - PhD Inter-Disciplinary Folk Media | University of Mumbai. She is a passionate journalist and a researcher. While practicing journalism since 2008, she has worked with various national dailies likes Pune Mirror, DNA, Free Press Journalism, Sakaal Times.

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