Difference between revisions of "Artificial Intelligence in Journalism"

Revision as of 20:55, 27 January 2023

Artificial-intelligence journalism, or AI journalism, is the use of artificial intelligence software and other algorithmic processes to assist in or fully automate aspects of the journalistic process. The AI algorithms used in this are generally categorized through a few broad categories: data collection, content generation, information verification, and news dissemination.^[1] The programs that fall into these general categories each play important roles in the story production process, and the combination of their individual output is what amounts to articles that require limited human interaction to generate. The behavior and capabilities of any automated software depends on the developers approach to creating the program, and this ultimately dictates the quality of the article that AI journalism is capable of creating. While there are no guidelines for the way in which these software function, typically, they follow a similar process. It involves an algorithm that is capable of scanning large amounts of provided data, identifying and piecing together key points gathered from the data, and combining this information to generate a human-readable piece. Aspects of writing like tone, expressiveness, and style are customizable, and largely dependent on the data which the AI algorithm was trained on.

The idea of automated news has existed for nearly a half century, with its earliest implementations beginning with numerical and statistical reports for topics like weather, and financial news.^[2] The automation of more advanced types of journalism and reporting is a relatively newer field, and very much still in its infancy. However, the rapid growth of AI technologies over the last decade due to increased computing power, reduced costs, and greater investment has enabled bolder and more powerful algorithms to enter the space, allowing more complex forms of writing to be emulated by software.^[3]

The role AI will play in journalism's future is hard to tell.^[4] AI journalism holds the potential to alleviate menial burdens from humans, increasing efficiency, reducing costs, and giving news agencies more time to delve into increasingly complex and pressing topics. At the same time, this technology raises concerns over content quality, and has the ability to threaten the role of humans journalists in the field.^[5] The influence of media is extremely large and impactful in our societies, communities, and lives, making the ethical concerns of implementing automation and technology a heavily covered topic.

Applications

Cost Reduction

AI journalism would drastically decrease the cost of content production. Over the last two decades, the news industry has been somewhat stagnant and showing signs of decline in profitability and growth. ^[6] With decreasing budgets, it's becoming harder for local news agencies to stay afloat, which hurts communities.^[7] The advent of AI in journalism addresses this issue, as automation leads to drastically lower costs. Cutting costs can help news agencies handle greater pressure by eliminating many human costs, while still adhering to quality.

Speed

The use of powerful algorithms in journalism would enable news to be spread much faster. The moment the software receives data, it can begin to formulate an article surrounding the topic, capturing the important information that is relevant to readers. This eliminates any barriers or time sinks associated with human production of content. This means important information can be generated and outputted within minutes after an event occurs. Increasing the speed and flow of information benefits everyone's ability to understand and remain updated about various situations.

Furthermore, automating many manual and menial processes enables journalists dedicate more time to producing quality content. Software is able to analyze and sort through large quantities of information at rates exponentially faster than their human counterparts.^[8] Removing the need of human intervention for tasks like calculations and other low level analyses, allows journalists to dedicate time to the more complex aspects of journalism. With the assistance of software, human journalists would be able to develop a much more in depth analysis of their data.^[9] Pairing journalistic skills with the quality of data extrapolated by computers, would enable journalists to create much more meaningful conclusions and insights from their work.

Ethical Concerns

Algorithmic Bias

One concern with AI journalism is the credibility of the content produced.^[10] AI driven systems are often subject to biases, and they often come from their creators.^[11] Unwittingly, biases can be baked into algorithms and this can implicitly impact the content generated by AI. The outputs of an AI system are heavily dependent on the data on which the algorithm was trained on. If low quality biased data goes in, the resulting article will end up low quality and biased as well. ^[12] Companies using poorly developed algorithms that frequently output biased articles can have severe negative effects. Media bias is a powerful force, as when spread widely enough, it can lead to inequitable outcomes for societies most vulnerable groups. ^[13] The efficiency of algorithmic generation provides the perfect recipe for bias to spread rapidly and far. As a result, it's important for algorithms used for media generation to be trained on the right data, to prevent the concern of bias emerging from news.

Authorship

Future of Employment

References

1. ↑ https://www.mdpi.com/2673-5172/2/2/14
2. ↑ https://towcenter.gitbooks.io/guide-to-automated-journalism/content/status_quo_of/the_state_of.html
3. ↑ https://www.wired.com/insights/2015/03/ai-resurgence-now/
4. ↑ https://www.washingtonpost.com/business/energy/why-the-future-of-technology-is-so-hard-to-predict/2022/12/28/57fd3ac2-86b0-11ed-b5ac-411280b122ef_story.html
5. ↑ https://www.tandfonline.com/doi/abs/10.1080/21670811.2016.1209083?journalCode=rdij20
6. ↑ https://www.pewresearch.org/fact-tank/2022/10/13/after-increasing-in-2020-layoffs-at-large-u-s-newspapers-and-digital-news-sites-declined-in-2021/
7. ↑ https://www.cbsnews.com/news/local-news-financial-firms-60-minutes-2022-06-12/
8. ↑ https://www.tandfonline.com/doi/abs/10.1080/10714421.2015.1031996?journalCode=gcrv20
9. ↑ https://thenextweb.com/news/face-it-ai-is-better-at-data-analysis-than-humans
10. ↑ https://www.sciencedirect.com/science/article/abs/pii/S0747563222002679
11. ↑ https://hbr.org/2020/11/a-simple-tactic-that-could-help-reduce-bias-in-ai#:~:text=credit%2C%20and%20more.-,It's%20been%20well%2Destablished%20that%20AI%2Ddriven%20systems%20are%20subject,by%20experts%20with%20implicit%20biases.
12. ↑ https://www.prolific.co/blog/data-quality-and-ai-safety#:~:text=Biased%20data%20creates%20biased%20AI&text=Bias%20in%20data%20collection%20for,applies%20to%20bias%20as%20well.
13. ↑ https://unitedwaysem.org/equity_challenge/day-4-understanding-our-bias-the-consequences-of-bias/#:~:text=Bias%20can%20be%20dangerous%20and,influence%20actions%20that%20are%20discriminatory.