Trustworthy journalism through AI. Data
From Digital Culture & Society
Contents |
[edit] Trustworthy journalism through AI. Data & Knowledge Engineering
Opdahl, A. L., Tessem, B., Dang-Nguyen, D.-T., Motta, E., Setty, V., Throndsen, E., Tverberg, A., & Trattner, C. (2023). Trustworthy journalism through AI. Data & Knowledge Engineering, 146, 102182-.
DOI: https://doi.org/10.1016/j.datak.2023.102182
[edit] Context
The increasing prominence of social media and ‘quick’ content has put pressure on traditional news media as the previous methods of spreading news headlines has been sped up drastically. Since the internet and social media can transmit information at a lightning-fast rate, many people, and organizations race to be the first to report on news stories. The speed of which news is reported has no bearing on the validity of the content that is shared. Social media news also has the added effect of being able to view people’s insights and feelings towards the news with their responses to posts; especially in the comment sections. Social media news accounts are also encouraged to be the first to tell a story as they will often be rewarded with financial benefits due the many platforms’ advertising and subscription strategies. Since traditional news websites focus on providing complete, accurate news stories, their creation time is severely increased due to the extra work that is involved. Traditional news mediums are also facing financial troubles with their decreasing ability to sell advertisements, subscriptions and sales due to the amount of free news sources that are available to the public. Due to the nature and operation of social media sites, content (including advertisements) is often recommended to users based on their collected user data and demographics which can lead to issues if ‘fake news’ or disinformation is being spread and recommended to a large number of users. As News media attempts to catch up to social media news, the speed of which content is spread is one of the main concerns. AI has becoming a new tool that journalists have been using in process of writing articles, however, AI is still not trusted completely by the public. AI can be used at many stages in the production of the news production cycle and this article goes over the ways journalists can use AI to increase trust and increase the speed of every stage in production.
[edit] Overview
This article explores the changes in the news industry over the past two decades since the introduction and growth in popularity of social media news. The authors touch upon the broken business models of traditional news and social media that creates a vicious cycle that damages the news industry. The article mentioned that from 2008-2020 over 25% of newsroom employees were lost. They go on further to talk about the many areas AI could prove useful in the production of a news article. For each stage of production, they go in depth by providing many areas where help or efficiency is need, what AI tools could be used, what these benefits these tools could provide, as well as how these tools could also misused negatively. They portrayed problem areas where AI could offer solutions as well as providing rationales for the opportunities and risks using AI could have. They repeated this process for each broad area in which AI could support journalists such as with resource gathering, assessing the validity of content, assisting in the writing of articles, and adjusting the presentation of news (automatically censoring faces, explain level of trust, monitor article engagement, etc.).
[edit] Strengths and Weaknesses
[edit] Strengths
This article is strong in the way that it provides and unbiased view and contains arguments for and against the use of AI in the news production cycle. It gives the reader a level of increased trust in AI and reduces potential fears once people better understand how it works as most of the fears stem from ignorance of how AI works and what it is capable of. This article also touched on many demographics other than simply North America as they explored other news markets such as with their observations about Norway’s level of trust in the news. This article is also relatively current as it was published in 2023.
[edit] Weaknesses
The articles lists many different problem areas in their many provided tables, however, they do not go in depth in the problem areas and explain their rationale for why they are problems. The points in their tables are also pretty brief without clear sections of where, if any, extra context for the points are found. Even though this article was posted in 2023, there is little speculation about the future and any new problems that could be created with AI implementation in the news cycle.
[edit] Assessment
To conclude, the article painted a very diverse picture of the news industry and the importance of trust and how AI could impact the level of trust for news media. This article was successful in providing ways journalists could use AI tools to aid in the production of news articles, as well as how to use AI in ways that can instill trust for the writers and the media consumers. They mentioned how traditional methods of quality journalism may be impacted negatively if they do not prioritize instilling trust in the public and avoiding misusing the AI tools that are available. All together, this article was enjoyable to read and was insightful of the positive ways AI could be used in news as well as offering the devil’s advocate approach of also providing examples of how AI could be misused. It was appreciated that the article went in depth on both the positive and negative side of AI tools to provide an unbiased view of the impact of AI in news.
Rm18xz 23:27, 1 December 2023 (EST)
Please consider the environment before
printing this page.