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University University Of Canterbury (UC)
Subject Journalism

TITLE

How artificial intelligence is impacting journalism

Executive Summary

The increasing presence of artificial intelligence and automated technology is changing journalism. While the term artificial intelligence dates back to the 1950s, and has since acquired several meanings, there is a general consensus around the nature of AI as the theory and development of computer systems able to perform tasks normally requiring human intelligence.

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Findings

AI tools can help journalists tell new kinds of stories that were previously too resource impractical or technically out of reach. While AI may transform the journalism profession, it will enhance, rather than replace, journalists’ work. In fact, for AI to be used properly, it is essential that humans stay in the loop.

Recommendations

Investment in training editors and reporters is crucial. As AI tools enter newsrooms, journalists need to understand how to use new resources for storytelling—not only ethically, but also efficiently.

Introduction

By Mark Hansen, director of Columbia’s Brown Institute for Media Innovation

Our conversation at June’s forum began where these discussions often do: with the idea that we can enhance human ability through computation. Our specific focus was on journalism and tasks associated with reporting, writing, and designing impactful visualizations and other journalistic “experiences.

Note on formatting: This policy exchange forum, the first of four, was closed to the public and followed the Chatham House Rule. It lasted three hours and was structured around three key areas: the newsroom, technology, and ethics. An eight-minute, lightning talk by an expert in the field kicked off the discussion, followed by a forty-five-minute debate.

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Discussion I: Al in the Newsroom

Drawn from presentation by Chase Davis (editor of interactive news at The New York Times)

Having framed computation as a way to enhance or extend (or, later, even automate) select processes of journalism, an obvious next question is how should we bring these tools into the newsroom responsibly? First, what are they good for?

Case Studies: ‘A Spectrum of Autonomy’

The incorporation of AI into the newsroom has led to a significant breakthrough in the abilities of reporters to act as amateur data scientists. AI can augment the human reporter in several ways: helping to classify and categorize documents, identify outliers in data worthy of closer examination, or find needles in the haystacks of data.

Data

The increasing availability of data, with everything from social media to government data, enables previously impossible reporting—but it still presents pitfalls. Journalists must be careful to assess the credibility of this new type of source, especially where AI is involved.

Challenges for Publishers: Large Newsrooms and Small

With all these new tools comes an obligation to train editors, reporters, and newsroom developers in how to use them responsibly. This effort, not to mention the AI itself, can be costly. While investment may not be a problem at a large news organization like The New York Times, for smaller newsrooms with fewer resources this will be a challenge.

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Discussion II: Technology

Drawn from presentation by Larry Birnbaum (professor at Northwestern University)

How does technology fit in the news pipeline? As mentioned earlier, AI increasingly assists in reporting, content creation, distribution, and audience interaction, to name a few examples. Recently, crowdsourcing, brainstorming, and fact-checking tools are being developed to aid data information gathering and, particularly, to structure relevant data. Among contemporary newsrooms, automation is a key tool in competing not just against each other for customer attention, but also against large platforms such as Netflix, Facebook, and Amazon.

Automation and Personalization of Stories

Larry Birnbaum’s lightning talk detailed how AI is making possible large strides in the potential for personalization of news. It may even eventually allow for different themes in article writing—for instance, a hero theme for a particular sportsperson (focused on words like “strong,” “victory,” and “heroic effort”).

Commenting Systems and Audience Engagement

A recent move by The New York Times seems to signal an important step toward automated process. The paper signed a partnership with Jigsaw, a technology incubator at Alphabet, and launched a new initiative to help filter comments. It currently takes fourteen moderators to handle around 12,000 comments a day. It is expected that moderators will be more efficient with this tool, which will allow the paper to publish more comments—on around eighty percent of their articles, as opposed to the current twenty percent. The moderator tool will automatically approve some comments and help moderators wade through others more quickly. In addition, this tool will identify toxic comments that can undermine a civil exchange of ideas.

Proprietary Versus Open Algorithms
“The dirty little secret of machine learning,” one participant quoted an industry colleague as saying, “is that the nearest neighbor, while not the best, is often in the top tranche of methods, and not too far off the leader.” That is, while the most advanced algorithms tend to be proprietary, the next best publicly available thing is never much worse. This is essentially the basis of common open source search and analytics tools like Apache Lucene and Elasticsearch.

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Challenges and Limitations

As with any complex system, errors happen, and with AI those errors can have serious consequences. This highlights the importance of keeping humans in the loop and rigorously checking the work of AI systems. As we point out in the forthcoming ethics section of this report, robots cannot be held accountable, or as one participant noted, external audits become indispensable:

Discussion III: Algorithms and Ethics
Drawn from presentation by Olga Pierce (deputy data editor at ProPublica) and Julia Angwin (investigative reporter at ProPublica)

Finally, using the latest innovations in AI tools in newsrooms—such as machine learning, natural language processing, face recognition, and machine vision—brings its own ethical considerations. The rapid introduction of bots in newsrooms and social media’s use of predictive analytics, to mention two examples, make the conversation around regulation, best practices, transparency, and disclosure more important than ever.

Transparency and Accountability

As AI can play many roles in journalism, care should be given to explain exactly when, how, and where it is used. Its implementation may not be clear to a reader or view, and journalists should not assume that it is. One example that arose in discussion involved the use of a chatbot to engage with readers: If powered by AI, how does the bot disclose that to the audience? Was a story actually authored by an algorithm?

Editorial Decisions and Bias

The role of algorithms in news curation is increasingly prevalent. Such algorithms, which represent editorial decisions, need to be written in human terms. As one participant put it, “We need journalists who can understand these models and understand these datasets, because selecting them is an editorial decision.” Take chatbots, for instance. Computers, just like people, cannot have conversations if they don’t understand their contents; the only areas a bot is able to talk about are ones in which we can build a model for that conversational context.

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Ethical Use of Data

AI tools allow journalists to process a high volume of data in a limited period of time. However, what can be an advantage can easily turn into a challenge. Smartphones have enabled a system of easy traceability, and this requires an ethical use of data that poses questions about sensitive matters like transparency, contextualization, sharing regulation, and trust. The ethical use of data is a fundamental question every journalist needs to confront.

Algorithmic Journalism—Current Applications and Future Perspectives

Abstract: Journalism, more so than other professions, is entangled with technology in a unique and profoundly impactful way. In this context, the technological developments of the past decades have fundamentally impacted the journalistic profession in more ways than one, opening up new possibilities and simultaneously creating a number of concerns for people working in the media industry.

Introduction

Journalism is a profession that has always been shaped by technology throughout history (Pavlik 2000). Despite its constant and very close relationship to technological advancements, however, the past decade has seen an especially large shift in the field, with many of the core elements of the journalistic profession being redefined (Druze and Witched 2018).

 Definition of Algorithmic Journalism

Algorithmic journalism is a term that attempts to describe the procedures that have been brought about by recent technological changes in the field of journalism. Some researchers such as Grief (2016) define algorithmic journalism as “the process of using software or algorithms to automatically generate news stories without human intervention”, not accounting for the original programming of the software of course.

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Areas of Application
Journalism has changed vastly over the past years and the responsibility for this change hinges mostly on the very significant impact modern technology has had in the news industry. What follows in an analysis and review of the main areas in which computational technology has brought the most notable changes in the field.

Artificial Intelligence and Automated Journalism: Contemporary Challenges and New Opportunities

Abstract: Artificial intelligence (AI) is today an integral part of the new media ecosystem. As such, this, study aims to (1) describe the status quo of technology and its role in renewing and modernizing journalism, (2) give insights about the impact of artificial intelligence in changing journalism practice, (3) identify potential implications of artificial intelligence on the future of journalists, and (4) to extrapolate ethical and professional challenges that may upset the practices of the journalism profession.

INTRODUCTION

Recently, the media landscape has undergone rapid and unprecedented transformations, due to the significant advancement of Information and communication technologies (ICTs), which drive innovation [1] and continues unabated on one hand [2], along with its role in renewing and modernizing journalism on the other. Indeed, traditional media companies around the world are confronted by many challenges stemming from the radical digital transformation of the publishing’s industry [3].

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1.1.Objectives

The goal of the present research to (1) describe the status quo of technology and its role in renewing and modernizing journalism, (2) give insights about the impact of artificial intelligence in changing journalism practice, (3) identify potential implications of artificial intelligence on the future of journalists, and (4) to extrapolate ethical and professional challenges that may upset the practices of the journalism profession.

1.2.Methodology

The increasing dependence on artificial intelligence technologies in journalism at an unprecedented form highlights the importance of studying this phenomenon in depth. In this respect, this research based on a systematic review of the literature, that differs from a narrative review. While a narrative review provides an overview of the content available on a given topic, a systematic review is more narrowly focused and seeks to assemble, critically appraise or evaluate and synthesize the results of primary studies in an integrative approach.

2. NEW TECHNOLOGIES AND JOURNALISM RENEWAL

Technology has become a real driving force for the media sector to produce new digital content in line with the demands of Internet users. Hence, we can say that the dramatic changes in the field of journalism have connected directly to advanced technology tools [5]. In this context; some authors adopt a positive and optimistic view of the leading role of technology in the field of media and journalism. Thus, the use of AI technologies has become an indispensable part of the field of media that has to lead to radical transformations in the field of journalism [6].

3. ARTIFICIAL INTELLIGENCE AND JOURNALISTIC PRACTICE

3.1.Quantitative Turn in Journalism

The quantitative forms have become more prevalent in contemporary journalism [11]. Nevertheless, these new forms of journalism despite its relative novelty have drawn significant attention in academic literature and recently become the focus of considerable interest in the media sector. Loosen (2018) highlight of new four forms of journalism (see Figure 1), which can be considered as transformation process journalism faces today not only at the level of the basic stages of news production and consumption but also affects journalism at its core [12]:

Data journalism: This concept has gradually appeared in newsrooms over the last decade, which refers to the process of extracting useful information from data, writing articles based on the information and embedding visualizations in the articles that help readers to understand the significance of the story [13]. As Linden (2017) notes, “digital revolution has expanded the supply and availability of data that can be used for computational journalistic processes, along with the expectation of events to a larger extent than before” (p. 24) [14]. However, data journalism represents the convergence of a number of fields which are significant in their own right – from investigative research and statistics to design and programming [15].

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Algorithm Journalism: This type of journalism defined as “the innovative processing that occurs at the intersection between journalism and data technology” [16]. Besides, it can be “the combination of algorithms, data, and knowledge from the social sciences to supplement the accountability function of journalism” [17].

 Automated Journalism: The focus with this term is to emphasize the increasing amount of content that is being produced automatically and by means of technologies being developed by providers of automated content solutions [18]. In other words, “algorithmic processes that convert data into narrative news texts with limited to no human intervention beyond the initial
programming” [19].

 Metrics-Driven Journalism: Refers to the varied attempts to make sense of an ever-growing amount of audiences’ digital traces with the potential to influence decision making processes at all stages of the news production process [20].

3.2.Automation and Newsrooms

There is no doubt that AI is gradually spreading through multi-creative spheres including journalism– that already has been impacted, especially in light of persistent economic disruption and the digital transformation [21]. In this respect, we can say that the evolution of artificial intelligence techniques radically reshaped newsrooms [22], specifically into all aspects of news production and dissemination as seen in the figure below [23]. Loosen (2018) pointed out that the technology facilitated by advances in the field of automated content production affects journalism at its very core: the production of news [24].

4. FUTURE OF JOURNALISTS: PRACTICAL CONCERNS

Recently, the journalistic landscape has undergone rapid and unprecedented transformations, due to artificial intelligence innovation. Unsurprisingly, then, this development raises questions about potential implications about the future of journalists, particularly with automated content production. As Carlson (2015) notes, “automated journalism harkens to the recurring technological drama between automation and labor present since the earliest days of industrialization” [28]. However, there is a diverse range of opinion on the issue that can be summarized in two main trends:

5. CHALLENGES OF AI IN JOURNALISM

Despite media companies undergo dramatic changes due to the introduction of automatic AI processes into all aspects of news production and dissemination; understanding and addressing the professional and ethical issue is still at a very early stage. However, these issues are critical and need to be discussed.

6. CONCLUSION

In general, journalism worldwide is undergoing a historic transition, thanks to rapid advancements in digital technology. Importantly, this development is another aspect of technological progress that had lead to major transformations in organizational structures and functions of media companies. In this respect, artificial intelligence algorithms considered the most important revolution of journalism in the digital age, which has been reorganized the newsroom as never before. On the other hand, these technologies offer much great potential for enhancing journalism today especially allow journalists to process a high volume of data in a limited period of time, create news stories from structured data and automatically deliver them, as well as more diverse coverage.

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