The image of using AI methods to discover hidden patterns in large volumes of data has many aspirations, thereby reducing the costs of investigative journalism investigations. But so far only a few research stories have used AI methods in relatively limited ways.
This article examines how much success has been achieved in investigative reporting using AI techniques, why more advanced methods have been difficult to implement, and how investigative journalism can be expected to address many of the issues in the future. Gives an overview of things.
Journalistic problems are usually story specific, which means that training data is not readily available and the costs of complex models can be spread over multiple projects.
Much of the data relevant to a story is not publicly accessible but is held by governments and private institutions, which usually must be collected, negotiated or purchased.
Journalistic estimates are made with extreme certainty, or require extensive manual verification, to avoid the risk of defamation. The factors that make some facts “noticeable” are others sociopolitical and thus difficult to code atomically.
The biggest opportunities for AI in investigative journalism are in data preparation tasks, such as data extraction from disparate documents and possible cross-database record linkage.
Artificial Intelligence (AI) is a branch of computer science that aims to create intelligent machines that are capable of acting like human intelligence in general. AI systems can learn from experience and improve their performance over time without explicit programming.
In recent years, AI has gained prominence thanks to advances in machine learning, natural language processing, computer vision, and robotics. These technologies are used in various industries, such as journalism, health, finance, transportation, and entertainment.
Machine learning plays an important role in AI, in which the use of algorithms allows machines to learn from data without explicit programming. Physical language processing gives machines the ability to understand and interpret human language, while computer vision gives machines the ability to interpret and analyze image information.
AI has the potential to automate everyday tasks and processes, improve decision-making, and improve user experience. Nevertheless, employment, privacy, and ethical concerns remain over the perception of AI. As AI continues to develop and spread, individuals and organizations must understand the technology and its potential impacts.
Various research studies have explored the application of Artificial Intelligence (AI) in journalism, yielding some key findings:
- Automated news writing: AI systems have been developed that can create news stories from datasets and templates. These systems raise questions about the informational and ethical implications of news.
- Fact-checking: AI can be used to fact-check news and identify fake news. Research shows that AI can reach high accuracy rates, diagnosing fake news with up to 90% accuracy.
- Personalized news: AI can be used to evolve news based on the preferred interests and preferences of personalized users, which increases customer engagement and loyalty.
- Content Recommendation: AI-powered content recommendations, based on user reading history and behavior, can improve user experience and increase the amount of time users spend on news websites or apps.
- Audience Analysis: AI enables the ability to analyze user behavior and preferences, which helps journalistic organizations target their content effectively and nurture their marketing strategies.
The use of AI in journalism brings both benefits and challenges. While this can increase efficiency and improve the user experience, there are ethical concerns about the quality and accuracy of automated reporting, potential concerns about biased content, and the impact on human employment. Research indicates that AI can automate a large portion of a reporter’s job (9%) and a large portion of a reporter’s job (15%).
Impact of AI in Journalism
Automated writing through AI programs
Automated writing by artificial intelligence (AI) programs is a growing trend in journalism. These programs leverage natural language processing (NLP) algorithms and machine learning techniques to generate news articles, reports, and other types of content.
One of the main advantages of using AI for writing is that it works very quickly. Automated writing programs can generate articles within seconds or minutes, which are a valuable asset for journalistic organizations that need to publish content quickly . In addition, AI also assists in the creation of large volumes of content that are sometimes overwhelming for human authors, which is often difficult for human authors.
One of the main advantages of automatic writing is its accuracy. AI algorithms are capable of analyzing large volumes of data sets and uncovering recommendations that may be difficult for human authors to understand. AI systems can also help with fact-checking, ensuring that articles are free of errors.
Identify and Reduce Biases
Following are some of the most important measures to detect and reduce bias in artificial intelligence (AI) systems to predict fair and equally extreme outcomes:
- Biases of Data Selection: Biases can be rooted in biased data. Inaccurate, out-of-sample or under-sampled databases can produce AI results. To minimize this, it is important to collect diverse and representative data sets, ensuring that they are properly balanced and free from bias.
- Algorithmic biases: Biases can also arise through the design of algorithms. Biases algorithms can amplify stereotypes and discrimination. It is important to look at Bayes and recover algorithms to overcome such problems.
- Diversity and integration: It is important to increase diversity and integration during the development and management of AI systems. A diverse team can help identify and eliminate bias when it becomes entrenched in technology.
- Ethical Framework: Using ethical frameworks and guidelines is essential to reduce bias in AI. These frameworks should include considerations related to fairness, accountability, transparency, and ethical principles during design and management.
- Human-in-the-loop: Incorporating humans into the decision-making process with AI systems to help reduce bias can help reduce bias. AI should not only be used as decision-making tools but should be used with key decision-makers.
- Continuous monitoring: The AI system must constantly monitor for bass and other errors. AI allows systems to reduce bias over time by allowing continuous testing of content for approval toward fairness, accuracy, and transparency.
The desire to reduce bias in AI requires comprehensive measures that include data selection bias, algorithmic auditing, increasing diversity and integration, following ethical frameworks, involving humans in decision-making, and continuous monitoring. This multifaceted approach is important so that AI systems work fairly and without bias.
Transcribing Interviews through AI
The use of AI for interview writing is becoming increasingly common and has various advantages such as time saving and error reduction. However, it is also important to consider and pay special attention to the problems that arise:
- Accuracy: The accuracy of AI based writing can be very good but it is not infallible. Accuracy depends on factors such as voice quality, language, and tone etc. . Any errors or discrepancies should be checked carefully.
- Cost: AI-based writing services often cost less than employing human writers. However, the complexity and quality of writing depends on relevance. The cost of AI-based writing is significant to compete with human writing services.
- Privacy: Protecting user privacy is very important when using AI-based writing. Some AI services may store or share audio files or text, which may compromise privacy . It is important to choose writing services with strong data privacy policies to protect the interviewer’s privacy.
- Technical Knowledge: Using AI-based transcription may require technical expertise to properly upload audio files and verify correct transcription. Having a team member with the necessary technical skills to solve technical problems in any problem area is critical.
- Proof and Review: While AI can write, most require human proofreading and proofreading to ensure quality and accuracy. This extra step in the writing process requires spending time.
- Specialization: Some AI writing tools allow specialization to improve accuracy for specific industries or accents. Assess the need for specificity for your interviews.
- Audio Quality: The accuracy of AI writing depends largely on the quality of the audio recording. Good recording
Record audio properly to ensure quality, such as reducing background noise and using quality recording equipment.
Spotting Trends in Journalism
Artificial intelligence (AI) is rapidly changing the field of journalism, and its impact can be seen in many areas. Here are some of the latest trends emerging between AI and journalism:
- Automated Content Creation: With AI-assisted algorithms, journalists now have the ability to generate articles, summaries, and even video reports on the latest news. Automated content creation tools can analyze data sets, recognize patterns, and generate reports that sound like human journalists.
- Delivering Personalized Content: AI is helping journalists deliver personalized content to their readers. By analyzing user data, AI can suggest content to planners that are tailored to a reader’s interests and preferences.
- Fact Checking and Verification: AI is helping journalists combat fake news and hoaxes, by providing tools to verify the authenticity of news and images. AI-based fact-checking tools identify miscarriages of justice by analyzing content and images.
- Data-Driven Journalism: AI is making it easier for journalists to analyze and make sense of large data sets. Using AI-based data analysis tools can help journalists identify patterns, patterns and insights that would have been missed by traditional methods.
- Automatic Translation: AI-based translation tools are helping journalists break language barriers and reach new audiences. Using AI-based translation tools allows journalists to quickly and accurately translate news into different languages.
- Audience Engagement: AI-based chatbots and voice assistants allow journalists to engage with their audience in new and innovative ways during their interviews.
The use of AI in journalism continues to grow. AI is a great tool for journalism that provides many advantages. However, it is also true that AI in journalism has not been able to reach its original features. Artificial intelligence and the future of journalism are going together. The limits of how much AI can be used for journalism still remain to be explored.