We are often told that journalism aims to be objective and neutral. However, journalists, just like everyone else, are exposed to stereotypes and prejudices about real or imagined groups of people in their society and in other countries. Some of these they may be consciously aware of, others may not appear to them as stereotypes at all. The same is true of beliefs about people and communities in other countries. The effect of these stereotypes can be amplified when journalists leave their own country to engage in foreign reporting. In addition to the stereotypes they bring from their own cultural setting, they are now exposed to a different cultural setting with its own actual differences as well as stereotypes. A journalist’s understandable lack of knowledge or linguistic skills can lead to reliance on pre-existing prejudices about the region or culture, or to undue trust of anyone who is able to communicate with them in a way they understand. All of these layers may lead to reporting that is biased, misleading or inaccurate. Bias and stereotyping in foreign reporting can be damaging to international relations when they cause misinterpretation of important events, as diplomats, politicians, and voters rely on media views to understand what actions to take in response to foreign events. Falsely portraying members of a community can also harm members of that community abroad. To avoid amplifying prejudice or bias in foreign reporting, it can be helpful to collaborate with local journalists, to make an effort to acquire cultural and historical context as needed, and to talk to a variety of sources, not just those who speak a shared language or share educational or socio-economic traits with the journalist.

Foreign reporting has often been described as geared towards war, crises, and conflicts. This may hold particularly true for media coverage of those countries that are rather marginalized in international news flows and that come to international attention only in times of conflict or natural disasters. When reporting on armed conflict, journalists often need to cooperate with military forces. With regard to recent wars and particularly the U.S.-led invasion of Iraq in 2003, so-called embedded journalism has been debated as an ethically challenging practice. “Embedded” reporters accompany a particular military unit. While this commonly grants a greater degree of safety to war reporters, it may challenge journalistic independence. However, it is nothing new that war reporters accompany armed forces; the televised immediacy of contemporary wars has, however, put this practice under greater scrutiny.

The question of how autonomous and objective reporters can cover war, especially if their home country is involved as a party to the conflict, or at least has a strategic stake in it, is as old as war reporting itself. Even more than foreign reporting in general, war and conflict coverage has been criticized for following domestic policy lines; focussing on elite actors at the expense of giving voice to ordinary soldiers and civilians on both sides; and pursuing a simplistic conflict narrative emphasizing political and military strategies while neglecting the complex causes and consequences of war. At the same time, war and conflict are typically accompanied by information wars, i.e., the strategic use of communication by the actors involved, which further complicates accurate reporting.

However, some observers have noted changes in more recent conflict reporting, such as a greater emphasis on the micro perspective of the individuals involved rather than the political macro perspective of society as a whole, on the personalities of individual actors rather than on ideology and strategy, as well as on new actors at the expense of the “conventional” focus on governments and the military. It is, however, fair to acknowledge that reporting environments differ between the various wars and conflicts over time and in different regions. War correspondents put themselves in great danger. As war reporting is particularly costly for news media organizations, it is often entrusted to specialized parachutist freelancers who report from the frontline for several media outlets at the same time. However, these freelancers often face specific vulnerabilities. Local fixers and stringers play a crucial role in reporting war and conflict, and often take enormous risks, such as being treated as “traitors” for their affiliation with international reporters.

To address the shortcomings of war and conflict reporting, concepts such as peace journalism or conflict sensitive journalism promote stances that emphasize dialogic, rather than violence-focused and victory-oriented, reporting, giving journalists an active role in conflict resolution. It is important to note, though, that these approaches refer not only to reporting about war and armed conflict, but to reporting on conflict-laden issues as such.

When journalists work together on a project, it is helpful for the project’s success that the team members embrace a collaborative mindset. This means showing equal respect for each team member’s perspective and experience, being open-minded about differences, and being humble about one’s own knowledge and contribution. As for reporters, one’s knowledge is limited. Being part of a team expands the available skills, experiences, and knowledge base of each member. However, this advantage is only available if members embrace a mindset that welcomes diversity rather than fighting against it and recognize that different viewpoints that call out our biases as reporters are indispensable for more accurate reporting. Being aware of our biases and the limits of our own knowledge can also be helpful for getting into a collaborative mindset.

Software in smartphones, smart watches, computers, tablets, smart speakers and even toys can be activated by means of a voice command. Because of the combination of artificial intelligence, machine learning, natural language processing, and big data, voice assistants can recognize the user’s voice, interpret a command or question, and execute what has been requested. Depending on the device it is integrated with, voice assistants are able to play music, present news and recipes, move curtains, turn on lights, issue reports on the user’s health status and many other functionalities that are extended frequently. Voice assistants can be useful for the inclusion of illiterate and visually impaired people in the world of the Internet.

In the 1950s, Bell Laboratories introduced the first known voice-activated assistant, but the road was long until the creation of Apple’s Siri, launched in 2011, that brought this technology to the mainstream. Google launched Google Now in 2012, which was replaced by Google Assistant in 2016. Microsoft launched Cortana in 2014, the same year as the launch of Amazon’s Alexa.

Most assistants use a female voice as a default, and this has generated a big debate about reinforcing women’s image of submission and subservience provoking the normalization of sexist harassment. It is a service that is available in only a few languages. Services in English, which was the first language and is considered the language of the Internet, have a higher success rate in understanding voice commands than other languages.

A voice-activated device or voice command device (VCD) is a device, or hardware, that is controlled with a voice-activated assistant, also referred to as a voice user interface (VUI). This VUI makes it possible for human voice interaction with computing devices, through the use of speech recognition that can decode spoken commands and answer questions.

The number of voice-activated devices is growing, and they are taking new forms, as artificial intelligence is more and more used in all kinds of devices. At present, voice devices can be used in cars; home automation systems (sometimes in so-called smart homes), for example by opening doors, turning lights on or off, or managing climate control by clapping hands or giving specific voice commands; computer operating systems; and home appliances, such as washing machines, refrigerators, televisions remote controls, and vacuum cleaners that operate on voice commands.

Some of the most known voice activated devices are smartphones or smart speakers. Alexa, one of the most known voice-activated assistants, was launched by Amazon with its ECHO smart speakers. These speakers were among the first products to be launched at more affordable prices. The technology had already existed, but with ECHO it was made more available to the general public. Nowadays there is a growing range of voice-activated devices and they have more diverse uses every day.

In the hardware sense, smart speakers are speakers equipped with microphones that need to be charged by electricity and connected to the internet. Normal speakers don’t have microphones.

As they are objects connected to other objects via the internet, smart speakers are part of the Internet of Things (IoT). They work by voice command; they do not have keyboard input. Precisely because of this, they can be used by people with disabilities, less literate people, and by people who have more difficulty in accessing the internet via mobile phones or computers, such as the elderly.

Some models have screens to display in video format the answers to questions asked by users. But the most popular models only respond to user requests via audio.

The devices are equipped with voice assistants.

Smart speakers can answer questions, play music, “read” news, activate appliances, trigger reminders, and more, by voice command. As soon as the user asks a question or requests an action, the system turns the voice input into text, then into data, and then returns the path to answer the user’s request in voice (or video) output.

Voice assistants use Artificial Intelligence, Machine Learning, Natural Language Processing, and Algorithms to try to accurately fulfil the user’s request.

The first smart speaker, Echo, was launched by Amazon in 2014. The device became popularly known by the name of the device’s voice assistant: Alexa. The timeline of smart speakers and voice assistants overlaps, as they are attached to each other. Currently, in the Western market, three brands produce smart speakers: Amazon, Google, and Apple. Some publishers are experimenting with the use of smart speakers and voice assistants for news. It is still an emerging field, but as we find them in our daily lives in subjects such as cars, kitchen appliances and other, they may be a way for journalism to thrive.

Natural Language Processing (NLP) is one of the branches of Artificial Intelligence focused on the analysis of human language. It is divided into different levels of processing: Phonology, Morphology and Lexical.

The first, Phonology, is about the interpretation of sound. It considers phonetic and phonemic rules and prosody (the intonation and rhythm of speech). Morphology deals with morphemes, the small units of meaning of a word, for example the understanding of prefixes and suffixes. Lexical language processing interprets the meanings of the word itself, taking into account the context of application. Interpretation at the lexical level requires syntactic, semantic, discursive, and pragmatic knowledge.

In voice assistants, NLP works in three steps: First, it converts speech to text. The system processes the set of sounds and transforms them into words. After that, the system processes the words and triggers other areas of Artificial Intelligence to search for the answer. Finally, the voice assistant transforms the words into sound to respond to the user.

NLP-based systems need to be trained. For a machine to recognize, “understand”, “interpret”, and answer a human query, it needs to have a large database, as diverse as possible.

Automatic Speech Recognition is known by the acronym ASR. It is an area of computing that uses Artificial Intelligence, algorithms, and machine learning with the objective of recognizing and transcribing spoken human language automatically by machine. It involves research from the fields of linguistics, computer science, and electrical engineering. For the technology to work, the computer must have a microphone. The sound is picked up by the microphone and the system identifies what is a human voice. What is said by the person is transcribed by the system, which transforms the content into commands for the machine.

There are systems that need training, when a person reads texts, and the system specifically analyses that speech, providing greater accuracy. Systems that do not use training are called “speaker-independent” systems. They use big data to do speech recognition. Speech recognition is often confused with voice recognition. While in speech recognition the goal is to discover the who, i.e., only to identify the voice of an individual user, the focus of speech recognition is the content, converting human speech from a verbal format into a written text that will be understood by the machine. Speech recognition studies began with the expansion of telephony at the end of the 19th century and beginning of the 20th century. But the biggest development in this area has come with the advance of machine learning and big data.

A chatbot is software that simulates the conversation of a human being. The interaction between machine and human being through the chatbot occurs through written text or speech. It is a feature that automates repetitive tasks and reduces the need for human involvement in customer service, for example. The word chatbot comes from a combination of chatter and bot (short for robot). There are two types of chatbots: those that are rules-based (simple chatbot) and those based on artificial intelligence (advanced or “smart” chatbot). Simple chatbots are word-based that serve as a parameter with keyword usage and a well-defined navigation flow. In this case, if the message sent by a person does not contain a keyword the system will not be able to respond to the command. A smart chatbot uses Artificial Intelligence to respond to messages from humans. It uses the information supplied by the person at the time of interaction and searches for information available in the system to answer questions. Instead of pre-programmed answers, the system responds in a personalized way, even if the question was not programmed in the system.

The development of chatbots began in the 1960s. Researchers consider Eliza, created by Massachusetts Institute of Technology (MIT) researcher Joseph Weizenbaum, the world’s first chatbot. In those days, building a chatbot was very difficult; now it can be harder or easier, depending on what kind of chatbot you need. But even if you lack significant knowledge about programming, you can create a chatbot using tools like Smarkio, Tidio, or others. Nowadays there are several examples of what can be considered a chatbot; they are used by supermarkets (for example, Lidl has Margot, its Wine Bot) and in online sales, health, media etc. They are also seen in movies and literature; a chatbot, voiced by Scarlett Johansson, starred the 2013 film Her by Spike Jonze.

Conversational Artificial Intelligence (CAI) is a system that uses machine learning and natural language processing to get closer to human natural language. It is currently used by many companies that manage chatbots, because it is a system that has a high hit rate and is very easy for humans to access. This system allows the machine to understand written messages or audio more complex than just one sentence. The main difference between a traditional chatbot and a CAI chatbot is that, while with traditional chatbots the user needs to type a basic and precise command to get a response, and responses are always the same (for a certain question there is a certain answer), chatbots with conversational artificial intelligence are able to perceive nuances in messages, recognize the speech (text or audio), make the interpretation, decipher different languages and respond in a “humanized” way, and adapt the answers, allowing better communication between machine and human. The efficiency of such a system depends on training and a good database. The more information the program has, the better are the chances of understanding the user’s need. As with most AI systems, CAI depends on big data to be most effective. It is possible to integrate a chatbot with conversational artificial intelligence in various messaging programs, such as the social network WhatsApp. It is one more technology that frees human beings from repetitive work, but it is also another example of replacing humans with machines. And in the case of a highly sophisticated system, CAI can raise ethical questions since it can lead the user to think that he is talking to a person, when in fact he is talking to a machine.