The Gatekeeper Between Brands and ConsumersAll four articles share the same overarching theme, Artificial Intelligence: About People, Government, and the Workforce. An article in Forbes magazine revolves around the importance of trust between AI and the consumer and how trust will play an important role in enabling AI to increase efficiency and effectiveness in the business and marketing sector ( DeGobbi 2018). It is argued that as trust in brands has declined among consumers and AI can be used as a means to save time and money and create more trusting relationships between the brand and the consumer and enable marketers to make more decisions informed and less subjective (DeGobbi 2018). Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay This article was chosen for its relevance to the IBA course and the topic: Artificial Intelligence. It provides insights into how AI will and is impacting the workforce marketing industry. Forbes has proven to be a suitable source as it dedicates an entire updated section to artificial intelligence where multiple perspectives can be explored. Found by searching the effect of artificial intelligence on business. Article 2: Artificial intelligence is on the march. But is the government ready? A second Forbes article accentuates the need for new and more effective regulation in terms of artificial intelligence. With the dramatic increase in the use of artificial intelligence, there has been growing unemployment and decreased competition among the most popular technology platforms (Seamans 2018). The article concludes that this may be due to inexperienced and therefore unsympathetic policy makers. Seamans (2018) discusses two solutions: the creation of a regulatory agency specifically for AI, or current agencies hiring their own staff after defining perceived needs. Seamans (2018) concludes that the second solution is the best. This article was chosen for its relevance in today's society, as it discusses the need for new regulations and more experienced policy makers in the field of artificial intelligence. Additionally, there is discussion of a different group of people who will be affected: the government. Forbes was again used as it provides a platform that exclusively discusses AI from different perspectives. Article 3: Do the benefits of AI outweigh the risks? As in the second Forbes article, an article by the economist discusses the need for strict control to be established in the artificial intelligence sector to avoid risks. However, this article focuses its arguments on the reliability and stability of different types of AI: narrow AI and Artificial General Intelligence. The main concern of the article is AGI, as scientists “need to ensure that an Internet-enabled AGI is stable indefinitely and has benevolent properties such as value learning and corrigibility before being deployed” (Ruta 2018) and the need to align AI with human values ​​and morality. This article has provided insight into the advantages and disadvantages of different types of AI, as well as shed light on the fact that much more regulation will be needed. For this reason it was chosen because it allowed another perspective and discussed a new perspective: the need for artificial intelligence to be equipped with human and moral values ​​to avoid risks. The economist is a different, but reliable source that allowed toanalyze a different perspective. Found through the economist search engine. Article 4: Workplace robots "could create twice as many jobs as they destroy The Guardian's fourth article also focuses its argument on the effect of artificial intelligence on the workforce; however, it is argued that an increase in AI will increase the number of jobs available rather than decrease them, illustrating a different position on the topic. Furthermore, the article discusses that for AI to have a positive impact on the workforce, greater investments in job training and training are needed. reskilling, since today all work activities in companies can be performed by machines in 2025 (Partington 2018 This article was chosen for its different interpretation of the topic of artificial intelligence, as it claims that more jobs will be created rather). than due to the general concern that jobs will decline due to artificial intelligence. The Guardian provided a different perspective to other popular media, it was chosen because it had a different style to other websites and therefore provided an in-depth source. The fundamental difference between scientific and everyday knowledge is the way information is collected. Everyday knowledge can be based on individual beliefs and shared or personal (everyday) experiences and is therefore mainly gathered using our senses, intuitions or emotions. Therefore, everyday knowledge can also be defined as conventional knowledge. Unlike scientific knowledge, everyday knowledge does not require universal acceptance, nor does it require to be supported by measurable evidence. Consequently, everyday knowledge can never be universally true. In contrast to everyday knowledge, scientific knowledge includes data that is measurable and reproducible through the scientific method. When collected data is contextualized to form conclusions and relationships, it is separated from personal opinions or emotions, which ensures that scientific knowledge is universally acceptable and accurate. According to BusinessDictionary. com (nd), there are four crucial factors in defining information as scientific knowledge; independent and rigorous testing, peer review and publication, measurement of actual and potential error rates and acceptance within the scientific community. In order for theory or knowledge to pass as scientific knowledge, it must conform to all these four factors. “Information processing errors, which are still cognitive errors, occur when investors irrationally process the information they receive. ” (6bc2 Behavioral biases: cognitive errors). There are various problems with information processing, namely framing bias, anchoring and adjustment, as well as availability bias. The first common error in information processing information is called framing bias. This happens when information is analyzed incorrectly, because the context or wording of the question suggests an answer. Framing bias leads to false information, which can lead to false research or “failure to understand the risk of short-term market movements” (6bc2 Behavioral bias: cognitive errors). This is a problem when arriving at conclusions based on surveys or other types of questionnaires. Secondly, anchoring and adjustment are also a mistake of common processing. This usually happens during negotiations, when investment decisions are based on initial forecasts (6bc2.