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All four articles share the same main theme, artificial intelligence: on people, the government, and the workforce. An article on Forbes magazine revolves around the importance of trust between AI and the consumer, and how trust will play a major role in allowing AI to increase efficiency and effectiveness in the business and marketing sector (DeGobbi 2018). It states that because trust in brands has decreased for 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 for marketers to make better informed and less subjective decisions (DeGobbi 2018).
This article was chosen because of its relevance to IBA as a course and to the theme: Artificial Intelligence. It provides insight into how AI will and is affecting the marketing sector of the workforce. Forbes was a suitable source as it dedicates a whole up-to-date section to Artificial intelligence where multiple perspectives can be explored. Found by searching the effect on business of AI. Article 2: Artificial Intelligence Is On The March. But Is Government Ready?
A second article from Forbes accentuates the need for new and more effective regulation in terms of AI. With the dramatic increase in the use of AI, there has been growing unemployment and a decrease in competition between popular tech platforms (Seamans 2018). The article concludes that this could be due to an inexperienced and thus non-understanding policymakers. Seamans (2018) discusses two solutions: the creation of an AI-specific regulatory agency, or current agencies hiring their own staff after defining their perceived needs. Seamans (2018) concludes that the second solution is best.
This article was chosen due to its relevance in today’s society, as it discusses the need for new regulations and more experienced policymakers in the AI sector. Furthermore, is discusses a different set of people that will be affected: the government. Forbes was again used as it provides a platform that solely discusses AI from different perspectives. Article 3: Do the benefits of artificial intelligence outweigh the risks?
Like the second article from Forbes, an article from the economist discusses the need for rigorous control to be set in the AI sector in order to avoid risks. However, this article centers its arguments around the reliability and stability of different types of AI: narrow AI, and Artificial General Intelligence. The main preoccupation of the article is AGI, as scientists “must ensure that an internet-enabled AGI is indefinitely stable 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 morals.
This article provided insight into the advantages and disadvantages of different types of AI as well as shedding light on the fact that much more regulation will be needed. For this reason, it was chosen as it allowed for another perspective and discussed a new angle: the need for AI to have human values and morals in order to avoid risks. The economist is a different, but reliable source which allowed for a different perspective to be analyzed. Found through the search engine of the economist. Article 4: Robots in workplace ‘could create double the jobs they destroy
A fourth article from The Guardian, also centers its argument around the effect of AI on the workforce; however, it discusses that an increase in AI will increase the number of jobs available rather than decrease them, illustrating a different stance on the argument. Furthermore, the article discusses that in order for AI to have a positive impact on the workforce, greater investments in vocational training and reskilling are required as all the workplace tasks at companies today can be performed by machines be 2025 (Partington 2018).
This article was chosen for its different spin on the argument of AI, as it argues that more jobs will be created rather than the general worry that jobs will decrease due to AI. The Guardian provided a different perspective to the other popular media, it was chosen as it was a different style to the other websites and thus provided an insightful source.
The fundamental difference between scientific and everyday knowledge is the way the information is gathered. Everyday knowledge can be based on individual’s beliefs and shared or personal (everyday) experiences, and is therefore primarily collected using our own senses, intuitions or emotions.
Therefore, everyday knowledge can also be defined as conventional knowledge. Dissimilar to scientific knowledge, everyday knowledge does not require universal acceptance, nor does it require to be backed up by measurable proof. Consequently, everyday knowledge can never be universally true. Opposite to everyday knowledge, scientific knowledge encompasses data that is both 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 (n. d. ), there are four factors that are crucial when defining information as scientific knowledge; independent and rigorous testing, peer review and publication, measurement of actual and potential rate of error and the degree of acceptance within the scientific community. In order for theory or knowledge to pass as scientific knowledge, it has to conform to all these four factors.
“Information processing errors, which are still cognitive errors, occur when investors irrationally process the information that they receive. ” (6bc2 Behavioural biases: Cognitive errors). There are various issues with information processing namely, Framing bias, anchoring and Adjustment as well as availability bias. The first common error in information processing is called framing bias. This is when information is analysed wrongly, because the context or the phrasing of the question hinted towards one answer. Framing bias leads to false information, which can result in false research or “failure to understand the risk of short-term market movements” (6bc2 Behavioural biases: Cognitive errors). This is an issue when coming to conclusions based off surveys or other types of questionnaires. Secondly, Anchoring and Adjustment is also a common processing error. This is usually during negotiations, when investment decisions are based off initial forecasts (6bc2 Behavioural biases: Cognitive errors). These valuations will still be used as “anchors” despite their irrelevance after a certain time point due to evidence suggesting a change in the valuation. This is a factor that causes doubt in some information, as investors would like to believe in the success of their investments.
Lastly, Availability Bias is when investors prefer a certain product because of their extensive publicity or availability. Their extensive availability does not mean that the information/product is superior, just that the marketing of this – sometimes false – information is more efficient. Availability bias is an issue for the trustworthiness of information as investors will have seen certain information more often, despite its falsehood. Meaning that this information is now treated as truth. These three arguments show that certain incorrect information is often portrayed as the truth, through the methods described above, in order to lure investors or consumers to buy a product. Because of these biases one must be careful today when looking through data, as there is so much.
To what extent does the development of Artificial Intelligence affect consumer trust in business? Our group chose this question as business is an integral part of our course, a sector in which we could one day be working in. Furthermore, we are all personally interested in the steps firms will take to make their business strategies more successful through the use of AI in the future.
To explore and assess what impact Artificial Intelligence has on consumer trust. Our research objective is exploratory in its nature. We want to explore how and to what extent Artificial Intelligence has an effect on consumer trust in businesses.
Our research will be fundamental. This is because our research will be exploratory in its nature, as we wish to understand the relationship between Artificial Intelligence and consumer trust. As we do not seek for a solution with our findings, our research cannot be labelled as applied research.
Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices
Article 1 was found through google scholar by using the following terms: “artificial intelligence impact on society trust” into the search engine. The article is the fourth link that appears under the name: “Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices”.
The article revolves around the need for consumer trust in “applied artificial intelligence in its weak notion, which is defined in terms of the tasks humans do rather than how humans think” (Hengstler, Enkel, Duelli, S. 2016). The article analyzes the research question: “how is trust in applied AI fostered?” (Hengstler, Enkel, Duelli, S. 2016) and continues on to breaking down the different aspects of trust in applied AI. It states that trust is more easily destroyed than created and is “essential to reducing perceived risk” (Hengstler, Enkel, Duelli, S. 2016). After explaining different aspects of trust, the article analyzes trust specifically in technology, stating: “over time, trust in automation evolves alongside the three dimensions of predictability, dependability, and faith” (Hengstler, Enkel, Duelli, S. 2016). It concludes by stating that innovating firms need to communicate and be transparent with their consumers in order to succeed, emphasizing consumer resistance to change.
This article related consumer trust to artificial intelligence describing how different aspects of trust need to be considered in order for AI to successfully be implemented in businesses. Thus it partially answers the research question as it analyzes the importance of consumer trust for businesses and the different parts of trust that need to be taken into account like perceived risk and the fact that level of advancement of the technology is not the problem for consumers but rather transparency and communication along with performance, process, and purpose or the technology.
The article described above is reliable as it scores highly in the journal listing.
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