Artificial Intelligence and Natural Language Processing Applications in Tech Industries

Natural Language Processing and Artificial Intelligence in the Tech Industry

The terms “technology industry” or “tech industry” refer to companies that are engaged in designing, manufacturing and/or marketing various electronic devices including computers and computer-based systems of all types as well as computer software and services and scientific componentry and related products (Technology background, 2022). This definition makes it clear that the tech industry subsumes a wide array of enterprises that are engaged in numerous initiatives to improve the functionality of their computer-based applications as well as the ways in which they are used. Moreover, in many ways, the tech industry differs from most other industries because of the accelerating pace of innovation that requires intensive research and development efforts (Technology background, 2022).

This definition indicates that many of the companies that are currently competing in the tech industry are the entities that are responsible for providing consumers in the public and private sectors with computer software such as artificial intelligence (AI) and natural language processing (NLP) applications that can be used in countless ways by consumers as well as organizations of all sizes and types. Indeed, new uses for AI and NLP are being developed every day, and their potential seems unlimited at present. For example, according to Schroer (2022), “From Google and Amazon to Apple and Microsoft, every major tech company is dedicating resources to breakthroughs in artificial intelligence. Personal assistants like Siri and Alexa have made AI a part of our daily lives” (para. 2).

In fact, digital assistants represent one of the most significant ways that the tech industry is leveraging its investments in AI and NLP to generate additional revenues and grow their market share. For instance, Brill et al. (2019) note that, “Digital assistants (e.g., Apple’s Siri, Amazon’s Alexa, Google’s Google Assistant) are highly complex and advanced artificial intelligence (AI) based technologies. Individuals can use digital assistants to perform basic personal tasks as well as for more advanced capabilities” (p. 1401). In addition, Amazon recently announced that it is updating its Alexa digital assistant so that it can mimic any human voice, including those of deceased relatives and friends (Boucher, 2022). In this regard, Amazon’s lead scientist on this project emphasized that, “"We're unquestionably living in the golden era of AI, where our dreams and science fiction are becoming a reality” (as cited in Boucher, 2022, para. 4). Notwithstanding the inherent creepiness of this “dead relative voice” innovation, it is clear that tech companies are making digital assistants more personal and intuitive to use, thereby making them even more attractive to a wider group of consumers.

Therefore, from one perspective, tech companies are using innovative AI and NLP applications to generate new revenues, but this is just part of a much larger picture (Li et al., 2019). Although precise figures and detailed proprietary information concerning how tech companies are using AI and NLP for other purposes, there is a growing body of evidence that indicates that these organizations are using AI and NLP to harvest consumer data in numerous ways to gain new insights concerning how these technologies are being used in order to fine-tune their future product offerings. In this regard, Freedman (2022) points out that, “Artificial intelligence is a critical tool for data capture, analysis, and collection of information that many businesses are using for a range of purposes, including better understanding day-to-day operations, making more informed business decisions and learning about their customers” (para. 3).

While not all uses of personal data are inherently insidious in design, many consumers and organizations may simply be unaware of just how much detailed information is being collected about them by tech companies, typically without their knowledge or informed consent. Not surprisingly, the growing number of ways that this valuable data can be used for marketing purposes has attracted even more players to this area, and many tech companies are focusing on this area exclusively as their business model (Freedman, 2022). Some examples of the types of consumer data that are collected by tech companies and the purposes for which they are used are set forth in Table 1 below.

Table 1

Types of consumer data collected by tech companies and their uses

Data Type

Description of Uses

Personal data

This category includes personally identifiable information such as Social Security numbers and gender as well as non-personally identifiable information, including IP address, web browser cookies, and device IDs (laptops and mobile devices both have these features).

Engagement data

This type of data details how consumers interact with a business’s website, mobile apps, text messages, social media pages, emails, paid ads and customer service routes.

Behavioral data

This category includes transactional details such as purchase histories, product usage information (e.g., repeated actions), and qualitative data (e.g., mouse movement information).

Attitudinal data

This data type encompasses metrics on consumer satisfaction, purchase criteria, product desirability and more.

Source: Adapted from Freedman (2022)

It is also important to note that tech companies are relentless in their efforts to locate consumer data, but the process is generally straightforward enough with this information concerning consumer activities being readily available on individual web sites, social media platforms, live chat forums and telephone contacts. In other cases, though, data collection strategies resemble those used by the CIA and smack of more sinister intentions. For instance, according to Freedman, “Location-based advertising utilizes tracking technologies such as an internet-connected device’s IP address (and the other devices it interacts with – your laptop may interact with your mobile device and vice versa) to build a personalized data profile. This information is then used to target users’ devices with hyperpersonalized, relevant advertising” (2022, para. 7). Likewise, tech companies also mine big data such as consumer service records to identify the frequency and duration of online or telephonic contacts with technical services and sales departments to determine consumer preferences (Freedman, 2022).

More troubling still is the fact that tech companies routinely sell the personal information they collect to third party vendors that do not disclose this process to consumers. In other words, virtually everything that people say and do is being monitored by AI-enabled applications, including NLP which can deduce consumer interests and preferences simply by listening in to ordinary conversations. This Orwellian eventuality has created a concomitant need for an ethical framework for tech companies to follow today and in the future. For example, Metcalf et al. (2019) emphasize that, “Given the increasing power and centrality of artificial intelligence in everyday life, there is an urgent need for a coherent approach to addressing ethics, values, and moral consequences” (p. 451). An ethical framework, however, is only part of a far more complex solution due to the very nature of the tech industry and the nebulosity of its AI-based activities. As Metcalf and his associates conclude, “This need is complicated by the centrality of personal data to many of the tech industry’s products, its own discourse presenting itself as ‘making the world a better place,’ and the lack of a timely regulatory response to many of the industry’s problems” (p. 451).