Is Artificial Intelligence a Threat or a Blessing?
Just like the previous industrial revolutions, which automated agricultural and manufacturing work, the fourth industrial revolution will bring disruption and upheaval to the job market and to society as a whole. As the fourth industrial revolution prevails, AI adoption continues to grow and AI’s use in business becomes more common. Last year, McKinsey held a survey to discover the current growth of AI in business. According to the survey, about 56% of survey respondents are adopting AI (using AI capabilities) in at least one function, or up from 50% in 2020. The adoption of AI has increased most at companies headquartered in emerging economies, which include China, the Middle East, and North Africa. The survey also reported that the business functions where AI adoption is most common include service operations, product and service development, and marketing and sales, while the top three use cases include service-operations optimization, AI-based enhancement of products, and contact-center automation, with the biggest percentage-point increase in the use of AI being in companies’ marketing-budget allocation and spending effectiveness.
In the human resources business function, AI is commonly used for the optimization of talent management and performance management. Last but not least, the survey found that cybersecurity remains the most recognized risk among respondents, where this risk is more prevalent in developed economies (57%) rather than emerging economies (47%) and the top five risks that are considered relevant by organizations include cybersecurity, regulatory compliance, explainability (the ability to explain how AI models come to their decisions), personal/individual privacy, and organizational reputation.
Even though AI adoption continues to grow, not all people are welcoming the idea of automated technologies because there are different possible outcomes of the widespread adoption of AI (Bornet, 2021; De Cremer & Kasparov, 2021). Some argue that due to its imitating abilities, AI has the quality to identify informational patterns that optimize trends relevant to the job. It also never gets physically tiring, which is why AI is perfectly suited to put at work lower-level routine tasks that are repetitive and take place within a closed management system to optimize efficiency and productivity. The adoption of AI would yield significant productivity gains which lead to vastly improved standards of living and a net increase in jobs.
Even when people are scared of losing their jobs to AI, some are optimistic that AI adoption will instead create more new jobs than it will destroy, resulting in a net gain of jobs. In 2018, The World Economic Forum predicted that workplace automation would create 133 million new jobs by 2022 while displacing only 75 million, resulting in a net increase of 58 million jobs (Cann, 2018). However, some people believe that jobs will be eliminated more quickly than employees can reskill, leaving a generation of people unemployable, and AI experts even believe that within the next decade, AI will outperform humans at writing high school essays, driving and working in retail by 2049. Furthermore, they predict a 50% chance of AI outperforming humans at all tasks within 45 years.
As we all know, AI is a very sophisticated technology that could be used for many activities in a variety of sectors because AI-based machines are fast, more accurate, and consistently rational. But they aren’t intuitive, emotional, or culturally sensitive, which could raise concerns about the validity of the capabilities of the AI in executing certain tasks. Recently, a new feature called Emotional Artificial Intelligence (EAI) is built to extract data on a person’s emotional state in various ways and some companies have started to utilize this technology for recruiting workers, assessing employee well-being, gauging customers’ sentiments, and even identifying patterns associated with workplace harassment.
However, there are still some concerns that arise from this technology. First, AI itself does not understand what emotions are or how they are constructed. Second, EAI does not have the ability to take into account human particularities such as attitudinal diversity, gender differences, or cultural idiosyncrasies (Mantello et al., 2021). Although there is still an ongoing debate regarding the utilization of AI, many organizations around the globe try to take advantage of these technological signs of progress, leading to the development of new AI-based applications that allow automating decision- making processes.
Application of AI in the Workplace:
1. Recruitment and Selection Process
AI-based processes can be implemented in different stages of the recruiting and selection process, for example in application screenings as well as in job interviews. Companies like IBM, LinkedIn, FedEx, and KPMG use AI to screen the curriculum vitae (CVs) of their applicants, while FedEx and KPMG utilize AI to determine the suitability of a candidate for a specific position. These companies decide to utilize AI because of the advantages that it offers, which include increased efficiency, time savings, fairness, and the prevention of nepotism. However, companies need to recognize that the implementation of AI in the recruitment and selection process might have its own disadvantages too (Goretzko & Israel, 2022). In 2015, for example, Amazon realized that its automated selection algorithm for technical positions favored male applicants by penalizing female resumes (Dastin, 2018). Besides the problem of algorithmic fairness, legal restrictions, and data protection policies are some other challenges that might be faced by companies when utilizing AI in the recruitment and selection process. Another issue that surfaces with the utilization of AI are the negative job- seekers or applicants’ reaction toward AI-based processes.
Wesche & Sonderegger (2021) and Schick & Fischer (2021) found that job-seekers were deterred by the prospect of undergoing an automated selection procedure, where they feel skeptical toward the assessment quality of AI-intense selection processes, especially if these assess complex assessment criteria such as personality or a job performance forecast and during the process of the job interview. Wesche and Sonderegger’s study indicates that there are negative consequences of communicating the use of AI-based technology in the screening stage of recruiting and selection processes on job-seekers reactions because they might think that the automated systems are primarily employed for time- and money-saving reasons which could be perceived as the lack of appreciation and social recognition on job-seekers and applicants. In addition, Schick and Fisher’s study indicates that job candidates seem to be skeptical of complex AI due to a lack of understanding. To reduce the negative effects of AI-based processes during the recruitment and selection process, companies counteract the information with additional information that may positively target job-seekers beliefs regarding AI-based selection tools, for instance, appreciation and support from the organization, the validated capabilities of the AI tools used, or opportunities to demonstrate individual strengths (Wesche & Sonderegger, 2021).
2. Behavioral Biometrics for Employee Experience
Biometrics is a tool used to identify and reliably confirm an individual’s identity on the basis of physiological or behavioral characteristics (or a combination of both), which are unique to a specific human being (Future of Identity in the Information Society, 2009). The use of biometrics today largely serves two aims: (i) to distinguish one person from another through verification or identification methods and (ii) to predict someone’s behavior or intentions. The second aim is more ambitious, involving an automatic interpretation or decision about a person; this process of identification results in a classification.
Human minds are fragile and full of bias when it comes to decision-making (Haselton, Nettle, & Murray, 2015), while our physiological reaction always shows the truth about what we experienced at that moment (Persiani, et.al., 2021). The second-generation biometric systems are focused on more intricate behavioral patterns, as indicated by gait or movement of the body or by biological traits, states, and conditions of the body (e.g., heat, smell, electrocardiogram, or DNA), with the aim of profiling people on the basis of prediction of their actions and behaviors (Sutrop & Laas-Miko, 2012). They can assess how well people know the information they are entering and how familiar they are with the application they are using by how they engage with it. Even the daily-based technology such as smartwatch to identify current physiological state, smartphone to monitor their daily routines, and webcam data to show which part of the screen engaged them, are able to be used to give suggestions about what they should do or what should be improved from a product. Behavioral biometrics are very attractive since they are completely passive, generally do not require a change in the user experience, and also can solve problems that traditional forms of cybersecurity do not (Turgeman, 2017).
However, the modern concept of privacy implies respect for the autonomy of the person; in the field of scientific research, this is connected with the moral and legal claim for informed consent. In the cases of distant screening and data capture, this condition is not fulfilled. Thus, from the ethical point of view, the main problem with behavioral biometrics is that it does not make any attempt to take into account the person’s self-identification; the person’s behavior or physical characteristics are interpreted and viewed without any deeper knowledge of the person’s own point of view.
But, aside from its ethical issues, behavioral biometrics could be used for so many beneficial activities. One of them is to ensure individuals’ well-being. According to Finnegan (2021), Microsoft has patented an employee “wellbeing” recommendation feature that uses biometric data to detect a worker’s stress levels when completing tasks such as sending emails, encouraging them to take a break when anxiety levels run high. The “Emotion Detection From Contextual Signals For Surfacing Wellness Insights” patent, filed in October 2019 and published in 2021, describes a “wellness insights service” that collates data from a range of sources. This includes blood pressure and heart rate monitoring data that could be obtained from an employees’ wearable devices, such as smartwatches and fitness trackers.
3. Knowledge Management
At present time, many companies around the world are investing their funds into knowledge management in order to give faster access to knowledge and information to their employees, improve the decision-making process, promote cultural change and innovation, improve efficiency, and also increase customer satisfaction through an enhanced customer experience (Eliyahu, 2020). In the past, printed materials were the primary source of information for companies. However, as technologies keep on improving as time goes on, it is easier for companies to find, gain, and share information by utilizing knowledge management systems that are now digitally transformed by the existence of artificial intelligence. By using the latest technologies, artificial intelligence solves the difficulties that are faced by companies as they manage knowledge, simplify knowledge discovery, aid employees in creating content related to knowledge and information sharing, and make it easier for people to find the knowledge or information that they’re looking for (Murugesan, 2021).
One of the latest technologies that could be utilized by artificial intelligence is natural language processing (NLP) and graph-based algorithms to bring relevant content based on keywords. In addition, text analytics can automatically prompt relevant tags for each knowledge base content and amplifies artificial intelligence function in bringing relevant content that leads to the enhancement of employee productivity as it becomes easier for them to find the right information quickly. Another function that could be done by artificial intelligence is assisting in writing and enriching content, as it could automatically correct typos, fix grammatical mistakes, offer suggestions to improve readability, and even suggest the right glossary terms which creates understanding among all employees and leads to organizational resilience. Not only it could simplify knowledge discovery and aid in content creation, but artificial intelligence could also alert employees to acquire new skills or knowledge based on their current expertise and the knowledge content that they access that will improve employees’ skills and enable them to be cross-trained. Thus, integrating artificial intelligence into knowledge management would result in greater productivity and improve the utilization of knowledge content.
Throughout this brief explanation, we could conclude that AI is indeed a sophisticated technology that could be both beneficial and threatening in the future. But, just like other things that were made by humans, AI is not a perfect technology as it is still lacking in some aspects. Also, despite the fact that we still don’t know how the utilization of AI will exactly affect our situation in the future, we could not avoid the massive growth of AI adoption. Therefore, it will be better for the workplace and workforce to prepare themselves, adapt to the situation, and use AI as optimally for their benefit. (RBA, OCY)
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