The integration of artificial intelligence in various industries is transforming the nature of work leading to increased human-AI collaboration. While AI has the potential to automate certain tasks and displace jobs it also creates new opportunities for human professionals to focus on high-value tasks that require creativity problem-solving skills and emotional intelligence.
However the impact of AI on creative professions such as graphic design writing and music composition is a topic of ongoing debate. On one hand AI-powered tools can assist creatives with repetitive tasks freeing them up to focus on more complex and innovative projects but the increasing reliance on AI-generated content also raises concerns about authorship and ownership.
The future of work will likely involve a mix of human and AI capabilities with each playing to their respective strengths while AI excels at processing large datasets and performing repetitive tasks humans bring creativity empathy and critical thinking skills to the table.
Defining Artificial Intelligence
Artificial Intelligence (AI) is generally defined as the development of computer systems that can perform tasks typically requiring human intelligence, such as visual perception, speech recognition, decision-making, and language translation. This definition is supported by multiple sources, including the Association for the Advancement of Artificial Intelligence (AAAI), which defines AI as “the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines” (AAAI, n.d.). Similarly, the journal article “Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig defines AI as “the study of how to make computers do things that would require intelligence if done by humans” (Russell & Norvig, 2020).
The term Artificial Intelligence was first coined in 1956 by John McCarthy, a computer scientist and cognitive scientist, at the Dartmouth Summer Research Project on Artificial Intelligence. This project aimed to explore ways to create machines that could simulate human thought processes (McCarthy et al., 1955). Since then, AI has evolved significantly, with various subfields emerging, including machine learning, natural language processing, and robotics.
Machine learning is a key aspect of AI, enabling computers to learn from data without being explicitly programmed. This concept was first introduced by Arthur Samuel in 1959, who defined machine learning as “the ability to learn without any explicit programming” (Samuel, 1959). Today, machine learning algorithms are widely used in various applications, including image recognition, speech recognition, and natural language processing.
The development of AI has been driven by advances in computer science, mathematics, and engineering. The creation of the first artificial neural network by Warren McCulloch and Walter Pitts in 1943 laid the foundation for modern AI research (McCulloch & Pitts, 1943). Since then, significant progress has been made in developing more sophisticated algorithms and models that can simulate human thought processes.
The field of AI is rapidly evolving, with new breakthroughs and applications emerging continuously. However, there are also concerns about the potential risks and challenges associated with AI, including job displacement, bias, and security threats. As AI continues to advance, it is essential to address these concerns through ongoing research, development, and regulation.
History Of Automation And Jobs
The concept of automation dates back to the early 19th century, when inventors like Jacques de Vaucanson created mechanical devices that could perform tasks autonomously . However, it wasn’t until the Industrial Revolution that automation began to have a significant impact on jobs. The introduction of textile machinery in Britain during the late 18th and early 19th centuries led to widespread unemployment among skilled artisans and weavers .
The development of assembly lines by Henry Ford in the early 20th century further accelerated the process of automation, making it possible for factories to produce goods more quickly and cheaply. While this increased efficiency and productivity, it also led to job losses as machines replaced human workers on the production line . According to a study published in the Journal of Economic History, between 1920 and 1940, the number of manufacturing jobs in the United States declined by over 30% due to automation .
The introduction of computers and robotics in the mid-20th century marked another significant milestone in the history of automation. The development of the first industrial robot, Unimate, in 1961 revolutionized manufacturing processes and led to further job losses as machines took over tasks previously performed by humans . A study published in the Journal of Labor Economics found that between 1970 and 1990, the introduction of robots in the US manufacturing sector led to a decline of over 20% in employment .
The rise of artificial intelligence (AI) and machine learning algorithms has further accelerated the process of automation. According to a report by the McKinsey Global Institute, up to 800 million jobs could be lost worldwide due to automation by 2030 . However, the same report also notes that while AI may displace some jobs, it will also create new ones, such as in fields related to data science and analytics.
The impact of automation on jobs has been a topic of debate among economists and policymakers. Some argue that automation will lead to significant job losses and exacerbate income inequality . Others contend that while automation may displace some jobs, it will also increase productivity and create new opportunities for workers .
Historical data suggests that the impact of automation on employment has been mixed. While automation has led to job losses in certain sectors, it has also created new industries and job opportunities. According to a study published in the Journal of Economic Perspectives, between 1980 and 2015, the US economy experienced significant growth in productivity due to technological advancements, including automation .
Current State Of AI Adoption
The current state of AI adoption is characterized by increasing investment in AI research and development, with many organizations exploring the potential applications of AI across various industries. According to a report by McKinsey Global Institute, the total investment in AI research and development has grown significantly over the past few years, from $1.4 billion in 2015 to $39.6 billion in 2020 (Manyika et al., 2020). This growth is driven by the increasing availability of large datasets, advances in computing power, and the development of new AI algorithms.
The adoption of AI is also being driven by the growing demand for automation and efficiency in various industries. A survey conducted by the International Data Corporation found that 75% of organizations worldwide are currently using or planning to use AI-powered solutions to automate business processes (IDC, 2020). The same survey also found that the top three areas where organizations are applying AI are customer service, sales and marketing, and supply chain management.
Despite the growing adoption of AI, there are still significant challenges to be addressed. One of the major concerns is the lack of transparency and explainability in AI decision-making processes. According to a report by the Harvard Business Review, many organizations are struggling to understand how their AI systems arrive at certain decisions, which can lead to mistrust and skepticism (Davenport & Dyché, 2019). This lack of transparency can also make it difficult for organizations to identify biases in their AI systems.
Another challenge facing the adoption of AI is the need for specialized skills and expertise. According to a report by Gartner, the demand for AI-related skills such as machine learning engineering and natural language processing has grown significantly over the past few years (Gartner, 2020). However, many organizations are struggling to find talent with these skills, which can slow down their AI adoption efforts.
The increasing use of AI is also raising concerns about job displacement. According to a report by the McKinsey Global Institute, up to 800 million jobs could be lost worldwide due to automation and AI by 2030 (Manyika et al., 2017). However, the same report also notes that while AI may displace some jobs, it will also create new ones, such as in fields related to AI development and deployment.
The use of AI is also being explored in various social sectors, including education and healthcare. According to a report by the World Health Organization, AI has the potential to improve healthcare outcomes by analyzing large amounts of medical data and identifying patterns that may not be apparent to human clinicians (WHO, 2020). Similarly, AI-powered adaptive learning systems are being used in education to personalize learning experiences for students.
Industries Most Vulnerable To AI
The retail industry is one of the most vulnerable to AI, with a study by the McKinsey Global Institute estimating that up to 73% of tasks in retail can be automated (Manyika et al., 2017). This includes tasks such as customer service, inventory management, and supply chain optimization. For example, chatbots are already being used by many retailers to provide customer support and answer frequently asked questions.
The manufacturing industry is also highly vulnerable to AI, with a study by the International Federation of Robotics estimating that up to 85% of tasks in manufacturing can be automated (IFR, 2020). This includes tasks such as assembly, inspection, and packaging. For example, many manufacturers are already using machine learning algorithms to optimize production processes and predict maintenance needs.
The transportation industry is also at risk from AI, with a study by the International Transport Forum estimating that up to 70% of jobs in the sector could be automated (ITF, 2019). This includes tasks such as driving, navigation, and logistics management. For example, many companies are already testing self-driving trucks and taxis.
The finance industry is also vulnerable to AI, with a study by the Financial Stability Board estimating that up to 60% of jobs in the sector could be automated (FSB, 2017). This includes tasks such as data analysis, risk assessment, and customer service. For example, many banks are already using machine learning algorithms to detect fraud and optimize investment portfolios.
The education industry is also at risk from AI, with a study by the National Center for Education Statistics estimating that up to 40% of jobs in the sector could be automated (NCES, 2020). This includes tasks such as grading, lesson planning, and student assessment. For example, many universities are already using AI-powered tools to personalize learning and provide real-time feedback.
The healthcare industry is also vulnerable to AI, with a study by the National Academy of Medicine estimating that up to 30% of jobs in the sector could be automated (NAM, 2019). This includes tasks such as diagnosis, treatment planning, and patient monitoring. For example, many hospitals are already using machine learning algorithms to analyze medical images and predict patient outcomes.
Job Displacement Vs Job Creation Debate
The debate surrounding job displacement versus job creation in the context of artificial intelligence (AI) is multifaceted and contentious. On one hand, some experts argue that AI will displace human workers, particularly those engaged in routine and repetitive tasks. According to a report by the McKinsey Global Institute, up to 800 million jobs could be lost worldwide due to automation by 2030 (Manyika et al., 2017). This perspective is supported by research from the Oxford Martin School, which suggests that nearly half of all occupations are at high risk of being automated (Frey & Osborne, 2013).
On the other hand, proponents of AI-driven job creation argue that while automation may displace some jobs, it will also create new ones. A report by the World Economic Forum estimates that by 2022, more than a third of the desired skills for most jobs will be comprised of skills that are not yet considered crucial to the job today (WEF, 2018). This perspective is supported by research from the Harvard Business Review, which suggests that AI has the potential to create new job categories and industries that we cannot yet anticipate (Brynjolfsson & McAfee, 2017).
Moreover, some experts argue that the impact of AI on employment will be more nuanced than a simple displacement versus creation dichotomy. According to research from the MIT Initiative on the Digital Economy, while automation may displace some jobs, it will also augment human capabilities and create new opportunities for workers (Brynjolfsson et al., 2014). This perspective is supported by a report from the Brookings Institution, which suggests that AI has the potential to enhance productivity and economic growth, leading to increased employment opportunities (Muro & Whiton, 2017).
However, concerns about job displacement remain. Research from the University of California, Berkeley suggests that the benefits of automation are often concentrated among business owners and shareholders, while workers may bear the costs of displacement (Acemoglu & Restrepo, 2020). This perspective is supported by a report from the Economic Policy Institute, which argues that policymakers must take steps to mitigate the negative impacts of automation on workers (Mishel et al., 2017).
The debate surrounding job displacement versus job creation in the context of AI highlights the need for nuanced and evidence-based policy discussions. According to research from the National Bureau of Economic Research, policymakers must consider a range of factors, including education and training programs, social safety nets, and labor market regulations (Goldin & Katz, 2019). This perspective is supported by a report from the Organization for Economic Cooperation and Development, which argues that governments must invest in lifelong learning and skills development to prepare workers for an increasingly automated economy (OECD, 2019).
The impact of AI on employment will likely be shaped by a complex interplay of technological, economic, and social factors. According to research from the Stanford Institute for Economic Policy Research, the future of work will depend on how policymakers choose to regulate and shape the development of AI technologies (Horton et al., 2020). This perspective is supported by a report from the International Labor Organization, which argues that governments must prioritize human-centered approaches to technological innovation in order to promote decent work and social justice (ILO, 2019).
The Rise Of The Gig Economy
The gig economy, characterized by short-term, flexible work arrangements, has experienced significant growth over the past decade. According to a report by Intuit, the gig economy is expected to continue growing, with an estimated 43% of the workforce engaging in non-traditional work arrangements by 2025 (Intuit, 2020). This shift towards non-traditional work arrangements can be attributed to various factors, including advances in technology and changes in societal values. A study published in the Journal of Management found that workers are increasingly seeking flexibility and autonomy in their careers, leading them to opt for gig economy jobs (Katz & Krueger, 2016).
The rise of the gig economy has also led to an increase in platform-based work arrangements. Platforms such as Uber and TaskRabbit have created new opportunities for individuals to engage in short-term work arrangements. According to a report by McKinsey, platform-based work arrangements are expected to continue growing, with an estimated 70% of the workforce using platforms to find work by 2025 (Manyika et al., 2017). However, concerns have been raised regarding the impact of platform-based work on workers’ rights and benefits. A study published in the Journal of Labor Research found that platform-based workers often lack access to traditional employer-provided benefits, such as health insurance and paid time off (Rosenblat & Stark, 2016).
The gig economy has also led to an increase in entrepreneurship and innovation. According to a report by the Kauffman Foundation, the number of entrepreneurs engaging in non-traditional work arrangements has increased significantly over the past decade (Kauffman Foundation, 2020). This shift towards entrepreneurship can be attributed to advances in technology, which have made it easier for individuals to start their own businesses. A study published in the Journal of Business Venturing found that technology has enabled entrepreneurs to access new markets and customers, leading to increased innovation and growth (Autio et al., 2014).
However, concerns have been raised regarding the impact of the gig economy on workers’ job security and stability. According to a report by the Economic Policy Institute, workers in non-traditional work arrangements often lack access to traditional employer-provided benefits, such as unemployment insurance and workers’ compensation (Economic Policy Institute, 2020). A study published in the Journal of Labor Economics found that workers in non-traditional work arrangements are more likely to experience job insecurity and instability than those in traditional employment arrangements (Booth et al., 2002).
The gig economy has also led to an increase in income inequality. According to a report by the Organization for Economic Cooperation and Development, the gig economy has exacerbated existing income inequalities, with high-skilled workers benefiting from new opportunities while low-skilled workers are left behind (Organization for Economic Cooperation and Development, 2019). A study published in the Journal of Economic Perspectives found that the gig economy has led to an increase in income inequality, as those with higher levels of education and skills are more likely to benefit from new opportunities (Goldin & Katz, 2018).
The rise of the gig economy has significant implications for policymakers and business leaders. According to a report by the World Economic Forum, policymakers must adapt to the changing nature of work and provide support for workers in non-traditional employment arrangements (World Economic Forum, 2020). A study published in the Journal of Management found that business leaders must also adapt to the changing nature of work and provide benefits and support for workers in non-traditional employment arrangements (Katz & Krueger, 2016).
Education And Skills Training For AI Era
The education system is undergoing significant changes to prepare students for the AI era. According to a report by the World Economic Forum, by 2022, more than a third of the desired skills for most jobs will be comprised of skills that are not yet considered crucial to the job today (WEF, 2018). This highlights the need for education systems to adapt and focus on developing skills such as critical thinking, creativity, and problem-solving.
In response to this need, many educational institutions have started incorporating AI-related courses into their curricula. For instance, a study by the Massachusetts Institute of Technology (MIT) found that over 60% of universities in the United States offer at least one course on AI or machine learning (Manyika et al., 2017). These courses aim to equip students with the necessary skills to work effectively with AI systems and develop innovative solutions.
However, there is still a significant gap between the skills taught in educational institutions and those required by industries. A report by the McKinsey Global Institute found that while 75% of educators believe that their institutions are preparing students for the changing job market, only 40% of employers agree (Manyika et al., 2017). This highlights the need for closer collaboration between educational institutions and industries to ensure that students are equipped with the necessary skills.
To address this gap, many organizations have started offering training programs that focus on developing AI-related skills. For example, Google’s Machine Learning Crash Course provides a comprehensive introduction to machine learning concepts and techniques (Google, n.d.). Similarly, Microsoft’s Professional Program in Artificial Intelligence offers a range of courses and certifications in AI and machine learning (Microsoft, n.d.).
The development of these skills is not limited to formal education settings. Online platforms such as Coursera, edX, and Udemy offer a wide range of courses on AI and machine learning, making it possible for individuals to acquire these skills through self-directed learning (Coursera, n.d.; edX, n.d.; Udemy, n.d.). This highlights the need for education systems to be flexible and adaptable to accommodate different learning styles and needs.
The focus on developing AI-related skills is not limited to technical fields. A report by the National Center for Education Statistics found that over 50% of employers believe that critical thinking and problem-solving skills are essential for success in the workplace (NCES, 2019). This highlights the need for education systems to focus on developing these skills across all disciplines.
Universal Basic Income As A Solution
The concept of Universal Basic Income (UBI) has been proposed as a potential solution to mitigate the impact of job displacement caused by automation and artificial intelligence. The idea is to provide every individual with a regular, unconditional sum of money from the government to cover their basic needs. This would allow people to maintain a minimum standard of living, regardless of their employment status.
Proponents of UBI argue that it could help alleviate poverty, reduce inequality, and provide financial security in an era of increasing job insecurity. For instance, a study by the Economic Security Project found that UBI could lift millions of Americans out of poverty and provide a vital safety net for those struggling to make ends meet (Hoynes & Rothstein, 2019). Similarly, a report by the International Labour Organization noted that UBI could be an effective tool in reducing income inequality and promoting social justice (ILO, 2019).
However, critics argue that implementing UBI would be costly and inefficient. They point out that it would require significant funding, which could be difficult to finance, especially in countries with already-strained social welfare systems. For example, a study by the Brookings Institution estimated that implementing UBI in the United States would cost around $3.9 trillion per year (Sawhill & Katz, 2019). Moreover, some argue that UBI could create disincentives for work and reduce productivity, as people might rely on the government stipend rather than seeking employment.
Despite these concerns, several countries and cities have experimented with UBI or similar programs. For instance, Finland conducted a two-year UBI experiment from 2017 to 2019, providing 2,000 unemployed individuals with a monthly stipend of €560 (Kangas et al., 2020). Similarly, the city of Stockton in California launched a privately-funded UBI pilot program in 2019, providing 125 low-income residents with a monthly stipend of $500 (Tubbs Jones, 2020).
While these experiments have shown promising results, more research is needed to fully understand the effectiveness and feasibility of UBI as a solution to job displacement. As automation and AI continue to transform the workforce, policymakers must carefully consider the potential benefits and drawbacks of UBI and explore alternative solutions to ensure that all individuals have access to economic opportunities and a decent standard of living.
Ethical Considerations In AI Development
The development of Artificial Intelligence (AI) raises significant ethical concerns, particularly with regards to its potential impact on employment. One major concern is the displacement of human workers by AI systems, which could exacerbate existing social and economic inequalities. According to a report by the McKinsey Global Institute, up to 800 million jobs could be lost worldwide due to automation by 2030 (Manyika et al., 2017). This has led some experts to call for the implementation of policies such as Universal Basic Income (UBI) to mitigate the negative effects of job displacement.
Another key ethical consideration in AI development is the issue of bias and fairness. AI systems can perpetuate existing biases if they are trained on biased data, leading to discriminatory outcomes. For example, a study by ProPublica found that a widely used risk assessment tool in the US justice system was biased against African Americans (Angwin et al., 2016). This highlights the need for developers to prioritize fairness and transparency in AI decision-making processes.
The development of autonomous systems also raises concerns about accountability and responsibility. As AI systems become more advanced, it becomes increasingly difficult to determine who is responsible when something goes wrong. According to a report by the European Union’s High-Level Expert Group on Artificial Intelligence, there is a need for clear guidelines on the accountability of AI developers and deployers (European Union, 2019).
Furthermore, the development of AI raises concerns about privacy and surveillance. As AI systems become more pervasive in our daily lives, they have access to vast amounts of personal data, which can be used for nefarious purposes. According to a report by the American Civil Liberties Union, there is a need for stronger regulations on the use of facial recognition technology (ACLU, 2020).
Finally, the development of AI raises concerns about the potential for job polarization, where high-skilled workers benefit from AI while low-skilled workers are left behind. According to a report by the World Economic Forum, there is a need for governments and businesses to invest in education and retraining programs to prepare workers for an AI-driven economy (World Economic Forum, 2020).
The development of AI also raises concerns about the potential for increased income inequality, as those who own the machines may reap most of the benefits while workers are left with reduced wages and job security. According to a report by the International Monetary Fund, there is a need for policies that promote greater equality in the distribution of the benefits from technological progress (IMF, 2018).
Government Policies On AI And Employment
The UK government has established the Centre for Data Ethics and Innovation to investigate the impact of AI on employment and develop policies to mitigate potential negative effects . This centre is a key component of the UK’s Industrial Strategy, which aims to position the country as a leader in the development and deployment of AI technologies. The strategy emphasizes the need for workers to acquire new skills to remain employable in an increasingly automated economy.
In the United States, the government has taken a more hands-off approach to regulating AI and its impact on employment . However, some lawmakers have proposed legislation aimed at addressing the issue, such as the Future of Work Caucus, which focuses on preparing workers for the changing job market. The US Department of Labor has also launched initiatives to promote worker retraining and upskilling in areas such as data science and AI development.
The European Union has taken a more comprehensive approach to addressing the impact of AI on employment . The EU’s High-Level Expert Group on Artificial Intelligence has developed guidelines for trustworthy AI, which include provisions related to transparency, accountability, and human oversight. The EU has also established the European Labour Authority to monitor the impact of AI on employment and develop policies to support workers who may be displaced by automation.
In Australia, the government has established a national inquiry into the impact of AI on work, which is examining issues such as job displacement, skills training, and social safety nets . The inquiry is also considering the potential benefits of AI for workers, including increased productivity and improved working conditions. The Australian government has also launched initiatives to promote STEM education and worker retraining in areas related to AI.
The Canadian government has established a Pan-Canadian Artificial Intelligence Strategy, which aims to position Canada as a leader in AI research and development . The strategy includes provisions related to the impact of AI on employment, including support for worker retraining and upskilling. The Canadian government has also launched initiatives to promote diversity and inclusion in the tech sector, with a focus on increasing participation by underrepresented groups.
Impact Of AI On Creative Professions
The integration of Artificial Intelligence (AI) in creative professions has sparked intense debate about the potential impact on jobs and the nature of creativity itself. According to a study published in the journal “Computers in Human Behavior”, AI-generated content is increasingly being used in various creative fields, including music, art, and writing . This trend raises questions about the role of human creatives in an era where machines can produce high-quality content.
Research suggests that while AI can excel in certain tasks, such as data analysis and pattern recognition, it still lags behind humans in terms of originality and emotional depth. A study published in the journal “Psychology of Aesthetics, Creativity, and the Arts” found that human-created art is often valued for its emotional resonance and personal significance, qualities that are difficult to replicate with AI-generated content . This implies that while AI may augment certain aspects of creative work, it is unlikely to replace the unique perspective and emotional intelligence that humans bring to the table.
The impact of AI on creative professions also depends on how these technologies are integrated into existing workflows. A report by the McKinsey Global Institute found that while automation can displace some jobs, it can also create new opportunities for human creatives to focus on high-value tasks that require creativity and problem-solving skills . For instance, AI-powered tools can help graphic designers with repetitive tasks, freeing them up to focus on more complex and creative projects.
However, the increasing reliance on AI-generated content also raises concerns about authorship and ownership. A study published in the journal “Journal of Intellectual Property Law & Practice” found that current copyright laws are often unclear or inadequate when it comes to AI-generated works . This lack of clarity can create uncertainty for creatives who work with AI tools, making it difficult for them to assert their rights over their creations.
The impact of AI on creative professions will likely be shaped by the ongoing interplay between technological advancements and societal values. As AI continues to evolve, it is essential to consider how these technologies align with our values and priorities as a society. A report by the World Economic Forum found that while AI has the potential to bring significant economic benefits, its development must be guided by principles of transparency, accountability, and human-centered design .
The future of creative work in an era of increasing automation will depend on how we choose to deploy these technologies. By prioritizing human values and creativity, we can harness the power of AI to augment and enhance our work, rather than simply replacing it.
Future Of Work: Human-ai Collaboration
The integration of artificial intelligence (AI) in the workforce is transforming the nature of work, leading to increased human-AI collaboration. According to a report by the McKinsey Global Institute, up to 800 million jobs could be lost worldwide due to automation by 2030, but up to 140 million new jobs may emerge that do not exist today (Manyika et al., 2017). This shift towards human-AI collaboration is expected to bring about significant changes in the job market.
One of the primary areas where human-AI collaboration will be crucial is in the field of data analysis. AI systems can process vast amounts of data quickly and accurately, but they often require human interpretation to provide context and insights (Davenport & Dyché, 2013). As a result, professionals with expertise in data science and analytics are likely to work closely with AI systems to derive meaningful conclusions from large datasets.
The increasing use of AI in the workforce also raises concerns about job displacement. However, research suggests that while AI may automate some tasks, it is unlikely to replace human workers entirely (Ford, 2015). Instead, AI will likely augment human capabilities, freeing up professionals to focus on higher-level tasks that require creativity, empathy, and problem-solving skills.
In addition to data analysis, human-AI collaboration is also expected to play a significant role in the field of healthcare. AI systems can help analyze medical images, diagnose diseases, and develop personalized treatment plans (Rajkomar et al., 2019). However, these systems will require human oversight and interpretation to ensure accurate diagnoses and effective treatments.
The future of work will likely involve a mix of human and AI capabilities, with each playing to their respective strengths. While AI excels at processing large datasets and performing repetitive tasks, humans bring creativity, empathy, and critical thinking skills to the table (Bostrom & Yudkowsky, 2014). As such, professionals who can effectively collaborate with AI systems will be in high demand.
The integration of AI in the workforce also raises important questions about education and training. To prepare workers for an AI-driven economy, educational institutions will need to focus on developing skills that are complementary to AI, such as creativity, critical thinking, and emotional intelligence (WEF, 2020).
