Digital and Workplace Literacies are Interrelated Skills

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Employers require employees to have vital digital and workplace literacy skills, and they expect new employees to have developed these skills before being hired. Beyond basic computer skills, digital literacy encompasses using digital tools and technologies effectively. Christian (2022) indicates that it “means being able to work adaptably and strategically across tools, devices, and platforms” (para. 1). Individuals who lack these skills are in danger of being left behind.

 

This blog post (a) provides definitions of digital and workplace literacies, (b) explains how digitalization has permeated employment settings and activities, and (c) then describes how digital and workplace literacies are interrelated.  

 

Digital literacy means having the skills you need to live, learn, and work in a society where communication and access to information is increasingly mediated through digital technologies like internet platforms, social media, and mobile devices. Digital literacy involves a “spectrum of skills that run from operational (device use) to navigational (understanding structures) to informational (searching and interpreting) to strategic (meeting personally meaningful objectives)” (Oliver & Williams-Duncan, 2019, p. 123). Given the pervasiveness of digital tools in daily life, digital literacy has become a vital skill for seeking and maintaining employment.

 

Workplace literacy can be defined as skills employees need to be competent in work.

Given the twin industrial revolutions of digitalization and artificial intelligence, workplace literacy inextricably involves the skills needed to use digital tools efficiently to perform tasks, communicate effectively, and collaborate with colleagues. It also means that employees must be adaptable and willing to learn new skills. Digital and workplace literacies are closely intertwined in today's digital age, where technology permeates almost every aspect of work environments. Understanding their relationship helps individuals and organizations navigate the demands of modern workplaces effectively.

 

Digitalization and Artificial Intelligence in the Workplace

We are amid what has been called a fourth industrial revolution, referring to the digitalization of the workplace and the proliferation of artificial intelligence tools (Briggs et al., 2023). Digitization and digitalization may seem like interchangeable terms; however, digitization focuses on converting information into data, while digitalization is about developing processes and changing workflows to improve manual systems (Monton, 2022). Muro et al. (2017) state that “Digitalization is the process of employing digital technologies and information to transform business operations” (p. 5), a conversion so thorough that it continuously reorients work and daily life.  Muro et al. indicate that over the past 50 years, digital technologies are pervasive in most businesses and workplaces.

 

Digitalization is changing the skills needed to access economic opportunity. It has permeated most industries. For example, healthcare workers at all levels, from janitorial services to highly skilled surgeons to upper management, use digital technologies. Auto mechanics use laptops to diagnose car problems, and salespeople use cloud-based artificial intelligence applications to schedule meetings. These are only a few examples of how the workplace has changed.  Knowing how to use these digital and artificial intelligence tools has become a workforce requirement.

 

Employees and business owners must use multiple digital tools such as email, word processors, spreadsheets, project management software, and specialized industry-specific applications. Klassen (2019) reports that workers report the need to use multiple digital tools simultaneously and are often assigned multiple computer monitors so that they can organize digital tools to suit their needs. Employees report experiencing anxiety and information overload. Employees with lower literacy need help with non-linear reading tasks and may find the volume of information overwhelming.

Image credit: Tranmautritan, Pexels

Digital and Workplace Literacies

The influence of digitalization on work in our society has led to emergence of digital jobs. Therefore, digital and workplace literacies are interrelated. Seven skill categories for the digital workplace are suggested below.

 

1.     Information Management

Digital literacy includes skills related to finding, evaluating, and managing information online. In the workplace, employees need to be able to navigate through vast amounts of digital information to find relevant data for their tasks, projects, or decision-making processes. Workplace literacy involves understanding how to assess the credibility of online sources, manage digital files, and organize information effectively to enhance productivity and decision-making (Law et al., 2017; Vuorikari Rina, 2022).

 

2.     Communication and Collaboration

Digital literacy is essential for effective communication and collaboration in the workplace. This includes using email, instant messaging, video conferencing, and other digital communication tools to interact with colleagues, clients, and stakeholders. Workplace literacy extends beyond basic communication skills to encompass understanding digital etiquette, managing online meetings, and leveraging collaborative teamwork and knowledge-sharing platforms (Law et al., 2017; Vuorikari Rina, 2022).

 

3.     Problem-Solving

Digital literacy fosters problem-solving skills and adaptability in the workplace. Employees need to be able to troubleshoot technical issues, learn new digital tools and technologies quickly, and adapt to changing digital environments. Workplace literacy involves developing critical thinking skills to analyze problems, identify solutions, and leverage digital resources effectively to achieve organizational goals (Law et al., 2017; Vuorikari Rina, 2022).

 

4.     Data-Informed Decision Making

In today's data-driven workplaces, digital literacy includes understanding basic data concepts, interpreting data visualizations, and making data-informed decisions. Workplace literacy involves applying data analysis skills to extract insights from large datasets, create reports and presentations, and communicate findings to relevant stakeholders (Law et al., 2017; Vuorikari Rina, 2022).

 

5.     Cybersecurity and Privacy Management

Digital literacy encompasses knowledge of cybersecurity best practices and understanding privacy issues related to digital technologies. In the workplace, employees must be aware of cybersecurity threats such as phishing attacks, malware, and data breaches, and understand how to protect sensitive information and adhere to privacy regulations (Law et al., 2017; Vuorikari Rina, 2022).

 

6.     Artificial Intelligence Usage

AI technologies are becoming pervasive in many digital tools and platforms that individuals interact with daily. Understanding AI is thus becoming a crucial component of digital literacy. Users must comprehend how AI works, its capabilities, limitations, and ethical considerations to make informed decisions about its use.

 

7.    Lifelong Learning and Professional Development

Digital and workplace literacies are not static skills but require continuous learning and professional development. Employees must stay updated with evolving digital trends, acquire new digital skills, and adapt to emerging technologies to remain competitive (Law et al., 2017).

 

In summary, digital and workplace literacies are deeply intertwined, with digital skills playing a crucial role in enhancing productivity, communication, problem-solving, and adaptability in modern work environments. Organizations can foster a culture of continuous learning and provide training and development opportunities to empower employees with the digital and workplace literacy skills needed to succeed in today's digital economy.

 

References

Briggs, X. D., Johnson, C. C., & Katz, B. (2023, October 13). There’s an industrial revolution underway. Unless we act, it will make the racial wealth gap even worse. Metropolitan Policy Program at Brookings. Retrieved https://www.brookings.edu/articles/theres-an-industrial-revolution-underway-unless-we-act-it-will-make-the-racial-wealth-gap-even-worse/

Christian, A. (2022, September 26). Why ‘digital literacy’ is now a workplace non-negotiable. BBC. Retrieved https://www.bbc.com/worklife/article/20220923-why-digital-literacy-is-now-a-workplace-non-negotiable

Klassen, A. (2019). Deconstructing paper-lined cubicles: Digital literacy and information technology resources in the workplace. International Journal of Advanced Corporate Learning, 12(3), 5–13. https://doi-org.lynx.lib.usm.edu/10.3991/ijac.v12i3.11170

Law, N. W. Y., Woo, D. J., de la Torre, J., & Wong, K. W. G. (2018). A global framework of reference on digital literacy skills for indicator 4.4. 2.

Monton, A. (2022, March 2022). Difference and similarities: Digitization, digitalization, and digital transformation. Retrieved https://www.globalsign.com/en-sg/blog/difference-and-similarities-digitization-digitalization-and-digital-transformation#:~:text=While%20digitization%20focuses%20on%20converting,generate%20insights%20from%20their%20behaviour.

Muro, M., Liu, S., Whiton, J., & Kulkarni (2017, November). Digitalization and the American workforce. Metropolitan Policy Program at Brookings. Retrieved https://www.brookings.edu/wp-content/uploads/2017/11/mpp_2017nov15_digitalization_full_report.pdf

Oliver, K. M., & Williams-Duncan, S. (2019). Faith leaders developing digital literacies: Demands and resources across career stages according to theological educators. Journal of Media Literacy Education, 11(2), 122–145. https://doi-org/10.23860/JMLE-2019-11-2-7

Vuorikari Rina, R., Kluzer, S., & Punie, Y. (2022). DigComp 2.2: The Digital Competence Framework for Citizens-With new examples of knowledge, skills and attitudes (No. JRC128415). Joint Research Centre (Seville site).

Making Sense of Complexity: Typologies of Artificial Intelligence

 

Image Credit: Microsoft Stock Images

    

Artificial intelligence (AI) influences many aspects of modern life and has multiple applications. AI is the ability of machines or software to perform tasks that are commonly associated with human intelligence, such as recognizing patterns, making decisions, or learning from data. AI is designed to mimic human capabilities, including pattern recognition, data analysis, and decision-making, and to perform tasks rapidly and efficiently.

 

Algorithms are a set of problem-solving steps computer programs use to accomplish tasks. AI operationalizes the algorithmic steps in smart machines that perform tasks usually associated with human intelligence such as “learning, adapting, synthesizing, self-correction, and use of data for complex processing” (Popenici & Kerr, 2017, para. 3). Machine learning is an application of AI in which large data sets are analyzed, without direct instruction, to detect patterns that might elude human beings. Generative AI is an artificial intelligence technology that can produce various types of content, including text, imagery, audio, and synthetic data.

 

AI originated in the 20^th century, but only recently have computers had the computational power to make it practical and useful (Anyoha, 2017). Most people are using AI without recognition because AI powers internet search platforms, predictive text, grammar- and spell-check, GPS, social media curation, smart devices, streaming services, and patient portals. Many people conflate generative AI with large-language models such as those used within ChatGPT, but this is only one type of AI.

 

Typologies of AI

It is essential to recognize that AI is multifaceted and has multiple applications. Therefore, it can be categorized in multiple ways: based on capability, functionality, application, or degree of supervision vs. autonomy.

 

Capability

One capability-based categorization is weak and strong or general AI. Narrow or weak AI can perform single-specific tasks such as making Netflix recommendations, facial recognition, self-driving cars, searching the internet, or translating languages. General or Strong AI can perform tasks in a human-like manner (AVContent Team, 2023). Some descriptions differentiate general AI from strong AI, with the former referring to a computer that is as smart as a human in a general sense and the latter referring to computers that have achieved human consciousness. The latter category is still somewhat theoretical because AI has not yet achieved consciousness or self-awareness. This counteracts the idea that sentient robots will take over the world and enslave humans, as many science fiction novels and films would have people believe. Think of the Terminator or 2001: A Space Odyssey. 

 

Another capability-based typology characterizes four levels of AI: (a) reactive, (b) limited memory, (c) theory of mind, and (d) self-awareness. Consistent with the weak vs strong typology, this conceptualization indicates that AI has not yet achieved theory of mind, meaning the capacity “to understand and remember other entities' emotions and needs and adjust their behavior based on these. This capability is like humans in social interaction” (Arya, 2023, para. 13). Humans develop this capacity as they mature. They also develop self-awareness and emotional intelligence, while AI does not.

Four Levels of AI

Functionality

One functionality categorization scheme asserts three categories of AI: (a) large language models (LLM), (b) learning analytics in which personalized learning is tailored for individuals, and (c) big data, meaning using large data sets to conduct comparative analysis between groups of people. These can be expressed in input and output, instructor and student, or data and functions.

Another functionality schematic suggests the following categories:

·       Analytic AI scans large datasets to identify, interpret, and communicate meaningful patterns of data.

·       Functional AI scans huge amounts of data to take actions.

·       Interactive AI automates communication without compromising on interactivity.

·       Text AI uses semantic search and natural language processing to build semantic maps and recognize synonyms to understand the context of user’s question.

·       Visual AI identifies, recognizes, classifies, and sorts objects or converts images and videos into insights. (Sarker et al., 2022).

Application

Yet another way of categorizing AI is by applications in which it is used. For example, expert systems use information collected from recognized domain experts to facilitate fast decision-making. Natural language processing (NLP) enables AI to use language in a human-like manner in chatbots, language translation, and sentiment analysis, which is used to determine whether the emotional tone of a message is positive, negative, or neutral. Sentiment analysis has become an important business function used to improve customer service, market research, and to monitor brand performance. It can distinguish the positive from the negative of a seemingly contradictory sentence such as: “While I liked this product, I was disappointed with the color.”

Supervision vs. Autonomy

This typology is often used to describe the process of machine learning, in which:

·       Supervised learning—all data are labeled.

·       Semi-supervised—some input data are labeled, while some are not.

·       Unsupervised—all input data are unlabeled (Alloghani et al., 2020).

 

These terms can also be used to describe AI. Examples of supervised processing include virtual assistants such as Siri and Alexa, while an example of unsupervised or autonomous processing is self-driving cars.

You may notice overlaps between the different typologies, as the following concept map clarifies.

No matter how you conceptualize it, the field of AI is complex, growing, and rapidly being integrated into multiple fields of professional practice. These typologies highlight the diverse nature of AI, and the various systems designed for specific purposes and possessing different levels of capabilities. The field of AI continues to advance and new typologies may be developed as its capacities evolve.

 

References

Alloghani, M., Al-Jumeily, D., Mustafina, J., Hussain, A., Aljaaf, A.J. (2020). A systematic review on supervised and unsupervised machine learning algorithms for data science. In M. Berry, A. Mohamed, & B. Yap (Eds.), Supervised and unsupervised learning for data science. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-22475-2_1

 Anyoha, R. (2017, August 28). The history of artificial intelligence. Harvard University. Retrieved https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/

 Arya, N. (2023, November 16). Theory of mind AI in artificial intelligence. Ejable. Retrieved from https://www.ejable.com/tech-corner/ai-machine-learning-and-deep-learning/theory-of-mind-ai-in-artificial-intelligence/#:~:text=Theory%20of%20Mind%3A%20This%20is,like%20humans%20in%20social%20interaction.

AVContent Team (2023, September 14). Weak AI vs strong AI: Exploring key differences and future potential of AI. Analytics Vidhya. Retrieved https://www.analyticsvidhya.com/blog/2023/04/weak-ai-vs-strong-ai/

 Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning,12, 22. https://doi.org/10.1186/s41039-017-0062-8

 Sarker, I.H.  (2022). AI-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science,. 3, 158. https://doi.org/10.1007/s42979-022-01043-x

 

President Biden Issues Executive Order to Establish Standards for AI Technologies

 

By Lilian H. Hill

On October 30, 2023, President Biden issued an executive order to establish Artificial Intelligence (AI) safety and security standards, protect Americans’ privacy and security, advance equity and civil rights, and advocate for consumers and workers. It employs broad emergency powers, usually only invoked for urgent situations such as the coronavirus pandemic or war, and the power of multiple government agencies to address the risks of artificial intelligence, which Biden described as the “most consequential technology of our time.” Biden has also called on Congress to create legislation to regulate AI as multiple attempts have failed to pass.

 

Released just days before an international AI Safety Summit held in the UK (Zakrzewski et al., 2023), the 111-page Executive Order has seven focus areas: 

• safety
• protection of American’s privacy 
• preventing bias 
• supporting consumers, students, and patients 
• supporting workers 
• promoting innovation 
• advancing American leadership abroad

 

Selected details are described for each focus area below.

 

Safety

 

• AI corporations will be required to conduct safety assessments of their products and submit findings to the federal government before implementing AI technology.
• Safeguards should be in place to shield Americans from AI-facilitated fraud and deception.
• Protocols and best practices will be established to detect AI-generated content and validate official content.
• A sophisticated cybersecurity initiative will be developed to identify and rectify vulnerabilities in critical software.

Protection of Americans Privacy

• Federal backing for expediting the development and application of privacy-preserving methods will be utilized, incorporating the use of cryptographic tools.
• Efforts will be made to strengthen the methods by which federal agencies collect and use commercially available information, alongside privacy guidelines to tackle AI-related risks.

Preventing Bias

• Standards will be formulated to furnish landlords, federal benefits programs, and federal contractors with precise guidelines to prevent AI algorithms from exacerbating discrimination.
• The Department of Justice and federal civil rights personnel will receive training to address algorithmic discrimination and to adopt best practices for investigating and prosecuting AI-related civil rights violations.
• Fairness will be encouraged throughout the criminal justice system by outlining best practices for the use of AI in sentencing, parole and probation, pretrial release and detention, risk assessments, surveillance, crime forecasting and predictive policing, and forensic analysis.

Stand Up for Consumers, Students, and Patients

• The responsible use of AI in healthcare will be promoted, with the Department of Health and Human Services establishing a safety program to receive reports of, and take action against, harms or unsafe healthcare practices involving AI.
• Efforts will be made to shape AI's potential to revolutionize education by creating resources to support educators deploying AI-driven educational tools, such as personalized tutoring in schools.

Promoting Innovation

• AI research across the United States will receive support by initiating the National AI Research Resource pilot program, providing AI researchers and students access to vital AI resources and data and increased grants for AI research in critical areas such as healthcare and climate change.
• An equitable, open, and competitive AI ecosystem will be encouraged by granting small developers and entrepreneurs access to technical aid and resources, assisting small businesses in commercializing AI breakthroughs, and encouraging the Federal Trade Commission to exercise its authority.
• The opportunities for highly skilled immigrants and nonimmigrants with expertise in crucial fields to study, remain, and work in the United States will be expanded by modernizing and streamlining visa criteria, interviews, and reviews.

Supporting Workers

• Principles and best practices will be devised to mitigate the negative impacts and maximize the benefits of AI for workers, addressing job displacement, labor standards, workplace equity, health and safety, and data collection.
• A report will be compiled on AI's potential effects on the labor market, and strategies to bolster federal support for workers facing labor disruptions, including those resulting from AI, will be developed.
• The opportunities for highly skilled immigrants and nonimmigrants with expertise in crucial fields to study, remain, and work in the United States will be expanded by modernizing and streamlining visa criteria, interviews, and reviews.

Advancing American Leadership Abroad

• Strengthen bilateral, multilateral, and multi-stakeholder engagements to collaborate on AI with the State Department, in conjunction with the Commerce Department, leading efforts to establish robust international frameworks for harnessing AI's benefits and managing its risks, ensuring safety.
• Accelerate the development and implementation of crucial AI standards with international partners and in standards organizations, ensuring the technology's safety, security, trustworthiness, and interoperability.
• Advocate for the safe, responsible, and rights-affirming development and deployment of AI globally to tackle global challenges, such as advancing sustainable development and mitigating threats to critical infrastructure.

Ensuring Responsible and Effective Government Use of AI

• Issue guidelines for agencies' use of AI, including clear standards to protect rights and safety, improve AI procurement, and strengthen AI deployment.
• Assist agencies in procuring specified AI products and services more quickly, affordably, and effectively through streamlined contracting processes.
• Expedite the rapid hiring of AI professionals as part of a government-wide AI talent surge led by the Office of Personnel Management, U.S. Digital Service, U.S. Digital Corps, and Presidential Innovation Fellowship. Agencies will provide AI training for employees at all levels in relevant fields.

 

This Executive Order is far-reaching and comprehensive. It builds on and expands the considerations articulated in the White House Blueprint for an AI Bill of Rights. However, news commentary indicates that the Executive Order is only a first step in regulating rapid AI development and implementation, but also indicates that the Executive Order needs to be improved in enforcement mechanisms.

 

References

The White House. (2023, October 30). Fact Sheet: President Biden Issues Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence. Retrieved from https://www.whitehouse.gov/briefing-room/statements-releases/2023/10/30/fact-sheet-president-biden-issues-executive-order-on-safe-secure-and-trustworthy-artificial-intelligence/

The White House Office of Science and Technology Policy (n.d.). Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People. Retrieved https://www.whitehouse.gov/ostp/ai-bill-of-rights/#safe

Zakrzewski, C., Lima, C., & Pager, T. (2023, October 25). White House to unveil sweeping AI executive order next week. Washington Post. Retrieved https://www.washingtonpost.com/technology/2023/10/25/artificial-intelligence-executive-order-biden/?itid=ap_catzakrzewski