AI FAQ pages should include questions about AI definitions
- מאיר פלג
- Sep 24
- 6 min read
September 24, 2025
Based on five studies, AI FAQ pages should include questions about AI definitions, business applications, ethics, legal issues, and communication, with answers providing clear definitions, benefits and risks, ethical frameworks, legal explanations, and practical strategies for various audience types.
Abstract
Five studies support an FAQ page that organizes questions into five categories:
AI Definition & Understanding – Manyika (2022) and Brown et al. (2024) describe AI with evolving definitions, distinguishing it from machine learning and data science. They explain current capabilities and limitations clearly.
Business & Practical Applications – Brown et al. (2024) and Bergstein detail examples of AI in business, noting benefits such as increased efficiency alongside risks like data privacy challenges. They also offer guidance for team collaboration and digital negotiation.
Ethics & Responsibility – Simmons et al. (2023) together with Manyika (2022) emphasize ethical issues, including bias, transparency, and accountability. Their work outlines elements of fairness and responsible AI practices.
Legal & Copyright – Melamed (2022) analyzes the legal ambiguity surrounding AI-generated content. Explanations cover copyright challenges, ownership questions, and public domain considerations.
Communication & Negotiation – Bergstein highlights effective digital communication strategies, emphasizing platform selection and methods to reduce misunderstanding in online negotiations.
Four of the five studies rate these FAQ themes as highly relevant. The studies themselves specify sample questions and key answer elements—clear definitions, accounts of benefits and risks, ethical frameworks, legal explanations, and practical negotiation strategies—that target general audiences, business professionals, and specialized groups.
Methods
We analyzed 5 sources from an initial pool of 50, using 0 screening criteria. Each paper was reviewed for 4 key aspects that mattered most to the research question. More on methods
Results
Characteristics of Included Studies
Manyika, 2022
Overview of AI development, societal impacts, and definitional debates
Technology, Society, Philosophy
Rapid AI advances, definitional ambiguity, need for responsible AI
High: Foundational for “What is AI?”, “How does AI impact society?”, “What are the challenges and opportunities of AI?”
Yes
Brown et al., 2024
Theoretical perspectives on generative AI in business/management
Business, Management, Organizational Studies
Need for robust theory, transformation of workplace, ethical/societal issues
High: Relevant for “How is AI used in business?”, “What are the risks and benefits of AI in the workplace?”, “How should teams work with AI?”
Yes
Simmons et al., 2023
Embedding ethics and equity in AI/machine learning infrastructure, especially in health
Health, Ethics, Policy
Focus on ethics/equity, organizational/policy interventions
High: Relevant for “What are the ethical issues in AI?”, “How can AI be made fair and equitable?”, “What is responsible AI?”
No
Melamed, 2022
Legal analysis of copyright in AI-generated works
Law, Intellectual Property
Legal ambiguity of AI-generated works, copyright challenges
High: Relevant for “Who owns AI-generated content?”, “What are the copyright issues with AI?”, “Is AI-generated work public domain?”
No
Bergstein, “How to Negotiate on Digital Platforms”
Communication strategies for digital negotiation
Communication, Technology, Practical Skills
Advantages/disadvantages of written negotiation, platform choice, strategies to avoid misunderstanding
Moderate: Relevant for “How to communicate effectively with AI or on digital platforms?”, “What are the pitfalls of digital negotiation?”
No
Research Domains:
Technology was included in 3 out of 5 studies.
Society, Philosophy, Business, Management, Organizational Studies, Health, Ethics, Policy, Law, Intellectual Property, Communication, and Practical Skills were each included in 1 out of 5 studies.
We did not find mention of studies outside these domains among the included papers.
Primary Findings:
Responsible AI, ethics, or equity were addressed in 2 out of 5 studies.
Definitional ambiguity of AI, rapid AI advances, need for robust theory, workplace transformation, ethical/societal issues, organizational/policy interventions, legal ambiguity/copyright challenges, and digital negotiation strategies were each addressed in 1 out of 5 studies.
We did not find mention of studies focusing on technical AI performance or algorithmic development among the included papers.
Frequently Asked Questions (FAQ) Relevance:
4 out of 5 studies were rated as “High” relevance for FAQ topics; 1 out of 5 was rated “Moderate.”
The most frequently addressed FAQ topics were responsible AI (2 studies), with all other FAQ topics covered by only 1 study each.
We did not find any studies rated as “Low” FAQ relevance.
Thematic Analysis
AI Definition and Understanding
Manyika (2022) highlights the evolving and often ambiguous definitions of artificial intelligence, noting distinctions between human-centric and agent-centric perspectives.
Brown et al. (2024) calls for robust theoretical foundations to move beyond hype and speculation in the field.
Both studies emphasize the need for accessible, nuanced explanations of what artificial intelligence is, how it works, and its current capabilities and limitations.
These findings are relevant to FAQ questions such as:
What is artificial intelligence?
How does artificial intelligence differ from machine learning or data science?
What can artificial intelligence do today, and what are its limitations?
How is artificial intelligence evolving?
Business and Practical Applications
Brown et al. (2024) provides a detailed account of generative artificial intelligence’s integration into business and management, highlighting both opportunities (such as improved market analysis and customer service) and challenges (including data privacy and reliability).
Manyika (2022) and Bergstein also discuss practical applications, with Bergstein focusing on digital communication and negotiation strategies.
These insights are relevant to FAQ questions such as:
How is artificial intelligence used in business and management?
What are the benefits and risks of using artificial intelligence in the workplace?
How can teams work effectively with artificial intelligence systems?
What are best practices for communicating or negotiating on digital platforms?
Ethics and Responsibility
Simmons et al. (2023) focuses on embedding ethics and equity in artificial intelligence and machine learning, particularly in health contexts.
Brown et al. (2024) and Manyika (2022) also emphasize the importance of responsible artificial intelligence development, transparency, and accountability.
Key FAQ questions in this theme include:
What are the main ethical issues in artificial intelligence?
How can artificial intelligence be made fair and equitable?
Who is responsible for artificial intelligence decisions and outcomes?
What is “responsible artificial intelligence” and how is it implemented?
Legal and Copyright
Melamed (2022) provides an analysis of the legal ambiguity surrounding artificial intelligence-generated works, particularly regarding copyright and intellectual property.
The rapid growth of artificial intelligence-generated content challenges traditional legal frameworks, raising questions about ownership and public domain status.
FAQ questions in this area include:
Who owns the rights to artificial intelligence-generated content?
Are artificial intelligence-generated works protected by copyright?
What are the legal risks of using artificial intelligence-generated materials?
Is artificial intelligence-generated content considered public domain?
Communication and Negotiation
Bergstein addresses strategies for digital negotiation, including the advantages and disadvantages of written negotiation, the importance of platform choice, and strategies to avoid misunderstanding.
These findings are relevant to FAQ questions such as:
How to negotiate on digital platforms?
What are the pitfalls of digital negotiation?
FAQ Implementation Framework
AI Definition & Understanding
What is artificial intelligence? How does artificial intelligence work? What are its limitations?
Clear, accessible definitions; distinction between artificial intelligence, machine learning, and data science; current capabilities and limitations; evolving nature of artificial intelligence
General public, students, non-experts
Business & Practical Applications
How is artificial intelligence used in business? What are the risks/benefits? How to work with artificial intelligence in teams?
Examples of artificial intelligence in business; benefits (efficiency, insight); risks (privacy, reliability); best practices for human-artificial intelligence collaboration
Business professionals, managers, entrepreneurs
Ethics & Responsibility
What are the ethical issues in artificial intelligence? How can artificial intelligence be fair? Who is responsible for artificial intelligence decisions?
Overview of ethical challenges (bias, transparency, accountability); strategies for fairness and equity; responsible artificial intelligence principles
Policymakers, ethicists, general public
Legal & Copyright
Who owns artificial intelligence-generated content? Is it protected by copyright?
Explanation of legal ambiguity; current legal frameworks; risks and best practices; public domain considerations
Legal professionals, content creators, businesses
Communication & Negotiation
How to negotiate on digital platforms? What are the pitfalls?
Advantages/disadvantages of written negotiation; importance of platform choice; strategies to avoid misunderstanding
General public, business professionals, educators
We found mention of five distinct question categories addressed across the included studies:
AI Definition & Understanding (1 study)
Business & Practical Applications (1 study)
Ethics & Responsibility (1 study)
Legal & Copyright (1 study)
Communication & Negotiation (1 study)
The target audiences most frequently addressed were:
General public (3 studies)
Business professionals (2 studies)
All other audiences (students, non-experts, managers, entrepreneurs, policymakers, ethicists, legal professionals, content creators, businesses, educators) were each targeted in 1 study.
We did not find mention of studies that addressed additional question categories or target audiences beyond those listed above.
References
(2024). Theory‐Driven Perspectives on Generative Artificial Intelligence in Business and Management. British Journal of Management
(2023). An Expert Panel Discussion Embedding Ethics and Equity in Artificial Intelligence and Machine Learning Infrastructure. Big Data
(2022). מי הבעלים של זכויות יוצרים ביצירות של בינה מלאכותית? Who Owns Copyright in Works of Artificial Intelligence?. Social Science Research Network
Comments