6.6 Specific Risks of AI-Generated Content (AIGC)

Between Realism and Fabrication: Beware the Specific Risks of AI-Generated Content (AIGC)

AI Generated Content (AIGC) technology, especially the Generative AI models driving it (like Large Language Models (LLMs), image generation models, audio synthesis models), is undoubtedly one of the forces with the most transformative potential and disruptive influence in the current wave of AI development. The core characteristic of these technologies is that they no longer merely analyze or identify existing information, but can actively and creatively generate entirely new content that is often highly realistic and difficult to distinguish from human creation, covering multiple modalities including text, code, images, audio, video, and even 3D models.

AIGC technology showcases incredible potential and application prospects across numerous fields such as creative industries (art, design, music, visual effects), entertainment & gaming (personalized characters, dynamic narratives), marketing & advertising (automated content production, personalized recommendations), online education & training (intelligent tutoring, virtual labs), and personalized customer service (chatbots, customized product suggestions). Imagine a lawyer obtaining a structurally complete, logically plausible initial draft of a legal memo with just a few sentences describing case facts and legal points; a designer receiving dozens of high-quality design options by simply inputting style and theme keywords; a researcher getting intelligent summaries and preliminary literature reviews by just posing a research question. AIGC is dramatically lowering the barriers and costs for creating various forms of content, producing and disseminating information at unprecedented speed and scale.

However, this seemingly “magical” powerful creativity inevitably comes with a series of unprecedented, unique, and potentially extremely dangerous risks and challenges if misused. Compared to traditional discriminative AI (which mainly analyzes data, classifies, or makes predictions), AIGC directly acts upon the creation, expression, and dissemination of information. The risks it brings more directly concern the foundations of veracity in our society (Veracity), the fundamental bonds of trust between people (Trust), the basic rules of intellectual property systems (Intellectual Property), and the health, stability, and security of the entire information ecosystem.

For the legal industry—which relies heavily on the truthfulness and reliability of information, strives to maintain the objective credibility of evidence, and needs to strictly protect intellectual property and trade secrets—deeply understanding, being highly vigilant about, and proactively addressing these specific, novel risks brought by AIGC is particularly important and urgent. This concerns not only how we effectively utilize this technology but also how we uphold the core values of the legal profession and the rule-of-law foundations of society.

I. Deepfakes: The Trust Crisis and Judicial Challenge of “Seeing Is No Longer Believing”

Deepfakes represent one of the most attention-grabbing and potentially destructive applications of AIGC technology. This term specifically refers to fake or maliciously altered audio, image, and video content created using deep learning techniques (especially advanced generative models like GANs, Diffusion Models, NeRFs, and various Autoencoders) that appear highly realistic, are difficult to distinguish from genuine content, and may even pass initial technical detection.

Their core purpose is usually to make a fabricated persona (often by “grafting” one person’s facial or vocal features onto another’s body, or synthesizing entirely from scratch) appear or sound like they are saying things they never said, or doing things they never did.

• Increasingly Sophisticated and Diverse Technical Forms:

  • Face Swapping: The most widely known early form. AI models learn key features of a source face and seamlessly, dynamically replace the face in a target video, preserving original expressions, head pose, and lighting. High-quality face swaps can be hard to detect with the naked eye, even in motion.
  • Face Reenactment / Lip Sync / Face Puppetry: These techniques precisely control the facial expressions (smiles, frowns), lip movements, even eye gaze and micro head movements of a person in an existing video (or static photo), making them perfectly mimic or match different audio content (e.g., making a historical figure “speak” modern language) or mimic another person’s facial actions in real-time (like a virtual puppet).
  • Voice Cloning & Synthesis: (Principles and risks discussed in Sections 2.6 & 5.6) Using AI models (like Transformer-based TTS) to learn a target person’s vocal characteristics (timbre, pitch, prosody, speed, accent) from just a small amount (sometimes only seconds) of their real voice samples, to generate synthetic speech that sounds almost identical and can say any arbitrary text.
  • Full Body Synthesis & Motion Transfer: Deepfake technology is no longer limited to faces and voices. More advanced models can now generate complete human video figures from scratch, or transfer the full-body movements and posture of one person (e.g., a complex dance routine, a unique gait) onto another (potentially entirely fictional or synthetic) figure, making the motion appear natural and fluid.

• Core Risk: Undermining the Foundation of “Authenticity,” Eroding Social Trust: The most fundamental and alarming danger of deepfake technology is that it directly challenges and could potentially completely undermine the most basic, intuitive means society has long relied upon to judge information authenticity—“seeing is believing” and “hearing is believing.” When video and audio evidence we personally see and hear can be easily and indistinguishably forged or altered, what can we trust? This pervasive doubt about “what we see and hear” will fundamentally erode the basic trust structures between individuals, between citizens and institutions, and even between nations, making social consensus extremely difficult to achieve and drastically increasing the costs of social operation.

• Profound Harm Potential at Legal and Societal Levels:

  • Disinformation Warfare, Political Manipulation & Social Order Disruption: Creating and spreading fake speech videos or recordings of national leaders, key government officials, political candidates, or social influencers can be used to disseminate destructive political rumors, maliciously interfere with election processes and outcomes, incite ethnic hatred or group antagonism, provoke social violence, undermine national security interests, or disrupt normal international relations. This has become a significant tool in new forms of information warfare and political interference.
  • Reputational Damage, Defamation, Blackmail & Novel Cybercrimes:
    • Catastrophic Misuse for Non-Consensual Intimate Imagery (NCII): Maliciously synthesizing victims’ facial images (especially women and minors, who are disproportionately targeted) onto pornographic videos or pictures, then widely distributing them online for public humiliation, cyberbullying, blackmail, or revenge. This is one of the most nefarious, widespread, and severely damaging misuses of deepfake technology, causing devastating and lasting psychological trauma and social harm to victims. This demands the strictest condemnation and legal crackdown.
    • Forging Inappropriate Behavior or False Statements: Creating audio/video clips showing target individuals (public figures, executives, competitors, ordinary citizens) making inappropriate or extremist remarks, confessing to fictional crimes or unethical acts, or engaging in other reputation-damaging behaviors, used for defamation, slander, character assassination, or unfair attacks in business competition or workplace conflicts.
  • New Means for Financial Fraud, Corporate Espionage & Economic Crime: Attackers might use highly realistic voice cloning or video synthesis to impersonate corporate executives, CFOs, key clients, or partners to:
    • Issue fraudulent instructions for large fund transfers or payments to finance departments or banks.
    • Defraud investors or financial institutions into providing loans or investments.
    • Impersonate insiders to steal trade secrets or conduct insider trading.
    • Release fake financial reports, earnings forecasts, or major deal information to maliciously manipulate stock markets or harm competitors.
  • Fundamental Threat to Evidence Integrity & Judicial Fairness: This is the most direct and severe challenge for the legal industry. As deepfake technology becomes more accessible and effective, criminals, dishonest litigants, or third parties seeking to interfere with justice could very well use these techniques to forge or tamper with crucial video or audio evidence, such as:
    • Fabricating key surveillance footage snippets that support their claims or refute the opponent’s (e.g., creating a fake alibi video, or grafting an innocent person’s face onto real crime scene footage).
    • Forging audio/video recordings of suspects or defendants making false confessions or incriminating statements.
    • Creating fake audio/video recordings of key witnesses providing false testimony to mislead fact-finding.
    • Making imperceptible alterations, splices, or content replacements to genuine phone recordings, meeting recordings, or voice messages. If such technically potentially highly indistinguishable forged or altered evidence is successfully submitted to and admitted by investigative bodies, prosecutors, courts, or arbitration tribunals, it will severely disrupt the accurate determination of case facts, undermine the integrity and fairness of judicial proceedings, and could even directly lead to wrongful convictions or miscarriages of justice, causing immeasurable damage to the rule of law. Effectively detecting and excluding deepfake evidence has become an extremely urgent and critical challenge facing all legislators, judges, forensic experts, and legal practitioners today.

• Exploring Multi-Dimensional Paths to Counter the Deepfake Threat: Addressing the potential menace of deepfakes requires synergistic efforts across technology, law, policy, and education to build multi-layered defenses:

  • Develop More Advanced and Reliable Technical Detection Methods: Continuously invest in R&D for stronger, more accurate detection algorithms and tools that are robust against various novel forgery techniques. These might need to synthesize multiple clues to identify forgery traces, e.g.:
    • Analyzing subtle digital signal artifacts (compression anomalies, audio spectral unnaturalness).
    • Checking for physical world inconsistencies (lighting, shadows, reflections, perspective).
    • Analyzing naturalness of biological signals (facial micro-expression coherence, blinking patterns, head movements, breathing patterns, perhaps even subtle skin color changes due to heartbeat).
    • Cross-modal consistency checks (e.g., precise temporal and semantic match between lip movements and audio).
    • Identifying unique “digital fingerprints” potentially left by different generative models. However, it must be clearly recognized that this is an ongoing “arms race” between forgery and detection technologies. As generative models improve, forgeries become more realistic, making detection harder. Expecting a technology that can detect all deepfakes with 100% accuracy is unrealistic.
  • Explore and Promote Digital Watermarking & Content Provenance Technologies: Actively research and promote techniques that can embed tamper-resistant digital markers into legitimate, authentic AIGC content to prove its origin, authenticity, or modification history. E.g.:
    • Invisible Digital Watermarking.
    • Blockchain-based Content Provenance to immutably record creation/modification history.
    • Industry Standard Coalitions: Support organizations like C2PA (Coalition for Content Provenance and Authenticity) to develop cross-platform, open technical standards for content provenance and authenticity, making verification easier for users and platforms.
  • Accelerate Development of Relevant Legal Regulatory Frameworks: Build upon existing laws to enact or amend specific regulations targeting the misuse of deep synthesis technology. E.g.:
    • Mandatory Labeling Requirements: Require clear, conspicuous labeling for all deep synthesis generated audio/video content that could potentially mislead the public (e.g., China’s regulations already mandate this).
    • Clarify Legal Liability: Further clarify the legal liabilities for creating, disseminating, especially maliciously using deepfakes (particularly when causing harm). This could involve civil tort liability (defamation, infringement of personality rights/privacy), administrative liability (violating cybersecurity or content management rules), and even criminal liability (libel, insult, extortion, fraud, perjury, spreading false information, etc., based on specific acts and consequences).
    • Platform Responsibility: Strengthen legal requirements for internet platforms providing deep synthesis technology or hosting related content regarding content moderation, security safeguards, and cooperation with investigations.
  • Adjust Evidence Rules & Forensic Practices:
    • Heightened Scrutiny Standards: Courts, tribunals, and investigative/prosecutorial bodies need to universally raise the standard of scrutiny and vigilance regarding the authenticity and integrity of all digital audio/video evidence (especially for crucial, questionable, or contested items).
    • Emphasize Technical Forensics: When reasonable doubt arises about authenticity, place greater emphasis on utilizing qualified forensic institutions or technical experts. Develop and standardize forensic techniques and standards specifically for deepfake detection.
    • Consider Adjusting Burden of Proof: Explore possibilities, in specific circumstances, for appropriately adjusting the burden of proof regarding the authenticity of audio/video evidence. E.g., if one party raises specific, evidence-based reasonable doubt about authenticity (e.g., providing preliminary technical analysis showing anomalies), should the proponent of the evidence bear a higher burden to prove its genuineness?
  • Strengthen Public Media Literacy & Digital Risk Education: Through school education, community outreach, media reporting, etc., broadly educate the public (including legal professionals themselves) about deepfake technology, its potential harms, basic identification methods, and preventive awareness. Improving overall societal digital literacy and critical thinking skills regarding suspicious information is a crucial social foundation against the flood of deepfakes.

Maintain Highest Vigilance & Scrutiny for Deepfake Evidence!

As legal professionals on the front lines of the justice system (lawyers, judges, prosecutors, forensic experts, etc.), when encountering and handling any digital audio or video evidence in the future, the question “Could this be a deepfake?” must be considered a significant, potential, even routine risk factor. We must completely abandon the past instinctive trust in “seeing is believing” or “hearing is believing.” For any audio/video evidence from unclear sources, with critical content, depicting bizarre events, or whose authenticity is contested by parties, maintain the highest level of vigilance and the most cautious attitude. When necessary, proactively consider and recommend (if lawyer) or order ex officio (if judge/prosecutor) professional technical forensic examination. Furthermore, regardless of forensic results, always adhere to the fundamental principle of comprehensively cross-validating audio/video evidence against all other evidence in the case (documents, physical evidence, other testimonies, logical consistency, common sense) through meticulous comparative analysis and logical judgment. Never solely or easily rely on any single piece of uncorroborated audio/video evidence.

II. Exponential Proliferation of Disinformation & Misinformation and Governance Dilemmas

AI Generated Content (AIGC) technology, especially the astounding text generation capabilities and increasing accessibility of Large Language Models (LLMs), is dramatically lowering the barriers, costs, and technical difficulty for creating and spreading various forms of Disinformation (intentionally fabricated and disseminated false/distorted information meant to deceive or mislead) and Misinformation (inaccurate or incomplete information spread unintentionally) in ways never seen before. This enables false and misleading information to be produced and diffused at astonishing speed, unprecedented scale, and even in highly personalized ways targeting specific populations, posing severe threats to social trust, public safety, and democratic decision-making.

• Risk Sources & Specific Manifestations:

  • LLM “Hallucinations” Packaged & Spread at Scale with Authority: As discussed, LLMs themselves can generate large amounts of factually incorrect or entirely fabricated content (“hallucinations”) that appears logically sound, fluent, even “well-cited” (with fake sources). If this erroneous AI-generated content is mass-copied and published onto websites, social media, or integrated into other information products (news reports, research papers) by users (individuals or organizations) without rigorous fact-checking, or even gets re-learned and cited by other AI systems (search engines, other LLMs) as “knowledge,” then errors can spread virally and self-reinforce throughout the information ecosystem. This greatly pollutes the authenticity of the information environment, making it extremely difficult for average users and even professionals to discern truth from falsehood.
  • Automated Propaganda, Online Opinion Manipulation & Proliferation of “Intelligent Sock Puppets”: Using AIGC tools (especially LLMs and image/video generators), malicious actors can easily, almost cost-free, and automatically generate massive volumes of social media comments, forum posts, blog articles, news-like reports, fake user reviews, etc., that appear to be from real humans. This massive AI-generated content can be orchestrated for:
    • Sock puppet campaigns: Artificially creating false impressions of public opinion trends, skewed discussion heat, or polarized atmospheres around specific social hot topics, commercial products, or public policies, attempting to influence public perception or decisions.
    • Manipulating public opinion & cognitive warfare: Systematically, mass-disseminating biased or completely false propaganda, conspiracy theories, or smear materials targeting specific political events, social issues, public figures, or institutions for cognitive manipulation.
    • Political propaganda or election interference: During elections, automatically generating and spreading large volumes of personalized campaign materials targeting different voter demographics, potentially containing false promises, smears against opponents, or inflammatory rhetoric, attempting to influence voting behavior and outcomes.
    • Commercial defamation or unfair competition: Anonymously or under false identities, using AI to generate large amounts of fake negative information about competitors’ product quality, service issues, or corporate scandals, spreading it widely to damage rivals and gain market share.
  • Highly Personalized, Hard-to-Detect Precision Fraud Content: AIGC technology can be combined with personal information about potential victims (obtained through various channels, legal or illegal) — e.g., name, occupation, social connections, recent activities, interests — to generate highly customized, convincingly toned, targeted, and deceptive phishing emails, scam text messages, fake official notices, or even personalized voice/video messages (e.g., using voice cloning to impersonate relatives in scams). This “tailor-made” fraudulent content is far harder for ordinary people to detect than traditional mass-distributed scams, significantly increasing the success rate and harm of online fraud.
  • Fabricated News Reports, Altered Historical Narratives & “Deep Reality” Fiction: Using AIGC tools (text, image, video), malicious actors can extremely quickly and cheaply produce fake news reports that are almost indistinguishable in format, style, and even included “on-site photos/videos” from authentic news published by reputable organizations. More worryingly, AIGC could also be used to systematically fabricate, distort, or “rewrite” historical event narratives, creating a kind of “seemingly real but purely fictional” “Alternative Facts” or “Deep Reality,” attempting to blur the line between truth and fiction, manipulating public historical understanding, collective memory, and social identity.
  • Dilution & Pollution of Information Ecosystem by Low-Quality “AI Content Farms”: The traditional “content farm” model, relying on manual or simple scripting for content aggregation and rewriting, gets a massive “intelligent” upgrade in the AIGC era. AI can be used to more efficiently and massively automate the production of vast quantities of “junk content” (Content Pollution) that might be grammatically correct but is content-empty, logically flawed, factually outdated, stitched together from various sources, or even directly contains significant errors or falsehoods. This content, often published en masse just to fill webpages, game search engine rankings (SEO), or trick ad networks for traffic monetization, directly dilutes the concentration of high-quality, original, valuable information online, makes it harder for users to find reliable information, and lowers the overall quality of the entire information ecosystem.

• Profound Impacts on Legal Practice, Social Order & Democratic Governance:

  • Erosion of Foundational Social Trust: The exponential proliferation of false and misleading information will severely weaken public trust in traditional information authorities (news media, government bodies, research institutions, experts, even the justice system itself). When people become confused and skeptical about “what is real,” achieving social consensus becomes extremely difficult, potentially exacerbating social division, polarization, and extremism, undermining social harmony and stability.
  • Drastically Increased Social Cost & Individual Burden of Fact-Checking: Individuals, organizations, and society as a whole will need to invest far more time, effort, technology, and financial resources than ever before to try to discern, verify, track, and counter the incessant flood of hard-to-verify, rapidly spreading false and misleading information. For legal work, which is premised on fact-finding, this means higher investigation costs, greater difficulty in evidence review, and increased challenges in seeking truth amidst the information fog.
  • New Challenges in Attributing Legal Liability for Disinformation: How to accurately define and effectively assign legal responsibility for various harms caused by AI-generated false information (e.g., defamation, commercial loss, emotional distress, public disorder)? Is the responsible party the AI model developer (should they be liable for model “hallucinations” or misuse)? The platform provider offering the AI service (should they bear stricter content moderation and risk control duties)? Or the specific user who generated and disseminated the false information (were they knowing? negligent?)? Existing legal frameworks (e.g., tort law, criminal statutes, cybersecurity laws, e-commerce laws) may face new challenges in specific application to these novel AI-driven cases regarding interpreting elements, establishing causation, proving mental state, and allocating liability.
  • Exacerbated Dilemma Between Free Speech and Information Governance: How to effectively govern the rampant spread of disinformation, combat online rumors, fraud, and malicious manipulation using AI, while avoiding unnecessary, excessive restrictions or “chilling effects” on legitimate freedom of speech, freedom of the press, academic discourse, and the free flow of information? This is an extremely complex and sensitive legal and policy balancing act in any era, but it becomes even more difficult and urgent in the AIGC context where information production and dissemination capabilities are exponentially amplified and decentralized.
  • Potential Interference with Legal Research, Decision-Making & Policy Formulation: If legal professionals (lawyers researching cases, judges referencing precedents, legislators investigating social conditions, scholars conducting legal research) inadvertently access, cite, or rely on AI-generated false legal information, erroneous statistics, biased analytical reports, or misleading case interpretations without rigorous verification, then their subsequent legal judgments, litigation decisions, rulings, or policy recommendations could be built on flawed foundations, with extremely serious and dangerous consequences. Maintaining vigilance and critical assessment of information sources is more crucial than ever in the AI age.

III. Deepening Fog and Opening Battlefields in Intellectual Property (IP)

The entire lifecycle of AI Generated Content (AIGC) technology—from the source and usage methods of the massive training data it relies on to learn, to the legal status and ownership of the novel content it ultimately generates—profoundly touches upon, challenges, and potentially even subverts many fundamental principles, core concepts, and operational rules of our existing intellectual property law system (especially copyright law). This has triggered a series of unprecedented, extremely complex theoretical debates, ethical arguments, and fierce legal battles globally, making intellectual property one of the most cutting-edge and thorny battlegrounds faced by the legal community in the age of AI.

• Copyright Compliance Risks of Training Data: Does AI Development Carry “Original Sin”?:

  • Core Dispute: Does Large-Scale Scraping & Unauthorized Copying Constitute Infringement?: Training modern large-scale AIGC models (whether LLMs for text, or models generating images, audio, video) typically requires “learning” from billions or even trillions of existing works sourced from the public internet, digitized libraries, code repositories, etc. This includes copyrighted news articles, literary works, academic papers, artworks, photographs, music recordings, film clips, software source code, and much more. AI developers often use web crawlers and other techniques to mass-scrape and copy this data, which is then used in the model training process (i.e., the model learns patterns, styles, and knowledge from this data). Does this massive copying and use of copyrighted works, mostly without explicit permission or license from the vast majority of original copyright holders, conducted for commercial purposes (developing and offering AI services), constitute infringement of copyright holders’ exclusive rights like reproduction and adaptation? This is the central focus and biggest controversy in numerous ongoing AI copyright lawsuits worldwide (e.g., The New York Times v. OpenAI, Getty Images v. Stability AI, and various class actions by authors and artists).
  • Debate over Applicability of “Fair Use” or Other Copyright Exceptions: AI developers and platform providers typically argue that their use of copyrighted works is for training AI models to promote technological progress and knowledge dissemination, qualifies as Transformative Use, and often involves only fragments or statistical features rather than full copies, thus should be protected under the “Fair Use” doctrine in US copyright law. In other jurisdictions (like EU, Japan), they might invoke specific copyright limitations and exceptions for “Text and Data Mining” (TDM) for scientific research or other purposes (e.g., Articles 3 & 4 of the EU Copyright Directive in the Digital Single Market). However, copyright holders (especially news media, publishers, artists) strongly object to whether these defenses hold, particularly for commercial AI model training. They argue this unlicensed, uncompensated mass use directly harms the market value and potential licensing revenue of their works and should not qualify as fair use or fall within exceptions. Currently, court rulings on these issues are still evolving globally and remain highly uncertain.
  • Lack of & Difficulty Building Large-Scale Data Licensing Models: For the massive, diverse, constantly updated data needed for AI training, there is currently no clear, efficient, easily operational large-scale data licensing mechanism globally that covers all types of works and rights holders. Copyright holders find it difficult to effectively track and control if and how their works are used for AI training, or to receive fair compensation. AI developers face huge legal risks and compliance costs, struggling to obtain all potentially needed licenses legally, conveniently, and affordably. How to build a future data licensing ecosystem that balances interests, promotes innovation while protecting creativity is a shared challenge for legislators, industry, and rights holders.

• The Conundrum of Copyright Ownership and Originality for AI-Generated Content (AIGC):

  • Core Question: Can AI Be a Legally Recognized “Author”?: According to the fundamental principles of copyright law in most countries (including China, US, EU members), copyright protection applies only to intellectual creations (i.e., “Works of Authorship”) independently created by a “person” (specifically, a natural person or legal entity with legal personality) that meet a certain threshold of “originality.” Therefore, content generated entirely autonomously by an AI program (assuming such “autonomy” is achievable) is generally considered to lack the requisite human authorship. The generation process might be seen more as an automated or mechanical execution rather than human creative expression, thus failing the core requirement for a “work” under copyright law and generally ineligible for copyright protection. (E.g., the US Copyright Office (USCO) has repeatedly issued guidance and rulings refusing copyright registration for works claimed to be solely generated by AI, unless significant human creative contribution can be demonstrated.)
  • Determining the “Author” Status of the Human User Guiding AI Generation via Prompts: So, can the human user who guides AI generation of specific content through carefully designed prompts be considered the “author” and thus hold copyright? This has become a central and highly debated issue in AIGC copyright. The emerging preliminary consensus tends to depend on the degree and nature of the user’s “creative contribution” throughout the “human-AI collaborative” creation process:
    • If the user only provides very simple, general, descriptive prompts (e.g., “draw a cat under the moonlight”), while the specific expression, artistic details, style choices, etc., are primarily determined by the AI model based on its internal algorithms and training data, the user’s contribution might be too minimal to meet the “originality” standard required by copyright law. The generated content might still be viewed as primarily AI-produced and ineligible for copyright.
    • However, if the user, through extremely complex, specific, creative, and instructive prompt design (e.g., detailing scene composition, lighting effects, character expressions/poses, color palettes, artistic style, possibly involving multiple rounds of prompt refinement, careful selection, combination, editing, and post-processing of AI-generated elements), ultimately creates a work that sufficiently and clearly reflects that user’s personal intellectual choices, aesthetic judgments, and unique creative expression, then that user might potentially be recognized as the author of the final work (or at least the parts formed by their creative contribution) and be entitled to copyright.
    • The Key Difficulty Lies In: How to legally define clearly the threshold at which user contribution becomes “sufficiently original”? How to effectively distinguish and prove in practice what constitutes the user’s creative input versus the AI’s automated output? Where is the line drawn? What are the standards? How is the burden of proof allocated? These are cutting-edge questions currently being actively explored and defined in judicial practice and theoretical research globally.
  • Does AIGC Constitute an Infringing “Derivative Work” or Copy of Training Data?: The “creativity” and “material” for AIGC ultimately derive from the massive training data it learned from. To what extent is specific AI-generated content (a melody, image, code snippet, story) an “imitation,” “recombination,” “style transfer,” or even unconscious “memorized reproduction” of specific copyrighted works present in its training data? If the generated content is “Substantially Similar” in expression to one or more existing copyrighted works in the training data under copyright law, then even if the AIGC itself might not qualify for copyright, it could be deemed an infringing reproduction or adaptation, constituting an Infringing Derivative Work. How to effectively and reliably determine and prove the existence of such “substantial similarity” within the large-scale, often “black box” processes of model training and content generation? How to balance protecting original authors’ rights with encouraging new technology application? This poses huge challenges in both technical forensics and legal determination.

• Risks Related to Trademarks, Goodwill, Trade Names, and Other Personality Rights:

  • Trademark & Trade Dress Infringement: AI-generated images, logos, product designs, packaging appearances, or even business names or slogans might unintentionally (or under malicious prompt guidance) be identical or confusingly similar to registered trademarks, distinctive trade dress, or trade names owned by others. This could lead to Likelihood of Confusion among consumers in relevant goods or services classes, constituting trademark infringement or unfair competition.
  • Infringement of Personality Rights:
    • Right of Publicity / Likeness Rights: AI-generated images or videos might be highly similar to the real likeness of a specific person (celebrity or ordinary citizen), or directly use their likeness for modification or synthesis (like deepfake face swaps). If done without consent and for non-fair use purposes (especially commercial or defamatory use), it could infringe their right of publicity or likeness rights.
    • Voice Rights (Right of Publicity / Voice Misappropriation): Using voice cloning technology to generate voices highly similar to specific individuals (actors, singers, voice artists) for commercial ads, audiobooks, or other unauthorized purposes might infringe their voice rights (in jurisdictions recognizing such rights) or constitute unfair competition.
    • Right to Name: Improper use of others’ names in AI-generated content.
    • Right to Reputation / Privacy: AI generating false information, defamatory content, or disclosing private personal information about specific individuals could infringe their right to reputation or privacy.

IV. Risk of Automated Amplification & Perpetuation of Bias, Discrimination & Harmful Content

AI Generated Content (AIGC) models, especially Large Language Models (LLMs), act like extremely powerful, tireless “cultural echo chambers” and “social bias amplifiers.” They not only passively learn and replicate various social biases, stereotypes, discriminatory patterns, and harmful information latent in their massive training data (sources discussed in Section 6.3), but might even, due to their algorithmic properties (e.g., giving higher weight to more frequent patterns in data) or through interactive learning with biased users, unintentionally amplify and entrench these biases, spreading them in new, potentially harder-to-detect forms.

• Manifestations of Risk:

  • Generating Content Reinforcing Stereotypes: One of the most common manifestations. When asked to depict or describe people of different occupations, genders, races, or origins, AI might disproportionately generate text descriptions or images aligning with prevalent (but often inaccurate or discriminatory) societal stereotypes. E.g., overwhelmingly showing white male images when asked for “scientist”; relying on outdated, simplistic, or derogatory labels when describing different cultures.
  • Generating Hate Speech, Violent Ideologies, or Extremist Content: If the AI’s training data contains significant amounts of harmful text or images promoting racism, sexism, religious extremism, political extremism, or glorifying violence and terrorism, even with developer efforts to filter and align, the model might, under certain prompt inducement (especially “jailbreak” prompts), “learn” and generate similar harmful content that incites hatred, creates division, or justifies violence and extremism.
  • Malicious Misuse for Generating Non-Consensual Intimate Imagery (NCII): One of the most nefarious and destructive misuses of AIGC (especially image/video generation tools based on Diffusion Models, etc.). Perpetrators use these technologies to maliciously and without consent synthesize victims’ facial images (or other identifiable features) onto pornographic or intimate pictures or videos, then distribute them online (social media, anonymous forums, malicious sites) for harassment, humiliation, blackmail, or revenge. This causes devastating, indelible psychological trauma, social harm, and privacy violation to victims (especially women and minors, who are disproportionately targeted).
  • Generating Biased or Inappropriately Suggestive Content in Legal Contexts: E.g.:
    • An AI tool assisting in drafting defense strategies, if trained mainly on successful cases for a specific type of crime or defendant background, might exhibit subtle biases or fixed thinking patterns when advising on other case types, overlooking more suitable defense angles.
    • An AI model used for preliminary case risk assessment or outcome prediction, if learning from historical data reflecting systemic biases in past judicial practice against certain groups (e.g., specific minorities, low-income populations), might unfairly and systematically assign higher risk ratings or predict less favorable outcomes for cases involving these groups.
    • Even when generating seemingly objective case summaries or evidence analyses, AI might, due to language patterns in its training data, unconsciously reveal bias or make inappropriate suggestions through word choice, descriptive style, or emphasis.

• Challenges in Mitigation & Governance:

  • Technical Limitations of Content Filtering & Safety Alignment: AI model developers typically invest heavily in technical measures like building sensitive word lists, training content classifiers, conducting specialized safety alignment training (e.g., using RLHF to teach models to refuse generating harmful content) to try to prevent models from generating obviously illegal, violent, pornographic, hateful, discriminatory, or otherwise defined “harmful” content.
  • However, these safety measures are not foolproof and far from perfect. Due to the extreme complexity of language and images, semantic ambiguity, cultural differences, and the constant emergence of adversarial prompts (e.g., using subtle language, role-playing, encoding tricks to design “jailbreak” prompts), malicious users can still find ways to bypass these safety guardrails. Completely eliminating all potential harmful, inappropriate, or biased output is considered technically extremely difficult, perhaps impossible.
  • The Dilemma of Balancing “Over-Censorship” vs. Utility/Creativity: At the same time, overly strict, blunt content filtering and safety restrictions can also have negative consequences. They might cause models to become too “conservative” or “rigid,” refusing to answer many legitimate queries involving sensitive or controversial topics (e.g., discussions on historical events, social issues, or necessary but potentially uncomfortable information in medical/legal domains); they might unduly suppress the model’s creativity, diversity, and sense of humor, making outputs dull and unengaging; or in some cases, overly broad filters might inappropriately censor legitimate speech, opinions, or artistic expressions that do not conform to certain mainstream values or political correctness. Finding the right, dynamic, socially acceptable balance between ensuring basic safety and ethical bottom lines while preserving model utility, fostering creative potential, and respecting freedom of expression and access to knowledge is a core, ongoing challenge in AI governance.

V. Profound Structural Impacts on Human Creativity, Employment Structures & Education Systems

The widespread application and rapid iteration of AI Generated Content (AIGC) technology are having impacts far beyond the purely technical and legal spheres. They are beginning to exert unprecedented, structural shocks and reshaping forces on fundamental pillars of human society, including cultural paradigms, economic structures, and education systems, potentially leading to profound long-term consequences.

• Blurring Boundaries & Redefining Value of Human Creativity:

  • Challenging Traditional Notions: AIGC technologies, especially those capable of generating text, images, or music with high artistic quality, novelty, or even apparent “emotional” expressiveness, are profoundly blurring and challenging our traditional notions of the boundaries of uniquely human “Creativity,” “Originality,” and “Authorship.”
  • Raising Philosophical Questions: When AI can independently (or under human prompting) create poems, paintings, or music indistinguishable from human creations, how do we redefine and understand “what is true creation?” “What is the unique, irreplaceable value of human creativity compared to machine generation?” “What is the essence of art?” These questions transcend technology, touching upon deep philosophical, aesthetic, and cultural value reflections.
  • Human-AI Collaboration as New Paradigm: The future likely involves not a simple dichotomy of “human creation” vs. “machine generation,” but Human-AI Collaboration becoming the new norm in content creation. The human role might shift more towards being the “director,” “curator,” or “collaborative creator”—proposing creative concepts, setting goals, designing intricate prompts, selecting, combining, editing, and deeply refining AI-generated elements.

• Impacting Employment Structures & Skill Requirements in Related Industries:

  • Heightened Automation Risk: AIGC development undoubtedly poses significant, potentially disruptive impacts on the existing employment structure and job demands in industries centered around content production, information processing, and creative design (e.g., journalism & media publishing, advertising & marketing, graphic design & illustration, film & animation production, music composition & arrangement, translation services, basic coding & documentation in software development, text & voice interaction in customer service, etc.). Many relatively routine, process-driven, repetitive creation, editing, translation, or information processing tasks are highly likely to be significantly automated or replaced by AI in the future.
  • Profound Transformation in Skill Demands: This doesn’t necessarily mean practitioners in these fields will become entirely unemployed, but it urgently requires them to proactively and rapidly adapt to this change, fundamentally reshaping and upgrading their skill sets. Future core competencies will shift away from traditional execution skills towards uniquely human values that AI struggles to replicate:
    • Deep, cross-disciplinary creative ideation and strategic thinking.
    • Complex emotional expression, empathetic communication, and interpersonal skills.
    • Sophisticated aesthetic judgment, cultural understanding, and ethical decision-making.
    • Proficiency in harnessing and collaborating with AI tools for higher-level creation and innovation (“Human-AI Collaboration” skills).
    • And crucially, deep domain expertise in specific fields (like law, medicine, finance), which is key to ensuring AI applications land effectively and deliver real value.
  • Adaptability & Lifelong Learning as Necessities: In this rapidly changing era, continuous learning, rapid adaptation to new technologies, and constant updating of knowledge and skills are no longer choices but basic requirements for survival and development for all professionals, especially knowledge workers.

• Severe Challenges & Transformative Opportunities for Existing Education Systems:

  • Fundamental Challenges to Assessment & Academic Integrity: Students can now extremely easily and almost cost-free use various AIGC tools (especially LLMs) to complete homework assignments, write essays, generate lab reports, or even produce code for exams. This poses an unprecedented, fundamental challenge to traditional educational assessment methods primarily relying on text output and the systems for maintaining academic integrity. Schools and educators urgently need to rethink and explore new forms of assignments, projects, and assessment methods that cannot be easily “outsourced” to AI and can more genuinely evaluate students’ deep understanding, critical thinking, problem-solving abilities, and original thought (e.g., emphasizing class discussions, hands-on practice, project-based learning, oral defenses, or requiring students to explain their process and thinking, including AI usage).
  • Profound Need for Change in Educational Goals & Content: AIGC’s prevalence also means educational models focused merely on transmitting “knowledge” content easily accessible or generatable by AI are becoming obsolete. Future educational goals need to shift focus towards cultivating core competencies AI struggles with: e.g., ability to ask good questions, critical thinking and information literacy, cross-disciplinary integration and complex problem-solving, creativity and imagination, communication, collaboration, leadership, and crucially—the ability to learn how to learn (Meta-learning) and a disposition for lifelong learning. Educational content also needs to incorporate more knowledge about AI technology itself, discussions of its ethical risks, and skill development in responsible collaboration with AI.
  • Huge Opportunities for AI-Powered Educational Innovation: Simultaneously, AIGC brings tremendous opportunities for innovation in education. It can be developed into various personalized learning tools and intelligent tutoring systems:
    • Providing customized practice problems and learning resources tailored to each student’s pace and characteristics.
    • Offering 24/7 instant answers to questions and preliminary feedback.
    • Creating immersive, interactive virtual learning environments and simulation labs.
    • Assisting teachers with tasks like lesson planning, grading (some standardized assignments), analyzing student progress, freeing up more teacher time for personalized guidance and inquiry-based teaching. Education systems need to actively explore how to effectively and responsibly integrate AI into all aspects of teaching and learning, leveraging its strengths to enhance educational quality, promote equity, and ultimately cultivate talent capable of adapting to and leading future societal development.

Conclusion: Embrace AIGC’s Creative Power, Beware Its Potential Harm, Guided by Rules and Wisdom

AI Generated Content (AIGC) technology is undoubtedly a “magical double-edged sword” containing immense creative power and transformative energy, but simultaneously harboring huge risks and destructive potential. It shows incredibly exciting prospects in greatly empowering individual and organizational creativity, enhancing content production and information dissemination efficiency like never before, and potentially bringing disruptive innovation and value across numerous fields.

However, accompanying this potential are severe challenges demanding urgent societal attention and solutions: the fundamental threat to social trust and evidence systems posed prominently by Deepfakes; the serious danger to the information ecosystem and public safety from the unprecedented scale and speed of disinformation and misinformation proliferation; the profound uncertainties and fierce conflicts encountered by existing intellectual property law systems when facing AI training and generation activities; the ethical concerns over automated amplification and entrenchment of social biases and harmful content; and the deep structural impacts on existing employment structures, skill demands, and educational models.

The legal industry, as the drafter, interpreter, applier, and ultimate guardian of societal rules, and the final arbiter of rights definition and responsibility allocation, plays an extremely critical and unique role in this profound transformation triggered by AIGC technology. Not only in our own daily practice, do we need to evaluate, select, and apply AIGC technology with the highest professional standards and utmost prudence (e.g., exploring beneficial uses in legal visualization assistance, drafting non-core initial documents, or internal knowledge management, while maintaining absolute vigilance and strict gatekeeping regarding evidence authenticity, content accuracy, and IP compliance). We also bear a significant historical mission: leveraging our legal expertise, commitment to fairness and justice values, and keen insight into potential risks, to actively and proactively participate in the entire societal process of setting reasonable boundaries for this powerful new technology, building effective governance mechanisms, refining relevant legal frameworks, and exploring adaptive ethical norms.

Deeply understanding AIGC technology’s unique capabilities, specific risks, ethical dilemmas, and potential deep impacts on law and society is the necessary prerequisite for us to be able to effectively anticipate, prevent, and respond to its potential harms, guiding its development path to always serve the construction of the rule of law and the long-term well-being of humanity, while enjoying the enormous convenience and infinite possibilities it may bring. This requires cross-disciplinary dialogue, sustained effort, and collective wisdom from legal professionals, technology experts, policymakers, industry leaders, educators, and the public. Only then can we ensure that this “magic sword” of the intelligent age is ultimately wielded for good.