Author: Zhang Feng

1. Technological Surge and Governance Lag: What Are the Boundaries of Digital Virtual Human Services?

As AI-driven virtual hosts engage in sales 24 hours a day, as "digital civil servants" in government service halls patiently address inquiries, and as tireless "AI doctors" emerge in the field of medical science education, we are witnessing a new social landscape deeply embedded with digital virtual humans. With their notable advantages of low cost, strong interactivity, high efficiency, and around-the-clock service, digital virtual humans have quickly become a key driver of the intelligent economy. From e-commerce live streaming to cultural tourism promotion, from medical science education to government consultations, their application scenarios are expanding with unprecedented breadth and depth.

However, the rapid advancement of technology often precedes the establishment of rules. When virtual images can easily mislead, when AI conversations may carry biases, and when the behavior of autonomous intelligent agents (Agent) becomes unpredictable, a series of pressing questions arise: What are the service boundaries of digital virtual humans? Who should bear the responsibility for their actions? While pursuing efficiency and innovation, how do we ensure that technological development does not deviate from a "human-centric" trajectory? These are not just technical issues but also governance questions related to social trust, ethical boundaries, and long-term development. The recently published "Digital Virtual Human Information Service Management Measures (Draft for Comments)" (hereinafter referred to as "Measures") by the National Internet Information Office is a concentrated response to these era-defining questions.

2. Multiple Interwoven Risks Call for Systematic Norms and "Technology for Good" Principles

The urgent need to delineate boundaries for digital virtual human services stems from the multiple and intertwined risks and challenges exposed during their development process.

Firstly, there are safety and ethical risks. Deep synthesis technology significantly lowers the threshold for identity fraud, spreading false information, and emotional manipulation, potentially infringing on personal rights, disturbing social order, and even threatening national security.

Secondly, there is the risk of ambiguous responsibility attribution. The behavior of digital virtual humans is driven by algorithms, and the responsibility chain among their designers, developers, operators, and users is complex. Once issues arise, it is easy to fall into the dilemma of "algorithmic black box" and responsibility vacuum.

Furthermore, there is the risk of digital divide and the entrenchment of biases. If there are biases in the training data of algorithms, digital virtual humans may unconsciously amplify existing societal biases or develop new forms of discrimination in their services.

More profoundly, with the development of cutting-edge technologies like the Rotifer intelligent agent’s autonomous evolution protocol, intelligent agents with certain self-learning and evolutionary capabilities may exhibit behaviors that exceed preset objectives, creating a long-term social impact filled with uncertainties.

These risks do not exist in isolation; they are interconnected, pointing to a core contradiction: the immense potential of technological advancement versus the lagging existing governance framework. Therefore, the issuance of the "Measures" is not only a "firefighting" response to specific irregularities but also reinforces the foundational logic for the healthy development of the digital economy and institutionalizes the core philosophy of "human-centered, technology for good".

3. Full-Process Norms and Responsibility Penetration: Constructing a "Human-Centric" Governance Framework

In the face of the aforementioned challenges, the "Measures" construct a governance framework centered on "full-process norms" and "penetrating responsibility". Its core strategies can be summarized as "drawing lines, clarifying entities, enhancing regulation, and promoting goodness".

First, explicitly delineate the non-negotiable safety and ethical baseline. The "Measures" detail the prohibitions against using digital virtual humans for activities that harm national security, damage public interests, infringe on the legal rights of others, spread false information, disturb economic and social order, etc. This sets clear red lines for all market participants.

Second, establish and penetrate the responsibilities of various entities. The measures clarify the responsibilities of service providers, technical supporters, content producers, and users, requiring service providers to fulfill obligations such as registration, identification, content review, data security, and emergency response, thus achieving closure and traceability in the responsibility chain.

Third, emphasize a "human-centric" service philosophy. This requires that the design, development, and application of digital virtual humans must respect social morals and ethics, protect users' rights to information and choice, avoid misuse of user data and excessive personalized recommendations, and ensure that technology serves the holistic development of individuals.

Fourth, implement the principle of "technology for good", encouraging innovation within regulation. The "Measures" do not hinder technological development but instead delineate safety zones to provide stable expectations for responsible innovation, support collaboration between industry, academia, and research, and guide resources toward ethical applications that enhance well-being. This set of measures aims to transform "human-centric" from an abstract concept into specific, operational, and regulatory rules.

4. Short-Term Pain and Long-Term Benefits: Injecting Certainty into the Intelligent Economy

The implementation of the "Measures" is expected to have a profound impact on the digital virtual human industry and the entire intelligent economy ecosystem.

In the short term, the industry may experience a "pain period" of rising compliance costs and restrictions on certain primitive growth models. Companies will need to allocate resources for technological rectification, establish internal review mechanisms, and complete registration processes, while some borderline applications will be forced to adjust or withdraw.

In the long run, the "certainty" dividends brought about by this regulation will far exceed the short-term costs. First, it greatly enhances industry credibility and social acceptance. Clear rules eliminate public concerns about technological abuse and help establish user trust, which is a psychological foundation for the scaled development of the industry. Second, it optimizes the market competitive environment. By clearing out low-quality, non-compliant competitors, market resources and user attention are directed toward companies that truly possess technological strength and compliance awareness, facilitating quality supply. Third, it provides clear directional guidance for capital and technological research and development. Investors and R&D institutions can more confidently channel resources into areas that align with policy directions and possess long-term social value, such as education, healthcare, elder care, and cultural heritage.

Ultimately, a regulated, healthy, and sustainable digital virtual human industry ecosystem will become a solid foundation for deepening and expanding "Artificial Intelligence +" actions, strongly supporting the digital transformation of traditional industries and the creation of new forms of the intelligent economy. From a macro perspective, this also signifies an important institutional exploration in the field of artificial intelligence governance in our country, contributing a "Chinese solution" that balances innovation and governance to the world.

5. Challenges in Rule Implementation: Technological Iteration Continues to Present New Questions

Although the "Measures" have constructed a basic regulatory framework, specific implementation still faces several noteworthy issues and risks, including technological integration, responsibility attribution, lack of standards, and technological iteration.

The primary risk lies in the complexity of technological regulation. Digital virtual human technology integrates a variety of cutting-edge technologies, such as AI, graphics rendering, natural language processing, and even blockchain and quantum networks, resulting in dynamic, complex behavior patterns. How to effectively identify violations while avoiding excessive intervention that hinders technological innovation poses high demands on regulatory technology (RegTech) capabilities.

Secondly, practical difficulties in responsibility attribution remain. For instance, when a digital virtual human developed based on an open-source technology ecosystem encounters issues, how should responsibility be accurately divided among the open-source community, model adjusters, application integrators, and end operators?

Furthermore, the absence of a standard system represents a significant shortcoming. There is currently a lack of unified, detailed industry and national standards regarding the identity identification, ethical evaluation, algorithm transparency, performance testing, etc., of digital virtual humans, which could lead to inconsistent enforcement across regions and affect fairness.

Lastly, the most fundamental risk is the rapid iteration of technology itself, particularly the autonomy of agents, multi-agent collaboration, and future capabilities that might come from the integration with quantum computing, continually challenging the foresight and inclusiveness of existing rules. Rules need to maintain a certain degree of flexibility; however, the balance point between flexibility and rigidity is difficult to grasp. These risks remind us that governance is a dynamically evolving process and cannot be a one-time effort.

6. Governance and Technology Co-Evolving Towards a "Responsible Intelligence" Future

Looking ahead, the governance of digital virtual human services is expected to exhibit a trend of deep co-evolution with the technology itself.

First, governance will increasingly become "technological" and "intelligent". Regulatory bodies will utilize AI technology more for oversight, for example by developing deepfake content detection platforms and establishing monitoring networks for digital virtual human behaviors to achieve "governing technology with technology." Blockchain technology may be used to create immutable digital virtual human identity identifiers and behavior evidencing chains to enhance traceability.

Second, open-source technology ecosystems will play a key role in compliant innovation. Healthy open-source communities can facilitate the formation of industry best practices, share compliance toolkits (such as ethical review algorithm modules), and lower compliance thresholds for small and medium enterprises, embedding the principles of "technology for good" into the foundational technology through code.

Third, standard and certification systems will accelerate establishment. It is expected that under the guidance of the "Measures", industry associations and standard organizations will lead the development of a complete set of standards from data and algorithms to application and evaluation, and may develop third-party ethical certification mechanisms that become important references for market choices.

Fourth, the focus of governance will shift from "post-event handling" to more "pre-event prevention" and "in-event intervention." Bias audits of training datasets, ethical alignment of algorithm objectives, and simulated sandbox testing of agent behaviors will control risks at their inception. Especially for protocols like Rotifer that emphasize autonomous evolution, governance logic may need to draw on ideas such as "safety barriers" or "constitutional AI" to establish inviolable core principles for the self-evolution of intelligent agents.

Ultimately, what we will welcome is not an industry stagnated by rules but a future that steadily advances along the path of "responsible intelligence." Digital virtual humans will truly become human partners that enhance production efficiency, enrich cultural life, and optimize public services, rather than uncontrollable risk sources. This process will require continuous dialogue and joint construction among policymakers, technology developers, businesses, and the public, with the core anchor being that enduring commitment to "human-centered" values.

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