Key Considerations for Deploying AIGC Products: Safety, Capacity, Cost, Legal Compliance, and Bias

GPT-4 was released on March 14, 2023 and is currently only available to paid ChatGPT Plus subscribers, enterprises, and developers.

Bill Gates highlighted AI as a historic turning point, suggesting we may be at a pivotal moment.

Major companies like Google, Microsoft, and domestic giants are heavily investing in AIGC, and technology managers must understand the challenges ahead.

If your organization is launching or has launched an AIGC product, five critical areas require attention.

1 Safety

Safety is the lifeline of any product, especially AIGC, where failures can jeopardize the entire business.

1.1 Content Safety

Content safety refers to the impact of generated content on the product, covering political, pornographic, violent, illegal, and value‑related topics that must be detected and filtered.

OpenAI added a safety reward signal during RLHF training of GPT‑4 to reduce harmful outputs, using a zero‑shot classifier to judge safety boundaries.

Since GPT‑4 is closed‑source, developers must rely on external safety platforms, dual‑layer filtering, real‑name traceability, and multi‑level propagation controls.

Integrate content‑moderation platforms for double‑layer filtering of inputs and outputs.

Real‑name traceability to track user‑uploaded or generated media and protect sensitive information.

Control propagation with tiered review thresholds (e.g., re‑moderation after 100,000 views).

1.2 Misinformation

ChatGPT can produce confident but fabricated answers, leading to misinformation in text, images, or video (e.g., deep‑fake scams).

Mitigation requires real‑name policies, content‑safety checks, and user education.

1.3 Model Security

Model attacks, backdoors, and bias can compromise AI systems; safeguards include robust training data, monitoring, and transparent algorithms.

Model attacks : injecting malicious or toxic data to corrupt outputs.

Model backdoors : hidden rules that trigger specific responses.

2 Capacity

AIGC products must address storage, processing, bandwidth, and moderation capacity as user volume grows.

Storage capacity : plan per‑user limits and retention periods.

Processing capacity : ensure sufficient compute resources to avoid latency.

Bandwidth capacity : provision high‑throughput networks for peak traffic.

Moderation capacity : allocate human and automated resources for content review.

3 Cost

Building large models in‑house is costly; most companies use third‑party APIs (e.g., OpenAI) or fine‑tune open‑source models.

OpenAI pricing: $2 per million tokens for ChatGPT, $0.06 per token for GPT‑4, image generation $0.02 per 1024×1024 image.

On‑premise GPU costs (e.g., V100) can exceed $7,000 per month per machine.

Cost‑saving measures include queue systems, rate limiting, reservation systems, and storage quotas.

4 Legal and Copyright

4.1 Regulations

China's "Internet Information Service Deep‑Synthesis Management Measures" (effective Jan 10 2023) require user consent for biometric editing, prohibit illegal content, and mandate safety, audit, and identity verification mechanisms.

Prohibit creation/dissemination of illegal content.

Establish user registration, algorithm audit, ethics review, data security, and anti‑fraud systems.

Publish management rules and real‑name verification.

Implement content‑review pipelines and rumor‑control mechanisms.

4.2 Copyright

AIGC training data often includes copyrighted material, leading to legal uncertainty; current practice treats the AI user as the copyright holder, not the model.

Best practices: obtain data licenses, anonymize data, label copyright, and follow ethical guidelines.

5 Model Bias

Pre‑trained models inherit biases from training data (e.g., language bias, gender or racial stereotypes, temporal bias).

Chinese language under‑representation in ChatGPT.

ControlNet generating overly sexualized images for certain prompts.

DALL·E 2 exhibiting gender and race stereotypes.

Mitigation strategies include diverse data collection, fairness metrics during fine‑tuning, continuous monitoring, and post‑generation bias detection.

Understanding and addressing these biases is essential before launching AIGC products.

Embrace AI at this historic turning point and move forward.