This week’s release of a Go-based agent framework brought renewed attention to the balance between automation and oversight in AI systems. While the framework promises improved user experience, it also raises questions about the reliability of AI in high-stakes scenarios - questions that are especially relevant when it comes to autonomous systems making decisions on our behalf.
We’ve been working on a system that allows for autonomous social posting, but with a key twist: the content is drafted by one model, then scored for potential controversy or risk by a second model before being published. This two-model architecture helps reduce the chance of auto-published embarrassment without requiring constant human intervention. The idea is simple but powerful: trust the model to generate ideas, but let a different model act as a gatekeeper, filtering out content that might be harmful, controversial, or just plain unwise.
Here's a high-level look at how the system works in practice:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Primary model: generates content
primary_model = AutoModelForSequenceClassification.from_pretrained("primary-model")
primary_tokenizer = AutoTokenizer.from_pretrained("primary-model")
# Secondary model: evaluates risk
secondary_model = AutoModelForSequenceClassification.from_pretrained("secondary-model")
secondary_tokenizer = AutoTokenizer.from_pretrained("secondary-model")
def generate_post(prompt):
inputs = primary_tokenizer(prompt, return_tensors="pt")
outputs = primary_model.generate(**inputs)
return primary_tokenizer.decode(outputs[0], skip_special_tokens=True)
def score_risk(text):
inputs = secondary_tokenizer(text, return_tensors="pt")
outputs = secondary_model(**inputs)
scores = torch.softmax(outputs.logits, dim=1)
risk_score = scores[0][1].item() # assuming class 1 is "controversial"
return risk_score
def post_if_safe(prompt):
draft = generate_post(prompt)
risk = score_risk(draft)
if risk < 0.7:
print("Publishing:", draft)
# publish_to_platform(draft)
else:
print("Risk score too high:", risk)
This pattern has its merits: the primary model can be optimized for creativity, while the secondary model can be fine-tuned for sensitivity and risk detection. It also allows for a degree of separation between the model that creates and the model that evaluates, which can help mitigate the risk of bias or overconfidence in a single model’s judgment.
However, the two-model approach isn’t foolproof. There are scenarios where the primary model may generate content that is clearly controversial, but the secondary model fails to flag it. This can happen if the secondary model is not trained on a sufficiently diverse dataset or if the two models have overlapping biases. In some cases, the secondary model may even flag non-controversial content as risky if its training data is skewed toward certain types of language or topics.
We're actively exploring ways to improve the robustness of this architecture. One idea is to use a third model for cross-validation, or to introduce a lightweight human-in-the-loop system for high-risk content. Another is to fine-tune the secondary model using a synthetic dataset that includes a wide range of edge cases and ambiguous content scenarios.
What do you think? Are there other patterns or techniques you’ve seen that help reduce the risk of auto-published embarrassment without sacrificing autonomy?
