OpenAI wants to solve the content moderation dilemma

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• 📖 OpenAI wants to solve the content moderation dilemma using GPT-4. We take a look how and what the implications could be.

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OpenAI wants to solve the content moderation dilemma

It’s a fairly safe assertion to make that no one has really figured out how to moderate harmful content at scale. OpenAI envisions a future where AI technology can alleviate the burden on human moderators and ensure a more positive online experience. The approach is open to anyone with OpenAI API access to implement their own AI-assisted moderation system.

Challenges in content moderation

Content moderation demands meticulous effort, sensitivity, a profound understanding of context, as well as quick adaptation to new use cases, making it both time consuming and challenging. Traditionally, the burden of this task has fallen on human moderators sifting through large amounts of content to filter out toxic and harmful material, supported by smaller vertical-specific machine learning models. The process is inherently slow and can lead to mental stress on human moderators.

Using large language models

OpenAI is exploring the use of LLMs to address these challenges. Large language models like GPT-4 can understand and generate natural language, making them applicable to content moderation. The models can make moderation judgments based on policy guidelines provided to them.

With this system, the process of developing and customising content policies is trimmed down from months to hours.

1. Once a policy guideline is written, policy experts can create a golden set of data by identifying a small number of examples and assigning them labels according to the policy.

2. Then, GPT-4 reads the policy and assigns labels to the same dataset, without seeing the answers.

3. By examining the discrepancies between GPT-4’s judgments and those of a human, the policy experts can ask GPT-4 to come up with reasoning behind its labels, analyse the ambiguity in policy definitions, resolve confusion and provide further clarification in the policy accordingly. We can repeat steps 2 and 3 until we are satisfied with the policy quality.

This iterative process yields refined content policies that are translated into classifiers, enabling the deployment of the policy and content moderation at scale.

Optionally, to handle large amounts of data at scale, they can use GPT-4's predictions to fine-tune a much smaller model.

Improvements to traditional approaches to content moderation:

• More consistent labels. Content policies are continually evolving and often very detailed. People may interpret policies differently or some moderators may take longer to digest new policy changes, leading to inconsistent labels. In comparison, LLMs are sensitive to granular differences in wording and can instantly adapt to policy updates to offer a consistent content experience for users.

• Faster feedback loop. The cycle of policy updates – developing a new policy, labelling, and gathering human feedback – can often be a long and drawn-out process. GPT-4 can reduce this process down to hours, enabling faster responses to new harms.

• Reduced mental burden. Continual exposure to harmful or offensive content can lead to emotional exhaustion and psychological stress among human moderators. Automating this type of work is beneficial for the wellbeing of those involved.

OpenAI's approach differs from Constitutional AI, which primarily relies on the model's internalised judgment. OpenAI's focus is on platform-specific content policy iteration and making the process faster and less burdensome. Trust & Safety practitioners are encouraged to try out this approach using OpenAI API access.

While AI technology holds promise for content moderation at scale, it is important to note the limitations. Language models can be susceptible to unintended biases introduced during training. The output of the models should be closely monitored, validated, and refined with human input. By using language models to handle certain aspects of moderation, human resources can be directed towards addressing complex edge cases that require their expertise.

It remains uncertain whether OpenAI's approach will succeed; however, one aspect is unequivocal: if OpenAI’s large language model can be used successfully for content moderation, it will unlock a multibillion-dollar market for the company.

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