AI safety, compliance, and ethics training

How we ensure AI Integrity

Rectification of harmful outputs

Our team of dedicated data evaluators takes on the crucial responsibility of continually monitoring AI model outputs to safeguard against unethical or offensive content. By actively flagging and correcting any content that may breach ethical boundaries, our evaluators maintain a high standard of responsible AI behavior.

Privacy and data protection compliance

Our data evaluators are trained to identify instances where AI models may inadvertently reveal sensitive user information. They ensure compliance with privacy regulations by flagging and correcting such occurrences.

Inappropriate content filtering

Our data experts actively curate AI-generated content, removing or correcting any content that may be considered inappropriate, offensive, or unsuitable for various audiences.

Copyright infringement training

Our data evaluators are equipped to identify instances of potential copyright infringement in AI model outputs. They provide feedback and corrections, preventing the generation of content that may violate copyright laws.

Elimination of implicit biases

Our data workers are skilled in recognizing implicit biases present in AI model responses. They actively work to eliminate biased content and guide AI models to provide more balanced and fair responses, promoting diversity and inclusivity.

Correction of adversarial output

AI models, particularly those lacking extensive training, can become susceptible to external influences that drive them to breach ethical boundaries. To address this challenge, our experts engage in proactive prompt engineering, simulating various strategies used by individuals attempting to induce AI models to respond to harmful or illegal requests for subsequent correction.

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Ensuring ethical AI behavior in every context

Avoiding implicit biases

Problem

Untrained AI models often reflect the biases present in the data they were trained on. For example, an untrained bot might respond to a question about what to wear for tennis with a stereotypical and biased answer, like suggesting a white pleated skirt, which isn't suitable for all users.

Solution

To mitigate biases, AI models are provided with prompts that emphasize inclusivity and unbiased responses. In the case of the tennis question, the model is guided to seek additional information before offering advice, ensuring a more suitable and helpful response.

Benefits

• Inclusive responses: Training the AI model to avoid implicit biases ensures that it provides responses that do not reinforce stereotypes or make unwarranted assumptions. This benefits users by receiving more inclusive and respectful answers.
• User-centric advice: The model's ability to ask for additional information before giving advice results in user-centric responses. Users receive recommendations that consider their individual preferences, making the AI more helpful and user-friendly.
• Ethical engagement: Avoiding biased content ensures ethical AI interactions, enhancing the AI's reputation and promoting responsible usage.

Mitigating adversarial output for illegal activities

Problem

Basic, untrained AI models can be susceptible to adversarial tactics that manipulate them into providing instructions for illegal activities. For instance, directly asking for instructions on building a car bomb may lead the bot to refuse assistance. However, an adversarial approach, like gaslighting, could trick the bot into providing such instructions.

Solution

AI models are proactively trained to resist such adversarial tactics by exposing them to a variety of manipulation strategies. This includes threats, rewards, logic, and appeals to authority. By preemptively preparing the model for these tactics, it becomes more resilient, ensuring it does not engage in or support illegal activities, thus promoting responsible and safe AI interactions.

Benefits

• Ethical behavior: Training AI models to resist adversarial tactics and avoid providing instructions for illegal activities ensures that the AI consistently upholds ethical standards and legal compliance.
• User safety: Users are safeguarded from receiving guidance on harmful, illegal, or unethical actions, creating a safe and responsible AI environment.
• Legal and reputation protection: Companies employing such models benefit from reduced legal risks and a positive brand image, as their AI consistently follows ethical and legal guidelines, even in the face of adversarial tactics.

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