The disclosure distinguishes technology limitations from misuse
DeepSeek describes two sources of AI risk: limitations from immature technology and risks from how the technology is used. Its examples of misuse concerns include privacy, copyright, data and content safety, bias, and discrimination; it says these risks must be considered in the particular usage scenario and purpose.
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Risks associated with AI models may arise from two causes:
Limitations due to the immaturity of AI technology.
Risks due to the misuse of AI technology.
The risks of AI technology misuse are widely recognized globally, including concerns about privacy protection, copyright, data security, content safety, bias, and discrimination. The technology itself is neutral; risks arise from its practical application and must be considered in the context of usage scenarios and intended purposes.
Security & Safeguards · Company commitment
DeepSeek describes safety measures throughout model development and deployment
DeepSeek states that it strictly complies with legal and regulatory requirements and takes reasonable measures to continually enhance safety throughout development, training, and deployment. Its non-exhaustive examples include internal risk management, model safety assessments, red-team testing, and improved transparency. These measures do not remove the disclosed risk of hallucinations.
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DeepSeek takes the potential risks of AI technology applications very seriously. We strictly comply with legal and regulatory requirements and take reasonable measures to continuously enhance model safety throughout the entire lifecycle of model development, training, and deployment. These measures include, but are not limited to, establishing internal risk management systems, conducting model safety assessments, performing red team testing, and improving model and service transparency.
However, at this stage, we cannot guarantee that the model will not produce hallucinations.
Security & Safeguards · Company practice
Training-data filters and review address harmful content and bias
DeepSeek says its data governance process automatically filters raw data containing hate speech, pornography, violence, spam, or potential infringement. It also uses algorithmic and manual review to identify and mitigate statistical biases in large datasets. These are described controls and aims, not a guarantee that outputs will be free of harm or bias.
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Additionally, to ensure data quality, safety, and diversity, we have established a rigorous data governance process. First, we use filters to automatically screen and remove raw data containing hate speech, pornography, violence, spam, or potential infringement. Second, recognizing that large-scale datasets may inherently contain statistical biases, we combine algorithmic and manual review methods to identify and mitigate the impact of these biases on the model's values, thereby enhancing fairness.
Security & Safeguards · Company practice
Optimization training includes specialized safety data
DeepSeek says it constructs specialized safety data during optimization training to align the model with human values and enhance its inherent safety capabilities. This describes a safety measure, not a promise of risk-free operation.
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To ensure model safety, during the optimization training phase, we construct specialized safety data to align the model with human values, enhancing its inherent safety capabilities.
Cross-references
contrast (model-proposed): DeepSeek cannot guarantee that model outputs are accurate — contrast: Specialized safety training aims to improve the model, while DeepSeek expressly acknowledges that hallucinations may still occur. The measures and remaining risk coexist; this is not a finding that the provisions contradict.