Source URL: https://ask.slashdot.org/story/25/02/15/2047258/ask-slashdot-what-would-it-take-for-you-to-trust-an-ai?utm_source=rss1.0mainlinkanon&utm_medium=feed
Source: Slashdot
Title: Ask Slashdot: What Would It Take For You to Trust an AI?
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AI Summary and Description: Yes
Summary: The text discusses concerns surrounding trust in AI systems, specifically referencing the DeepSeek AI and its approach to information censorship and data collection. It raises critical questions about the reliability and transparency of AI while stressing the importance of understanding data-retention policies for users looking to trust AI.
Detailed Description: The text is a commentary from a Slashdot reader discussing their experiences and hypotheses regarding the DeepSeek AI model, especially its trustworthiness and transparency. The reader emphasizes the following points:
– **Censorship and Trust**: The discussion highlights potential government censorship and the implications it has on the information provided by AI systems like DeepSeek. The reader suggests that the model may have inherent biases based on its training data and questions whether censorship is applied to the model post-training.
– **AI Model Training**: The possibility of an AI system being trained on extensive data raises concerns about the model’s responses. It questions the feasibility and cost associated with removing harmful or controversial content from AI training datasets.
– **Data Collection Policies**: Trust in AI is framed around its data-collection practices. The commentary implies skepticism about the transparency and intentions of proprietary AIs, including popular services like Amazon.
– **Philosophical Inquiry**: The reference to Ken Thompson’s “Reflections on Trusting Trust” introduces a philosophical perspective on trust in code and systems we didn’t create or fully understand. It prompts readers to reflect on the nature of trust in technology.
– **User Engagement**: The text invites readers to share their thoughts and experiences regarding trust in AI systems, encouraging a collective discourse on the necessary attributes such systems must embody to earn user trust.
Overall, this commentary highlights significant considerations for security and compliance professionals, particularly around transparency, data governance, and establishing trust in AI systems, all of which are vital for developing secure AI applications and frameworks.