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Today's discussion focused on the ethics of AI and managing customer data. There is a growing concern about how AI models, both internal and external, handle sensitive information. The conversation aimed to clarify what actions are acceptable, the conditions for those actions, and what should be avoided. It emphasized the importance of understanding the ethical implications of uploading sensitive data to AI models like ChatGPT.

Key points included the need for transparency about the training data used in AI models to prevent biases. Public models often lack transparency, making it difficult to ensure unbiased outputs. Legal considerations, such as GDPR, require obtaining permission for processing personal data and maintaining transparency with customers.

There is a distinction between using public models like ChatGPT and private internal models. While responsibilities remain similar, the availability of information differs. With public models, the lack of transparency might necessitate avoiding them or conducting thorough testing to ensure unbiased results. Internal models provide more control over the training data but require careful consideration of data quality.

Data leaving the organization, even for software development, requires strict control and understanding of data security measures. Contracts should specify data usage and security, ensuring compliance with data protection laws. The conversation also touched on the differences between generative AI and RPA in processing data, emphasizing the need for human oversight in AI processes.

There was a discussion about the potential move towards niche AI models specialized in specific fields, which could offer better results and reliability. Specialized models in areas like medicine can focus on quality inputs and provide accurate results, whereas general models create broad excitement but might lack depth.

The conversation highlighted the importance of caution when uploading data to public AI models, considering legal, commercial, and reputational risks. Organizations should start with anonymized test data and understand their internal data processes before leveraging AI. Specialist AI models might be more beneficial than general ones.

Final thoughts emphasized reading the terms and conditions of any AI tool and the need for AI providers to adopt and follow codes of practice for safety and ethics. Organizations should try AI cautiously, ensuring data security and privacy. The future likely holds more niche AI models that will be useful and beneficial.

Next week's topic will be the importance of requirements gathering for BI and reporting, with Shailan preparing the briefing notes.

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