2024-10-25 One-Minute Post

Apple offers $1 million for hacking its private AI cloud, emphasizing its commitment to user privacy. The Biden-Harris Administration outlines a coordinated approach to address AI risks related to privacy and bias. A report stresses the need for transparency in the privacy policies of generative AI tools. Meanwhile, businesses are urged to consider the impact of AI on data governance, particularly in addressing biases. Cybersecurity teams are being excluded from AI implementation discussions, raising concerns about bias and privacy. To build trustworthy AI systems, developers are advised to focus on fairness, interpretability, privacy, safety, and security. Additionally, accountants are encouraged to seek fairness-promoting companies when using AI, while efforts are made to enhance transparency and fairness in automated credit decisions using AI.

Articles we found interesting:

• 1. Apple Intelligence bug bounty invites researchers to test its privacy claims - The Verge link Highlight: Apple, of course, has made a big deal over the years about how much it cares about user privacy, so poorly designed cloud servers for AI could poke a …

• 2. Apple will pay security researchers up to $1 million to hack its private AI cloud | TechCrunch link Highlight: … AI model, dubbed Apple Intelligence, which can handle far heavier-lift AI tasks in a way that Apple says preserves the customers' privacy. Topics.

• 3. Transparency needed for risks, privacy policies of generative AI tools, report says link Highlight: The need for transparency about the potential benefits, risks, and data privacy policies associated with generative AI tools is needed now more …

• 4. FACT SHEET: Biden-Harris Administration Outlines Coordinated Approach to Harness … link Highlight: These requirements require agencies to monitor, assess, and mitigate AI risks related to invasions of privacy, bias and discrimination, the safety of …

• 5. Business Data Privacy Standards And The Impact Of Artificial Intelligence On Data Governance link Highlight: Data Quality and Bias: AI models are only as good as the data they are trained on. Biases inherent in training data can perpetuate inequalities or …

• 6. Cybersecurity teams being excluded from AI implementation discussions, ISACA study shows link Highlight: … bias in AI algorithms, and can come with ethical and privacy concerns. Other developments, including research into integrating AI with quantum …

• 7. Four Cornerstones For Building A Future Where We Can Trust AI - Forbes link Highlight: To build trustworthy AI systems, developers should focus on fairness, interpretability, privacy, safety and security—the pillars of responsible …

• 8. How can accountants safely use AI in practice? link Highlight: Fairness: Always search for companies that promote fairness in their large language models (LLMs) or machine learning (ML) models, as AI should not …

• 9. Enhancing transparency and fairness in automated credit decisions: an explainable novel … - Nature link Highlight: As highlighted, integrating Artificial Intelligence (AI) in credit scoring improves predictive accuracy but also raises concerns regarding …

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