2024-03-19 One-Minute Post

Privacy concerns are at the forefront as the EU plans to scrutinize US-based social media apps for data privacy and safeguards against AI. Meanwhile, Info-Tech Research Group emphasizes the importance of privacy impact assessments for AI technologies. On the other hand, Microsoft’s health VP is taking on responsible AI, addressing potential biases in AI models. India has revised its approval stance on AI model launches, emphasizing the need to ensure content is free of bias and discrimination. The conversation around building fairness into AI continues, highlighting the crucial and challenging nature of this task, with a focus on the influence of privacy on AI fairness.

Articles we found interesting:

1. Building fairness into AI is crucial – and hard to get right - The Conversation link Highlight: … privacy can significantly influence the fairness of AI systems. For … Artificial intelligence (AI) · Technology · Fairness · Algorithmic bias …
2. Privacy in the Age of AI: Insights on Implementing Ethical Practices Published by Info-Tech … link Highlight: Info-Tech Research Group's latest research highlights the critical importance of performing privacy impact assessments (PIAs) for AI technologies …
3. After TikTok, EU to scrutinise US-based social media apps for data privacy, safeguards against AI link Highlight: With the US on the verge of either banning TikTok or forcing ByteDance to sell its stake, EU may start investigating American social media …
4. Microsoft's health VP takes on responsible AI - Healthcare Dive link Highlight: … AI amid thorny questions about the models' potential for mistakes and bias. Last week Microsoft — along with 16 health systems and two healthcare …
5. India revises approval stance on AI model launches - ReadWrite link Highlight: The letter outlined the need to adhere to existing Indian law and the imperative for the generated content to be free of bias, discrimination and any …
6. The need for responsible AI - ASPI The Strategist link Highlight: And first up is bias mitigation: AI models should be designed with care to avoid unfair or discriminatory outcomes. Transparency and …

Updated Everyday by: (Supriti Vijay & Aman Priyanshu)