2024-02-20 One-Minute Post

Welcome to One-Minute-AI-Fairness-And-Privacy, where SEC Chair Gensler highlights AI amplifying biases and flawed decision-making. Meanwhile, AI’s ubiquity raises concerns about data accuracy and bias protection. Mosaic Data Science supports NIST’s AI Safety Institute Consortium to mitigate biases and risks in AI applications through bias auditing and risk management. Fujitsu implements AI Ethics for Fairness to test AI model fairness, while Beyond Limits redefines AI sports coaching with a focus on ethical considerations and fairness. Additionally, efforts to empower women in AI aim to combat bias and drive innovation for robust and fair AI systems. Stay tuned for more updates on AI privacy and fairness!

Articles we found interesting:

1. SEC Chair Gensler weighs in on AI risks and SEC's positioning - JD Supra link Highlight: However, he highlighted that the use of AI amplifies many issues, noting how AI models can be flawed in making decisions, propagating biases, and …
2. AI will be everywhere in 2024 - Inquirer Business link Highlight: Artificial intelligence (AI) has become ubiquitous and has become a key … bias or data accuracy. Organizations, thus, must commit to protecting …
3. Mosaic Data Science Supports NIST's U.S. AI Safety Institute Consortium | Newswire link Highlight: … AI technology to mitigate potential biases and risks in AI applications. … bias auditing and risk management engagements. Mosaic recommends …
4. Fujitsu Data Science Interview and Hiring Process in 2024 - Analytics India Magazine link Highlight: … AI Ethics for Fairness to test AI model fairness, and Fujitsu Wide Learning for simulating scientific discovery processes. The aim is to …
5. Beyond Limits: AI Sports Coaching Redefined - AutoGPT Official link Highlight: … AI in Injury Prevention and Recovery in Sports. Ethical Considerations and Fairness in AI Sports Coaching. Ethical concerns surrounding AI in sports …
6. Empowering Women in AI: Combating Bias and Driving Innovation - Medriva link Highlight: This is not only beneficial for the end-users of these technologies but also for the robustness and fairness of the AI systems themselves.

Updated Everyday by: (Supriti Vijay & Aman Priyanshu)