This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Who is responsible when AI mistakes in healthcare cause accidents, injuries or worse? Depending on the situation, it could be the AIdeveloper, a healthcare professional or even the patient. Liability is an increasingly complex and serious concern as AI becomes more common in healthcare. Not necessarily.
This shift raises critical questions about the transparency, safety, and ethical implications of AI systems evolving beyond human understanding. This article delves into the hidden risks of AI's progression, focusing on the challenges posed by DeepSeek R1 and its broader impact on the future of AIdevelopment.
This week, we are diving into some very interesting resources on the AI ‘blackbox problem’, interpretability, and AI decision-making. Parallely, we also dive into Anthropic’s new framework for assessing the risk of AImodels sabotaging human efforts to control and evaluate them. Enjoy the read!
The adoption of Artificial Intelligence (AI) has increased rapidly across domains such as healthcare, finance, and legal systems. However, this surge in AI usage has raised concerns about transparency and accountability. Composite AI is a cutting-edge approach to holistically tackling complex business problems.
This is a promising shift for AIdevelopers, and many organizations have realized impressive benefits from the technology, but it also comes with significant risks. AI’s rapid growth could lead more companies to implement it without fully understanding how to manage it safely and ethically.
Principles of Explainable AI( Source ) Imagine a world where artificial intelligence (AI) not only makes decisions but also explains them as clearly as a human expert. This isn’t a scene from a sci-fi movie; it’s the emerging reality of Explainable AI (XAI). Present the model’s predictions to stakeholders.
We organize all of the trending information in your field so you don't have to. Join 15,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content