Explainability has moved from an academic debate to a significant barrier to AI adoption. A slew of new tools and approaches are intended to address this problem - but will they close the explainability gap?
We've established that unethical AI hurts protected classes - but it doesn't stop there. Across industries and regions, unethical AI can impact the entire population. Here's some questions to consider.
We've been trying to teach "ethics" for years. Teaching AI ethics to organizations is proving to be just as problematic. Yet as the urgency of ethical AI increases, we need a way forward. What are the options?
Is the current obsession with AI Ethics doing any good? Maybe Asimov's Three Laws of Robotics wasn't such a great starting point after all
With the possibility of serious negative consequences springing directly from AI projects, there needs to be more focus and discussion around ensuring ethical standards are upheld.
Those who laud the potential benefits of AI in healthcare are too often silent on the risk of exacerbating the healthcare system's current failings
Airplanes don't flap their wings like birds, and artificial general intelligence (AGI) will never think like the human brain, which is more complex than we imagine
The question of AI ethics and bias remains a potent one - but are we framing these issues in the right way? A better approach would be centered around AI fairness. But can fairness be monitored?
AI explainability remains an important preoccupation - enough so to earn the shiny acronym of XAI. There are notable developments in AI explainability and interpretability to assess. How much progress have we made?
Data brokers and personal data collection continues to cross ethical lines. But there are bright spots - including supply chain data sharing startup Aperity. I talked with their CEO about how their approach is different, and why AI and machine learning play a crucial data processing role.
When model drift becomes a deluge - the Coronavirus pandemic wreaks havoc with data science and ML models
All predictive models are wrong - but some are very wrong. How did we wind up in the predicament of flawed Machine Learning models, just when we need them the most?
The technical realities of functional quantum computers - is Google’s ten-year plan for Quantum Computing viable?
Google's quantum computing exploits have garnered plenty of attention in recent months. But how might they apply this commercially? A contrasting technical view comes by way of Itamar Sivan, CEO of Quantum Machines.