Establishing Trust Between Humans and AI
As AI becomes more capable, building mutual trust is crucial. We look at ideas like transparency and value alignment.
Business
Facial Recognition AI Sparks Debate on Privacy Rules
Facial recognition AI raises concerns balanced by the need for security, fueling debate over privacy protections.
Exposing Hidden Biases: The Key to Equality
Uncover how implicit biases shape our judgments and actions – and what we must do to override them.
Correcting Imbalanced Datasets: A Key to Fair and Effective AI
Imbalanced training data fuels unfair AI – here’s why oversampling, undersampling and more help.
Automated Systems: Who’s Really in Control?
Investigate how automated systems can discriminate due to data biases – and how to make them more ethical.
The Ethical AI Professional: Nurturing Responsible Talent
Discover how to instill a strong sense of ethics and responsibility in your AI talent pool.
Who’s Responsible for Biased AI Systems?
AI systems can unintentionally perpetuate harmful biases. We discuss ethical frameworks to hold organizations accountable for unfair AI.
Who’s Accountable? Addressing Liability Gaps in AI Systems
AI systems can cause unintentional harm, but legal liability is unclear. This article examines accountability gaps and potential solutions.
Corporate AI Labs: Innovation at Scale
Explore how large corporations are fostering AI innovation through in-house labs and startup collaborations.
Unlocking the Future: 7 Strategies for Effectively Managing AI Risks
Artificial Intelligence (AI) is reshaping industries, economies, and societies at an unprecedented pace. From autonomous vehicles and healthcare diagnostics to financial algorithms and smart cities, AI is pushing the boundaries of what technology can achieve. However, as the integration of AI accelerates, so do the risks associated with its use. Misaligned algorithms, unintended biases, privacy…