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.
Building Inclusive AI: Representation and Accessibility
For AI to benefit society, it must work well for diverse populations. We discuss ideas to bake inclusiveness into AI from the start.
Corporate AI Labs: Innovation at Scale
Explore how large corporations are fostering AI innovation through in-house labs and startup collaborations.
The Ethical AI Professional: Nurturing Responsible Talent
Discover how to instill a strong sense of ethics and responsibility in your AI talent pool.
AI Guards Personal Data While Enabling Innovation
Responsible AI techniques allow personalized services while keeping user data private, secure, and in context.
AI in the Government – Who Keeps it Accountable?
How can governments implement oversight for public sector AI systems? We review policy options and open questions.
Teaching AI Systems Right from Wrong: The Business of Ethical AI
Can we develop AI with a moral compass? We look at new techniques for aligning AI goals with ethical values.
Automated Systems: Who’s Really in Control?
Investigate how automated systems can discriminate due to data biases – and how to make them more ethical.
Correcting Imbalanced Datasets: A Key to Fair and Effective AI
Imbalanced training data fuels unfair AI – here’s why oversampling, undersampling and more help.
Exposing Hidden Biases: The Key to Equality
Uncover how implicit biases shape our judgments and actions – and what we must do to override them.