Recent years have seen growing interest among both researchers and practitioners in user-
engaged approaches to algorithm auditing, which directly engage users in detecting …

Artificial intelligence (AI) presents new challenges for the user experience (UX) of products
and services. Recently, practitioner-facing resources and design guidelines have become …

The ongoing global race for bigger and better artificial intelligence (AI) systems is expected
to have a profound societal and environmental impact by altering job markets, disrupting …

Despite the widespread use of artificial intelligence (AI), designing user experiences (UX) for
AI-powered systems remains challenging. UX designers face hurdles understanding AI …

Technology companies continue to invest in efforts to incorporate responsibility in their
Artificial Intelligence (AI) advancements, while efforts to audit and regulate AI systems …

Machine learning models with high accuracy on test data can still produce systematic
failures, such as harmful biases and safety issues, when deployed in the real world. To …

An emerging body of research indicates that ineffective cross-functional collaboration–the
interdisciplinary work done by industry practitioners across roles–represents a major barrier …

Diversity in datasets is a key component to building responsible AI/ML. Despite this
recognition, we know little about the diversity among the annotators involved in data …

Numerous toolkits have been developed to support ethical AI development. However,
toolkits, like all tools, encode assumptions in their design about what work should be done …

“AI as a Service”(AIaaS) is a rapidly growing market, offering various plug-and-play AI
services and tools. AIaaS enables its customers (users)—who may lack the expertise, data …