In the rapidly evolving landscape of artificial intelligence (AI), the significance of competitions and benchmarks cannot be overstated. This book provides a comprehensive exploration of the role, design, and impact of AI challenges and benchmarks across academic, industrial, and educational domains. From historical perspectives and design principles to hands-on tutorials, the book offers an invaluable analysis of the organization and execution of AI competitions.

This book compiles insights from experienced challenge organizers, providing guidelines for the effective design of data-driven scientific competitions. The authors represent various institutions from academia, industry, and non-profit.

The book offers critical insights for researchers, engineers, and organizers to develop high impact competitions, through an exploration of dataset development, evaluation metrics, competition platforms, incentives, execution, and practical aspects. By addressing both theoretical and real-world considerations, this book serves as an essential guide for anyone looking to understand, participate in, or organize AI challenges and benchmarks.

Over the last 15 years, challenges in machine learning, data science, and artificial intelligence have proven to be effective and cost-efficient methods for rapidly bringing research solutions into industry. They have also emerged as a means to direct academic research, advance the state-of-the-art, and explore entirely new domains. Additionally, these challenges, with their engaging and playful nature, naturally attract students, making them an excellent educational resource. Finally, challenges act as a catalyst for community engagement by offering a structured and stimulating environment for individuals to collectively work towards a common goal.

This book addresses the gap in the literature on the theoretical foundations and optimization of challenge protocols, which has persisted despite the remarkable successes and progress achieved in challenge organization. It assembles leading experts in challenge organization to provide insights and directions for future research. It also provides a deeper understanding of challenge design, and introduces new methods and application domains for designing and implementing high-impact challenges that advance the frontiers of innovation.

How data science challenges engage the research community to turn large, complex datasets into reliable benchmarks and practical solutions for science and society.

Guidelines for designing challenges as structured projects, from planning and rules to execution and post-analysis, aimed at solving real problems and advancing science.

A framework for building reliable datasets, covering requirements, design, implementation, evaluation, distribution, and maintenance to avoid risks and ensure practical use.

Methods to reduce uncertainty in competition judging through metrics, test data design, error bars, phases, and score aggregation.

Post-challenge activities and templates for turning results into papers and benchmarks that ensure lasting impact.

Survey of academic competitions, their goals, major achievements, and impact on advancing research across domains.

Survey of competitions in industry. Use real-world problems to drive innovation, benchmark solutions, and identify talent for research and product development.

Competitions as a hands-on learning tool, motivating students and professionals to gain skills, explore real problems, and stay up to date.

An overview of AI benchmarks, from their history and applications to the challenges of reproducibility and the need for robust infrastructure.