What Human-Horse Interactions may Teach us About Effective Human-AI Interactions

This article explores human-horse interactions as a metaphor for understanding and designing effective human-AI partnerships. Drawing on the long history of human collaboration with horses, we propose that AI, like horses, should complement rather than replace human capabilities. We move beyond traditional benchmarks such as the Turing test, which emphasize AI's ability to mimic human intelligence, and instead advocate for a symbiotic relationship where distinct intelligences enhance each other. We analyze key elements of human-horse relationships: trust, communication, and mutual adaptability, to highlight essential principles for human-AI collaboration. Trust is critical in both partnerships, built through predictability and shared understanding, while communication and feedback loops foster mutual adaptability. We further discuss the importance of taming and habituation in shaping these interactions, likening it to how humans train AI to perform reliably and ethically in real-world settings. The article also addresses the asymmetry of responsibility, where humans ultimately bear the greater burden of oversight and ethical judgment. Finally, we emphasize that long-term commitment and continuous learning are vital in both human-horse and human-AI relationships, as ongoing interaction refines the partnership and increases mutual adaptability. By drawing on these insights from human-horse interactions, we offer a vision for designing AI systems that are trustworthy, adaptable, and capable of fostering symbiotic human-AI partnerships.

The Principles of Data-Centric AI (DCAI)

Data is a crucial infrastructure to how artificial intelligence (AI) systems learn. However, these systems to date have been largely model-centric, putting a premium on the model at the expense of the data quality. Data quality issues beset the performance of AI systems, particularly in downstream deployments and in real-world applications. Data-centric AI (DCAI) as an emerging concept brings data, its quality and its dynamism to the forefront in considerations of AI systems through an iterative and systematic approach. As one of the first overviews, this article brings together data-centric perspectives and concepts to outline the foundations of DCAI. It specifically formulates six guiding principles for researchers and practitioners and gives direction for future advancement of DCAI.