Beyond Benchmarks: The Qualitative Imperative in Human-Centered AI Evaluation
Fuente:
Zenodo
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Recurso digital |
| Publicado: |
Zenodo
2025
|
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866901155070607360 |
|---|---|
| author | Revista, Zen IA, 10 |
| author_facet | Revista, Zen IA, 10 |
| contents | The burgeoning field of Artificial Intelligence (AI) has seen remarkable progress, largely driven by advancements in quantitative performance metrics and benchmark datasets. While these metrics are invaluable for measuring technical capabilities such as accuracy, speed, and efficiency, they often fall short in capturing the intricate and nuanced aspects of AI's interaction with human users and its societal impact. This paper argues for a qualitative imperative in human-centered AI evaluation, proposing that a sole reliance on quantitative benchmarks creates a significant gap in understanding user experience, ethical implications, fairness, transparency, and overall societal acceptance. We contend that a holistic evaluation framework must integrate rigorous qualitative methodologies to uncover deep insights into how AI systems are perceived, used, and integrated into complex human contexts. This approach moves beyond simple performance scores to explore the subjective, experiential, and contextual factors that define truly successful and responsible AI. We review the limitations of current benchmark-driven evaluations, survey established qualitative research methods adaptable to AI, and outline a framework for their systematic integration, emphasizing the need for interdisciplinary collaboration to foster the development of AI that is not only powerful but also trustworthy, equitable, and genuinely beneficial to humanity. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17828779 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Beyond Benchmarks: The Qualitative Imperative in Human-Centered AI Evaluation Revista, Zen IA, 10 The burgeoning field of Artificial Intelligence (AI) has seen remarkable progress, largely driven by advancements in quantitative performance metrics and benchmark datasets. While these metrics are invaluable for measuring technical capabilities such as accuracy, speed, and efficiency, they often fall short in capturing the intricate and nuanced aspects of AI's interaction with human users and its societal impact. This paper argues for a qualitative imperative in human-centered AI evaluation, proposing that a sole reliance on quantitative benchmarks creates a significant gap in understanding user experience, ethical implications, fairness, transparency, and overall societal acceptance. We contend that a holistic evaluation framework must integrate rigorous qualitative methodologies to uncover deep insights into how AI systems are perceived, used, and integrated into complex human contexts. This approach moves beyond simple performance scores to explore the subjective, experiential, and contextual factors that define truly successful and responsible AI. We review the limitations of current benchmark-driven evaluations, survey established qualitative research methods adaptable to AI, and outline a framework for their systematic integration, emphasizing the need for interdisciplinary collaboration to foster the development of AI that is not only powerful but also trustworthy, equitable, and genuinely beneficial to humanity. |
| title | Beyond Benchmarks: The Qualitative Imperative in Human-Centered AI Evaluation |
| url | https://doi.org/10.5281/zenodo.17828779 |