Leveling the Playing Field: The Push for Open AI Evaluation

Author: Denis Avetisyan

A new perspective argues that truly useful AI benchmarking requires accessible tools and standardized practices for reliable performance assessment.

This review advocates for democratizing AI benchmarking through reproducible workload characterization, standardized metrics, and adherence to FAIR principles for responsible AI development.

While AI benchmarks are crucial for tracking progress, their increasing complexity often creates a disconnect between reported performance and real-world applicability. This paper, ‘AI Benchmark Democratization and Carpentry’, argues that truly useful evaluation requires moving beyond static assessments towards dynamic, accessible, and reproducible practices. We demonstrate that overcoming barriers-including resource demands and a lack of specialized expertise-is essential for democratizing benchmark design and use. Can a community-driven approach to ‘AI Benchmark Carpentry’ foster the sustained expertise needed to ensure responsible and equitable AI development and deployment?

The Inevitable Burden of Measurement

The escalating sophistication of artificial intelligence necessitates the development of robust, standardized evaluation methods. As AI transitions from narrowly defined tasks to more generalized problem-solving, relying on ad-hoc testing or isolated benchmarks becomes increasingly insufficient to guarantee dependable performance. Rigorous evaluation isn’t simply about confirming whether an AI works; it’s about comprehensively understanding how it works, identifying potential failure points, and pinpointing areas where further refinement is crucial. Without such standards, subtle biases or unexpected behaviors can remain undetected, hindering progress and eroding trust in these increasingly powerful systems. This need extends beyond academic research, becoming paramount for responsible deployment in critical applications like healthcare, finance, and autonomous vehicles, where reliability and predictability are non-negotiable.

Current methods for assessing artificial intelligence frequently fall short in capturing the subtleties of increasingly sophisticated systems. Traditional benchmarks often prioritize narrow tasks, overlooking crucial aspects like adaptability, common sense reasoning, and robustness to unexpected inputs. This limited scope hinders genuine progress, as improvements on these simplified tests don’t necessarily translate to real-world performance. Consequently, a lack of comprehensive evaluation fosters distrust in AI capabilities, impeding its responsible deployment and widespread acceptance. Without the ability to thoroughly probe an AI’s understanding and limitations, stakeholders remain uncertain about its reliability, hindering innovation and potentially amplifying societal biases embedded within these complex algorithms.

The Ghosts in the Machine: Data Provenance

Reliable AI evaluation is fundamentally dependent on data integrity and provenance; therefore, a comprehensive record of data origins and all subsequent transformations is essential. This tracking should encompass data collection methods, any preprocessing steps applied – such as cleaning, normalization, or augmentation – and version control to ensure reproducibility. Maintaining this detailed history allows for verification of results, identification of potential biases introduced during data handling, and facilitates auditing of the entire evaluation pipeline. Without transparent provenance, claims made about AI system performance lack substantiation and hinder independent validation, impacting the trustworthiness of research and deployment.

The FAIR Principles – Findability, Accessibility, Interoperability, and Reusability – establish a framework for data management that maximizes the value and impact of research outputs. Findability is achieved through rich metadata and clear identification via persistent identifiers. Accessibility necessitates data be associated with clear usage licenses and deposited in repositories that adhere to open standards. Interoperability requires data to use common vocabularies and formats, facilitating integration with diverse analytical tools and systems. Finally, Reusability demands data be well-documented, versioned, and accompanied by information regarding provenance and limitations, enabling future research and validation of findings. Implementing these principles is critical for promoting collaborative science, accelerating discovery, and ensuring the long-term utility of research data.

The inability to trace the origins and modifications of datasets used in AI research – a lack of clear data provenance – presents significant challenges to result verification and reproducibility. Without detailed records of data collection, cleaning, and transformation processes, independent researchers cannot reliably validate findings or replicate experiments. This opacity hinders the identification of potential biases introduced during data preparation and limits the ability to assess the generalizability of AI models. Consequently, the credibility of AI research is diminished, impacting both scientific progress and the responsible deployment of AI systems, as stakeholders lack confidence in the robustness and reliability of reported outcomes.

The Illusion of Control: Simulation & Digital Twins

Simulation environments, and particularly the implementation of Digital Twins, facilitate comprehensive AI system testing by creating controlled, yet realistic, operational conditions. Digital Twins are virtual representations of physical assets, processes, or systems, allowing developers to subject AI algorithms to a diverse range of scenarios without the risks or costs associated with real-world deployment. These environments enable manipulation of variables, repeatable testing, and the isolation of specific functionalities for detailed analysis. By mirroring real-world complexities, simulations provide a high degree of fidelity for evaluating AI performance across numerous edge cases and validating system robustness prior to implementation.

Simulation environments facilitate the assessment of AI system performance under varied and configurable conditions, allowing developers to proactively identify potential failure points and performance bottlenecks. This preemptive evaluation encompasses testing against edge cases, unusual input data, and stressful operational loads that might not be immediately apparent during standard testing procedures. By exposing AI systems to a diverse range of scenarios before deployment, developers can refine algorithms, adjust parameters, and improve robustness, ultimately leading to more reliable and efficient AI applications. The process enables targeted improvements to address specific weaknesses, minimizing the risk of unexpected behavior or failures in real-world settings.

The integration of simulation environments with established AI benchmarks constitutes a rigorous methodology for verifying and validating AI system functionality. This approach allows for quantifiable assessment of performance characteristics across diverse operational scenarios. Recent computational advancements exemplify this synergy; comparative analysis reveals a 209x improvement in peak performance per energy unit between current high-performance computing systems, such as Frontier, and their predecessors like Jaguar. This substantial gain highlights the efficacy of combined simulation and benchmarking in driving improvements in AI system efficiency and capability, and provides a data-driven basis for system validation prior to real-world deployment.

The Fragile Ecosystem of Trust

The advancement of artificial intelligence relies heavily on collaborative efforts, and organizations like MLCommons are central to this progress. These groups establish standardized benchmarks – rigorous tests that allow for objective comparison of different AI systems – and curate publicly available datasets essential for training and validating new models. Beyond simply providing resources, MLCommons actively promotes open-source collaboration, enabling researchers and developers worldwide to build upon each other’s work and accelerate innovation. This shared infrastructure fosters transparency and reproducibility, critical components in building trust in increasingly complex AI technologies, and ensures that progress isn’t siloed within individual institutions or companies.

The advancement of artificial intelligence increasingly relies on access to substantial computational power, making facilities like the Leadership Computing Facility indispensable for meaningful progress. Large-scale simulations and evaluations, crucial for developing and refining AI models, demand resources far exceeding those available to most researchers or institutions. These high-performance computing centers provide the necessary infrastructure – powerful processors, extensive memory, and high-bandwidth networks – to train complex models, process massive datasets, and conduct rigorous testing. Without such resources, the development of cutting-edge AI would be severely constrained, hindering innovation in fields ranging from natural language processing to computer vision and beyond. The ability to efficiently run and analyze these computationally intensive tasks is therefore central to accelerating the field and ensuring the reliability and performance of future AI systems.

Advancing artificial intelligence evaluation relies heavily on sustained financial investment, as the computational demands of modern models are substantial. Training a single large-scale language model, such as GPT-3, now necessitates approximately 1,287 MWh of energy-a figure comparable to the yearly electrical consumption of roughly 130 U.S. homes. This energy expenditure underscores the need for both innovative evaluation methodologies that minimize computational load and continued support from funding agencies like the NSF and DOE to facilitate research into more efficient AI development and assessment techniques. Without such backing, progress in evaluating increasingly complex models becomes prohibitively expensive and environmentally impactful, potentially hindering the broader advancement of the field.

The Inevitable Imperfection of Measurement

The pursuit of transparent and reproducible AI research increasingly benefits from assistive technologies designed to enhance clarity and precision in scientific communication. Tools such as Grammarly and ChatGPT offer valuable support in refining documentation, checking for grammatical errors, and improving the overall readability of evaluation results. However, these technologies are not without limitations; they should be considered aids to, not replacements for, careful human review. Automated suggestions require critical assessment to ensure accuracy, contextual relevance, and the preservation of nuanced scientific meaning, particularly when reporting complex performance metrics or statistical analyses. A commitment to rigorous verification remains essential for maintaining the integrity and reliability of AI evaluation processes and fostering trust in research findings.

The development of reliable artificial intelligence hinges on the ability to accurately measure performance, a challenge addressed by initiatives like AI Benchmark Carpentry. This training program focuses on equipping practitioners with the skills to create, implement, and rigorously evaluate AI benchmarks – standardized tests that assess model capabilities. Recent analysis demonstrates the critical need for these standardized methodologies; performance variability on identical AI models, when tested across five state-of-the-art GPU clusters, averaged a significant 8%. This discrepancy underscores how subtle differences in hardware, software, and implementation can dramatically impact reported results, necessitating careful control and consistent evaluation protocols to ensure meaningful comparisons and accelerate progress in the field. Ultimately, a skilled workforce capable of designing and interpreting these benchmarks is essential for building trust and unlocking the full potential of AI systems.

The sustained advancement of artificial intelligence hinges not solely on algorithmic innovation, but critically on the development and implementation of rigorous evaluation methodologies. Without standardized and robust methods for assessing performance, biases, and limitations, the true capabilities of AI systems remain obscured, hindering progress and potentially leading to unreliable or harmful deployments. Continued investment in this area-encompassing benchmark creation, data curation, and the refinement of metrics-is therefore paramount. Such investment facilitates a deeper understanding of AI’s strengths and weaknesses, enabling researchers and developers to build more trustworthy, efficient, and beneficial systems, and ultimately unlocking the full potential of this transformative technology while mitigating associated risks.

The pursuit of standardized AI benchmarks, as detailed within this study, echoes a sentiment articulated by Donald Knuth: “Premature optimization is the root of all evil.” The drive for easily comparable performance metrics, while seemingly pragmatic, risks prioritizing quantifiable results over the nuanced characterization of workloads and a deep understanding of energy efficiency. A system built solely on achieving high scores, devoid of meticulous examination, is ultimately brittle. The paper correctly identifies the need for democratized access, but true progress necessitates a shift from merely measuring intelligence to cultivating an ecosystem where failures reveal the path towards genuine, adaptable systems-systems that, like living things, evolve rather than simply perform.

What’s Next?

The call for democratized AI benchmarks, though laudable, risks becoming another exercise in architectural hubris. It presumes a stable target, a fixed definition of ‘performance’ against which all systems can be measured. Yet, everything optimized will someday lose flexibility. The very act of standardizing invites a narrowing of focus, a privileging of easily quantified metrics over the subtle, emergent behaviors that may ultimately define intelligence. The pursuit of reproducible results, while essential, often conflates precision with understanding; a perfectly replicated failure is still a failure.

Scalability is just the word used to justify complexity. The field will inevitably find itself burdened by increasingly elaborate benchmarks – cathedrals of code built on shifting sands. Each attempt to capture the totality of ‘AI capability’ will reveal its own inherent limitations, its own blind spots. The illusion of objective measurement will persist, masking the fundamentally subjective nature of evaluation.

The perfect architecture is a myth to keep people sane. Perhaps the true next step lies not in building better benchmarks, but in accepting the inherent ephemerality of AI systems. To treat them not as fixed entities to be measured, but as evolving ecosystems, constantly adapting and redefining themselves. To focus less on what can be known, and more on anticipating what will inevitably be unknown.

Original article: https://arxiv.org/pdf/2512.11588.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/