Underneath the surface of the buzz and excitement surrounding AI research, lurks a nagging issue that concerns its reproducibility and replicability- the ability of researchers to replicate their findings. Without this essential feature, the integrity of AI research is at stake, and it becomes difficult to establish credibility in this evolving field. In other words, that groundbreaking AI model you read about might be more hype than substance. However, a systematic process of validation can address these concerns, reduce ambiguity and make AI research more trustworthy.
AI reproducibility Vs. replicability- What’s the Difference?
Reproducibility means that with the same starting data set, other researchers can reproduce the same results with the same code or algorithms. It involves the ability to precisely recreate the same analysis from scratch by following similar methods or adopting similar code. Replicability, on the other hand, refers to the ability of researchers to achieve similar or identical results but starting with different data, hardware, software, or tools. Replicability provides evidence of the robustness of an AI model, given its ability to generate comparable results under different circumstances.
Why is it essential to Replicate results?
Replicability validates the claims researchers make, and publishing replicable results helps in constructing a stronger case for a new discovery. AI systems are complex, and the absence of replicability casts doubts on the credibility of a proposed model or method. Simply put, if a new AI algorithm or built is not replicable, it is not trustworthy or highly useful as one cannot rely on obtaining consistent and reliable results. In addition, reproducibility of results allows for easier monitoring of mistakes or inaccuracies in the underlying data set, code or analysis techniques. This way, AI researchers can continually evaluate and improve the model, thereby ensuring the reliability of the results.
What are the challenges to AI Reproducibility and Replicability?
Replicating AI results is a task that is not as easy as it sounds. Unlike traditional scientific research, AI research is plagued by unique challenges that make replicating results a daunting task. Here are some of the significant challenges that AI researchers face:
Lack of Standardization
There is no standardization in the way AI researchers document their research, leading to ambiguity in their methods, which makes it challenging for other researchers to follow and reproduce the same results. Since no two AI systems are alike, using different analytical tools or coding techniques can produce drastically different results. Even small variations in the data, pre-processing steps or analysis techniques, can yield vastly different outcomes, and without the procedures being documented and standardized, it will be impossible to verify claims.
Reproducibility in Big AI systems
Reproducibility of big AI systems is a significant challenge, given the scale and complexity of the models. Large AI systems often involve multiple teams, multiple data sets and multiple stages of development, making it challenging to reproduce results along with the many steps involved in building, training and testing an AI system. It is often hard to officially define the beginning or end, thus also complex when trying to compare results.
Validation and Error Estimation
AI systems often require validation and error estimation after training the model, and how AI researchers conduct these steps can lead to different results. If one uses their method for validating and error estimation, the results obtained may not be the same compared to another researcher who used a different method. There is a need for a standard method.
Data Privacy and Access
Data is the lifeblood of AI models, and without it, AI systems would not exist. AI researchers require access to huge volumes of high-quality data to ensure their models perform optimally. However, data privacy and access can prove problematic for researchers, especially when the data is sensitive or private. Even when data is publicly available, it may have limitations on how it can be used, thus affecting the results.
How Can Researchers Improve Reproducibility and Replicability in AI?
AI researchers are becoming aware of the need to reproduce and replicate their results to create more trustworthy outcomes. Here are some strategies that AI researchers can use to enhance the reproducibility and replicability of AI research:
Standardization of Processes
On top of critical frameworks used in AI research, there is a need for a standardized and consistent approach to documenting the method and steps being used to build and train AI systems. This approach ensures that other researchers can recreate the same model from the documentation, thereby making the results more reproducible and easier to validate.
Centralization of Methods and Data
Creating a centralized repository for sharing code, datasets and permissions can aid in replication and improve reproducibility. This way, researchers can have access to essential data and code, which can help in data comparison as well as tracking modifications and changes.
Sharing of Pre-Trained Models
Sharing pre-trained models can also provide an excellent starting point for researchers to build their models from. If the pre-trained model is shared with the code, and data used in the original study, this will lead to an increase in the likelihood of the model being successfully replicated.
Reproducibility Checklist and Automation
Creating automated reproducibility checks, comparing outcome data sets and error estimation can help in ensuring reproducibility. Implementing these checks can reduce the risk of errors and validate findings easier.
Conclusion
AI researchers recognize the importance of reproducibility and replicability in research for its credibility and effectiveness. It ensures the results are trustworthy and establishes a robust foundation for further research. Standardizing the methods and the process of documenting is one significant step towards ensuring better AI research. In addition, sharing data and models can serve as a launching point for researchers who want to replicate or use the same model in another application. With continued efforts put to standardization of AI research, we can help increase the trust in AI models, lend better understanding about the capacity of machine learning algorithms and lead to more reliable results from AI research.
References
Perkel, J. (2018) The trouble with reproducibility [online] Available at: https://www.nature.com/articles/d41586-018-06073-0 [Accessed 8th June 2021]
Gerstein, M. (2021) REPRODUCIBILITY IN ARTIFICIAL INTELLIGENCE [online] Available at: https://crosstalk.cell.com/blog/reproducibility-in-artificial-intelligence [Accessed 8th June 2021]