In order to develop a more nuanced understanding of the different types of claims made in ML research and their intended audiences, it is essential to first categorize the types of claims typically found in ML papers. These can include model performance claims (related to generalizability and robustness of the model) and social claims (related to functionality, efficiency, fairness, societal benefits, etc.). By clearly delineating these categories, researchers can better identify the diverse nature of claims being made in ML research.
Furthermore, understanding the intended audiences for these claims is crucial. Model performance claims are often targeted towards the scientific community, including researchers, reviewers, and peers in the field. On the other hand, social claims may have a broader audience, including policymakers, industry stakeholders, end-users, and the general public. Recognizing the different audiences helps in tailoring the communication of claims to ensure clarity, relevance, and impact.
To evaluate the replicability and accountability of these claims, researchers can implement strategies such as:
Transparent Reporting: Clearly articulating the methodology, data sources, and assumptions behind each claim to facilitate replication and verification by the scientific community.
External Validation: Seeking feedback and validation from diverse stakeholders, including experts from relevant fields, impacted communities, and end-users, to ensure the claims are robust and applicable in real-world scenarios.
Contextual Analysis: Considering the social, ethical, and practical implications of the claims to assess their replicability and accountability beyond just numerical performance metrics.
By adopting a multidimensional approach that considers the nature of claims, their intended audiences, and the context in which they are made, researchers can enhance the evaluation of replicability and accountability in ML research.