Two goals - improving replicability and accountability of Machine Learning research respectively, have accrued much attention from the AI ethics and the Machine Learning community. Despite sharing the measures of improving transparency, the two goals are discussed in different registers - replicability registers with scientific reasoning whereas accountability registers with ethical reasoning. Given the existing challenge of the Responsibility Gap - holding Machine Learning scientists accountable for Machine Learning harms due to them being far from sites of application, this paper (...) posits that reconceptualizing replicability can help bridge the gap. Through a shift from model performance replicability to claim replicability, Machine Learning scientists can be held accountable for producing non-replicable claims that are prone to eliciting harm due to misuse and misinterpretation. In this paper, I make the following contributions. First, I define and distinguish two forms of replicability for ML research that can aid constructive conversations around replicability. Second, I formulate an argument for claim-replicability's advantage over model performance replicability in justifying assigning accountability to Machine Learning scientists for producing non-replicable claims and show how it enacts a sense of responsibility that is actionable. In addition, I characterize the implementation of claim replicability as more of a social project than a technical one by discussing its competing epistemological principles, practical implications on Circulating Reference, Interpretative Labor, and research communication. (shrink)

Social Networking Sites (SNS) are the bread and butter of our digital lives, but research has shown SNS usage to lead to higher levels of Fear of Missing Out (FOMO) and lower levels of subjective wellbeing (Przybilski, Murayama, Dehaan, & Gladwell, 2013). Existing psychological theories explain this causal relationship as an outcome of either an innate need for relatedness (Self Determination Theory) or behavioral addiction (SNS Addiction). Theoretically, these theories also posit that individuals with a greater number of SNS (...) accounts are also more likely to experience FOMO and in turn lower levels of subjective wellbeing. The aim of this paper is to investigate this hypothesis, through a post hoc analysis of data previously obtained for a college project. Our sample consisted of 181 participants (Males = 57, Females = 123, Others = 1) with the mean age being 21.51 (SD = 1.76). Results show FOMO to completely indirectly mediate the relationship between number of SNS accounts used and subjective wellbeing (effect = -.20, SE = .10, CI = -.43 to -.03); supporting our hypothesis. Future directions for research and interventions are discussed. (shrink)