From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gap

arXiv:2404.13131v1 Announce Type: cross
Abstract: 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 …

abstract accountability ai ethics arxiv attention bridge change claim community cs.ai cs.cy cs.lg ethics focus gap improving machine machine learning performance research responsibility transparency type