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However, the decision-making logic of some widely used machine learning models, such as deep neural networks, is characterized by opacity, thereby rendering them exceedingly difficult for humans to understand and explain and, as a result, potentially risky to use. Considering the importance of addressing this opacity, this paper calls for research that studies empirically and theoretically how machine learning experts and users seek to attain machine learning explainability. Focusing on automated trading, we take steps in this direction by analyzing a trading firm\u2019s quest for explaining its deep neural network system\u2019s actionable predictions. We demonstrate that this explainability effort involves a particular form of human\u2013machine interaction that contains both anthropomorphic and technomorphic elements. We discuss this attempt to attain machine learning explainability in light of reflections on cross-species companionship and consider it an example of human\u2013machine companionship. <\/jats:p>","DOI":"10.1177\/20539517221111361","type":"journal-article","created":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T06:35:55Z","timestamp":1657089355000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":34,"title":["Toward a sociology of machine learning explainability: Human\u2013machine interaction in deep neural  network-based automated trading"],"prefix":"10.1177","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8217-5880","authenticated-orcid":false,"given":"Christian","family":"Borch","sequence":"first","affiliation":[{"name":"Department of Management, Politics and Philosophy, Copenhagen Business School, Frederiksberg, 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