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For algorithmic systems to support human decision-making effectively, people must be willing to use them. Yet, prior work suggests that accuracy and privacy concerns may both deter potential users and limit the efficacy of these systems.<\/jats:p><jats:p>We expand upon prior research by empirically modeling how accuracy and privacy influence intent to adopt algorithmic systems. We focus on an algorithmic system designed to aid people in a globally-relevant decision context with tangible consequences: the COVID-19 pandemic. We use statistical techniques to analyze surveys of 4,615 Americans to (1) evaluate the effect of both accuracy and privacy concerns on reported willingness to install COVID-19 apps; (2) examine how different groups of users weigh accuracy relative to privacy; and (3) we empirically develop the first statistical models, to our knowledge, of how the<jats:italic>amount<\/jats:italic>of benefit (e.g., error rate) and<jats:italic>degree<\/jats:italic>of privacy risk in a data-driven decision aid may influence willingness to adopt.<\/jats:p>","DOI":"10.1145\/3488307","type":"journal-article","created":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T16:36:22Z","timestamp":1635525382000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["How Good is Good Enough? Quantifying the Impact of Benefits, Accuracy, and Privacy on Willingness to Adopt COVID-19 Decision Aids"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8886-4748","authenticated-orcid":false,"given":"Gabriel","family":"Kaptchuk","sequence":"first","affiliation":[{"name":"Boston University, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel G.","family":"Goldstein","sequence":"additional","affiliation":[{"name":"Microsoft Research, New York, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eszter","family":"Hargittai","sequence":"additional","affiliation":[{"name":"University of Zurich, Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jake M.","family":"Hofman","sequence":"additional","affiliation":[{"name":"Microsoft Research, New York, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elissa M.","family":"Redmiles","sequence":"additional","affiliation":[{"name":"Max Plank Institute for Software Systems, Saarbr\u00fccken, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,3,26]]},"reference":[{"issue":"1","key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1037\/0033-2909.97.1.129","article-title":"A variance explanation paradox: When a little is a lot","volume":"97","author":"Abelson Robert P.","year":"1985","unstructured":"Robert P. 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