{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T15:42:42Z","timestamp":1785512562174,"version":"3.56.0"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"20","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Inequality measures such as the Gini coefficient are used to inform and motivate policymaking, and are increasingly applied to digital platforms.\nWe analyze how measures fare in pseudonymous settings that are common in the digital age.\nOne key challenge of such environments is the ability of actors to create fake identities under fictitious false names, also known as ``Sybils.''\nWhile some actors may do so to preserve their privacy, we show that this can hamper inequality measurements: it is impossible for measures satisfying the literature's canonical set of desired properties to assess the inequality of an economy that may harbor Sybils.\nWe characterize the class of all Sybil-proof measures, and prove that they must satisfy relaxed version of the aforementioned properties.\nFurthermore, we show that the structure imposed restricts the ability to assess inequality at a fine-grained level.\nWe then apply our results to prove that popular measures are not Sybil-proof, with the famous Gini coefficient being but one example out of many.\nFinally, we examine dynamics leading to the creation of Sybils in digital and traditional settings.<\/jats:p>","DOI":"10.1609\/aaai.v40i20.38781","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:47:45Z","timestamp":1773794865000},"page":"17293-17301","source":"Crossref","is-referenced-by-count":2,"title":["Inequality in the Age of Pseudonymity"],"prefix":"10.1609","volume":"40","author":[{"given":"Aviv","family":"Yaish","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nir","family":"Chemaya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dahlia","family":"Malkhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin William","family":"Cong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38781\/42743","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38781\/42743","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:47:46Z","timestamp":1773794866000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38781"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i20.38781","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}