{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:50:31Z","timestamp":1773802231756,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"17","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>We study a general framework of optimization with the aim to compute fair solutions in settings with a set of agents whose valuations are combined using an aggregation function. The strength of our framework lies (1) in its generality and (2) in the fact that we leverage the power of ex-ante fairness, a concept that has recently gained much attention in the scope of fair allocation and fairness in AI in general. More precisely, in our setting there are n set functions f\u2081, \u2026, f\u2099 (e.g., the valuation functions of n agents) that are combined using an aggregation function g (e.g., the minimum, Nash social welfare, p-norm). The power of ex-ante fairness is obtained by allowing as a feasible solution not simply a finite set S, but instead a distribution \u03a0 over feasible sets.\nThe goal in our setting is then to find a probability distribution p in \u03a0 that maximizes the value resulting from aggregating (using g) the n expected values of the functions f\u2081, \u2026, f\u2099 obtained when sampling a set S according to the distribution p. We stress that this is different from maximizing the expected value of g (ex-post fairness) and typically allows for much fairer solutions. We give three different greedy algorithms for three different settings of this framework and prove that they achieve constant approximation guarantees under certain realistic assumptions. For some of the settings, we show that these approximation guarantees are tight. Specific scenarios that can be modelled using our framework include fair information diffusion in social networks, fair submodular matching problems, and ex-ante versions of item assignment problems.<\/jats:p>","DOI":"10.1609\/aaai.v40i17.38428","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:30:10Z","timestamp":1773793810000},"page":"14157-14165","source":"Crossref","is-referenced-by-count":0,"title":["Greedily Maximizing Ex-Ante Fairness"],"prefix":"10.1609","volume":"40","author":[{"given":"Ruben","family":"Becker","sequence":"first","affiliation":[]},{"given":"Bojana","family":"Kodric","sequence":"additional","affiliation":[]},{"given":"Cosimo","family":"Vinci","sequence":"additional","affiliation":[]}],"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\/38428\/42390","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38428\/42390","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:30:10Z","timestamp":1773793810000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38428"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i17.38428","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]]}}}