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Fairness of Exposure in Rankings

Ashudeep Singh, Thorsten Joachims
2018 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '18  
As part of this framework, we develop efficient algorithms for finding rankings that maximize the utility for the user while provably satisfying a specifiable notion of fairness.  ...  To address these often conflicting responsibilities, we propose a conceptual and computational framework that allows the formulation of fairness constraints on rankings in terms of exposure allocation.  ...  First, this paper draws on concepts for algorithmic fairness of supervised learning in the presence of sensitive attributes. Second, we relate to prior work on algorithmic fairness for rankings.  ... 
doi:10.1145/3219819.3220088 dblp:conf/kdd/SinghJ18 fatcat:vhb7gciumfeudk4sjrovcxmibi

Probabilistic Machine Learning for Healthcare [article]

Irene Y. Chen, Shalmali Joshi, Marzyeh Ghassemi, Rajesh Ranganath
2020 arXiv   pre-print
Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning.  ...  Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare.  ...  ACKNOWLEDGMENTS The authors thank Noémie Elhadad, Rahul G. Krishnan, Peter Schulam, and Pete Szolovits for helpful and useful feedback.  ... 
arXiv:2009.11087v1 fatcat:htosfeqvhndvfmlmud2pvl3nsy

Pooling of Causal Models under Counterfactual Fairness via Causal Judgement Aggregation [article]

Fabio Massimo Zennaro, Magdalena Ivanovska
2018 arXiv   pre-print
We propose two simple algorithms, grounded in the theory of counterfactual fairness and causal judgment aggregation, that are guaranteed to generate aggregated probabilistic causal models respecting the  ...  We build upon the work on causal models and fairness in machine learning, and we express the problem of combining multiple models within the framework of opinion pooling.  ...  two complementary algorithms for performing aggregation of probabilistic causal models under counterfactual fairness; finally, Section 3.3 offers an illustration of the use of our algorithms on a toy  ... 
arXiv:1805.09866v2 fatcat:bizzwkipgrf3bpfhd5d7j25eei

Parameterized Verification by Probabilistic Abstraction [chapter]

Amir Pnueli, Lenore Zuck
2003 Lecture Notes in Computer Science  
The utility of the approach of γ-fairness with network invariants is demonstrated on Lehman and Rabin's Courteous Philosophers algorithm.  ...  The utility of the Planner approach is demonstrated on a probabilistic mutual exclusion protocol.  ...  Algorithm RESPONSE for model-checking the P-validity of ϕ : ¼ (a → ½ b) The main difference between the algorithm in Fig. 2 and its counterpart in [KPR98] is the treatment of probabilistic requirements  ... 
doi:10.1007/3-540-36576-1_6 fatcat:g67z7waebbflrnrgsammyqnmta

The 10,000 Facets of MDP Model Checking [chapter]

Christel Baier, Holger Hermanns, Joost-Pieter Katoen
2019 Lecture Notes in Computer Science  
This paper presents a retrospective view on probabilistic model checking. We focus on Markov decision processes (MDPs, for short).  ...  We discuss in particular the manifold facets of this field of research by surveying the verification of various MDP extensions, rich classes of properties, and their applications.  ...  It is impossible to give a complete treatment of all works and developments on MDP model checking; this paper reflects the main directions and achievements from the perspective of the authors.  ... 
doi:10.1007/978-3-319-91908-9_21 fatcat:yjsuwb5ibjff3cq3niatu6sbxq

Fairness in Machine Learning with Tractable Models [article]

Michael Varley, Vaishak Belle
2020 arXiv   pre-print
Many definitions have been proposed in the literature, but the fundamental task of reasoning about probabilistic events is a challenging one, owing to the intractability of inference.  ...  We will also motivate the concept of "fairness through percentile equivalence", a new definition predicated on the notion that individuals at the same percentile of their respective distributions should  ...  Nonetheless, it is extremely simple to guarantee that an algorithm is fair in this sense.  ... 
arXiv:1905.07026v2 fatcat:54hupbxitfby5f4np47zvaqrtu

The perils of artificial intelligence in healthcare: Disease diagnosis and treatment
English

C. Lee Jung
2019 Journal of Computational Biology and Bioinformatics Research  
This review addresses the uncertainty of AI applications to disease diagnosis and treatment, not only pinpointing AI's inherent algorithmic problems in dealing with non-patternable stochastic healthcare  ...  For the past decade, artificial intelligence (AI) and its related technologies have made remarkable advances in marketing and business solutions based on AI-driven big data analysis of customer queries  ...  These together speak of a mortifying reminder of the risk of AI in disease diagnosis and treatment, which must roll out accurate, fair, and trustworthy decisions.  ... 
doi:10.5897/jcbbr2019.0122 fatcat:6op6efqpfjbvrbekbm76qwu4uq

Page 3831 of Mathematical Reviews Vol. , Issue 87g [page]

1987 Mathematical Reviews  
Pnueli, On the ex- tremely fair treatment of probabilistic algorithms (pp. 278-290); D. Kozen, A probabilistic PDL (pp. 291-297); Y. A.  ...  Guibas, Finding extremal polygons (pp. 282- 289); Eric Bach, Fast algorithms under the extended Riemann hypothesis: a concrete estimate (pp. 290-295); Hong Zhu and Robert Sedgewick, Notes on merging networks  ... 

Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns

YooJung Choi, Golnoosh Farnadi, Behrouz Babaki, Guy Van den Broeck
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
In particular, we introduce the notion of a discrimination pattern, which refers to an individual receiving different classifications depending on whether some sensitive attributes were observed.  ...  As machine learning is increasingly used to make real-world decisions, recent research efforts aim to define and ensure fairness in algorithmic decision making.  ...  The algorithmic fairness literature has proposed various solutions, from limiting the disparate treatment of similar individuals to giving statistical guarantees on how classifiers behave towards different  ... 
doi:10.1609/aaai.v34i06.6565 fatcat:qe4er5ksuraovhh5blcav4dkhe

Quantitative program logic and expected time bounds in probabilistic distributed algorithms

A.K. McIver
2002 Theoretical Computer Science  
common to probabilistic distributed algorithms.  ...  We illustrate the methods with an analysis of expected running time of the probabilistic dining philosophers (Lehmann and Ravin,  ...  This treatment di ers from other approaches to performance analysis of probabilistic algorithms [17, 3, 9, 21] in that we do not refer explicitly to the distribution over computation paths; neither do  ... 
doi:10.1016/s0304-3975(01)00049-4 fatcat:mncofranmbgvjksnmifadv3csa

A New Efficient Methodology for AC Transmission Network Expansion Planning in The Presence of Uncertainties [article]

Soumya Das, Ashu Verma, P. R. Bijwe
2019 arXiv   pre-print
In both the systems, rated wind generation is considered to be more than one-tenth of the total generation capacity.  ...  Therefore, the proposed method provides a tool for efficient solution of future probabilistic ACTNEP problems with greater level of complexity.  ...  This is expected, as in the latter case, all of the possible line contingencies in the system are considered without their probabilistic treatment.  ... 
arXiv:1908.00710v1 fatcat:dwww4di5pnfhzagynmnbxig6ze

The Vigilant Eating Rule: A General Approach for Probabilistic Economic Design with Constraints [article]

Haris Aziz, Florian Brandl
2021 arXiv   pre-print
We consider the problem of probabilistic allocation of objects under ordinal preferences.  ...  When the set of feasible allocations is convex, we also present a characterization of our rule based on ordinal egalitarianism.  ...  They also thank the participants of the following events where the paper  ... 
arXiv:2008.08991v3 fatcat:fp2e3xaaordcpj44uwhlcosrfa

On the Impossibility of Non-trivial Accuracy in Presence of Fairness Constraints

Carlos Pinzón, Catuscia Palamidessi, Pablo Piantanida, Frank Valencia
2022 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
One of the main concerns about fairness in machine learning (ML) is that, in order to achieve it, one may have to trade off some accuracy.  ...  In the probabilistic case, however, the issue is more complicated: It has been shown that under differential privacy constraints, there are data sources for which EO can only be achieved at the total detriment  ...  There are also several works that focus on the compatibility of fairness constraints.  ... 
doi:10.1609/aaai.v36i7.20770 fatcat:kwio3ed2yzalxautxptcs5dfqe

A Probabilistic Theory of Occupancy and Emptiness [chapter]

Rahul Bhotika, David J. Fleet, Kiriakos N. Kutulakos
2002 Lecture Notes in Computer Science  
algorithm that draws fair samples (i.e., 3D photo hulls) from it.  ...  Based on formal definitions of visibility, occupancy, emptiness, and photo-consistency, the theoretical development yields a formulation of the Photo Hull Distribution, the tightest probabilistic bound  ...  The support of the National Science Foundation under Grant No. IIS-9875628 and of the Alfred P. Sloan Foundation for Research Fellowships to Fleet and Kutulakos is gratefully acknowledged.  ... 
doi:10.1007/3-540-47977-5_8 fatcat:cy76qrn7xzb4rimjnrzewvyizm

iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making [article]

Preethi Lahoti, Krishna P. Gummadi, Gerhard Weikum
2019 arXiv   pre-print
People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning.  ...  Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: giving adequate success rates to specifically protected groups.  ...  This research was supported by the ERC Synergy Grant "imPACT" (No. 610150) and ERC Advanced Grant "Foundations for Fair Social Computing" (No. 789373).  ... 
arXiv:1806.01059v2 fatcat:iqci7fthbngjlg5ccpiydcwxny
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