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1. Satisfying complex top- k fairness constraints by preference substitutions

   https://doi.org/10.14778/3565816.3565832  

   Islam, Md. Mouinul; Wei, Dong; Schieber, Baruch; Roy, Senjuti Basu (October 2022, Proceedings of the VLDB Endowment)

   Given m users (voters), where each user casts her preference for a single item (candidate) over n items (candidates) as a ballot, the preference aggregation problem returns k items (candidates) that have the k highest number of preferences (votes). Our work studies this problem considering complex fairness constraints that have to be satisfied via proportionate representations of different values of the group protected attribute(s) in the top- k results. Precisely, we study the margin finding problem under single ballot substitutions , where a single substitution amounts to removing a vote from candidate i and assigning it to candidate j and the goal is to minimize the number of single ballot substitutions needed to guarantee that the top-k results satisfy the fairness constraints. We study several variants of this problem considering how top- k fairness constraints are defined, (i) MFBinaryS and MFMultiS are defined when the fairness (proportionate representation) is defined over a single, binary or multivalued, protected attribute, respectively; (ii) MF-Multi2 is studied when top- k fairness is defined over two different protected attributes; (iii) MFMulti3+ investigates the margin finding problem, considering 3 or more protected attributes. We study these problems theoretically, and present a suite of algorithms with provable guarantees. We conduct rigorous large scale experiments involving multiple real world datasets by appropriately adapting multiple state-of-the-art solutions to demonstrate the effectiveness and scalability of our proposed methods. 

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2. Interactions of heparin with key glycoproteins of human respiratory syncytial virus

   https://doi.org/10.3389/fmolb.2023.1151174  

   Shi, Deling; He, Peng; Song, Yuefan; Linhardt, Robert J; Dordick, Jonathan S; Chi, Lianli; Zhang, Fuming (April 2023, Frontiers in Molecular Biosciences)

   Introduction:The unexpected surge of respiratory syncytial virus (RSV) cases following pandemic phase of COVID-19 has drawn much public attention. Drawing on the latest antiviral research, revisiting this heightened annual outbreak of respiratory disease could lead to new treatments. The ability of sulfated polysaccharides to compete for a variety of viruses binding to cell surface heparan sulfate, suggests several drugs that might have therapeutic potential for targeting RSV–glycosaminoglycan interactions. Methods:In the current study, the binding affinity and kinetics of two RSV glycoproteins (RSV-G protein and RSV-F protein) to heparin were investigated by surface plasmon resonance. Furthermore, solution competition studies using heparin oligosaccharides of different lengths indicated that the binding of RSV-G protein to heparin is size-dependent, whereas RSV-F protein did not show any chain length preference. Results and discussion:The two RSV glycoproteins have slightly different preferences for heparin sulfation patterns, but theN-sulfo group in heparin was most critical for the binding of heparin to both RSV-G protein and RSV-F protein. Finally, pentosan polysulfate and mucopolysaccharide polysulfate were evaluated for their inhibition of the RSV-G protein and RSV-F protein–heparin interaction, and both highly negative compounds showed strong inhibition. 

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3. PreFAIR: Combining Partial Preferences for Fair Consensus Decision-making

   https://doi.org/10.1145/3630106.3658961  

   Cachel, Kathleen; Rundensteiner, Elke (June 2024, ACM)

   Preference aggregation mechanisms help decision-makers combine diverse preference rankings produced by multiple voters into a single consensus ranking. Prior work has developed methods for aggregating multiple rankings into a fair consensus over the same set of candidates. Yet few real-world problems present themselves as such precisely formulated aggregation tasks with each voter fully ranking all candidates. Instead, preferences are often expressed as rankings over partial and even disjoint subsets of candidates. For instance, hiring committee members typically opt to rank their top choices instead of exhaustively ordering every single job applicant. However, the existing literature does not offer a framework for characterizing nor ensuring group fairness in such partial preference aggregation tasks. Unlike fully ranked settings, partial preferences imply both a selection decision of whom to rank plus an ordering decision of how to rank the selected candidates. Our work fills this gap by conceptualizing the open problem of fair partial preference aggregation. We introduce an impossibility result for fair selection from partial preferences and design a computational framework showing how we can navigate this obstacle. Inspired by Single Transferable Voting, our proposed solution PreFair produces consensus rankings that are fair in the selection of candidates and also in their relative ordering. Our experimental study demonstrates that PreFair achieves the best performance in this dual fairness objective compared to state-of-the-art alternatives adapted to this new problem while still satisfying voter preferences. 

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4. Fairer Together: Mitigating Disparate Exposure in Kemeny Rank Aggregation

   https://doi.org/10.1145/3593013.3594085  

   Cachel, Kathleen; Rundensteiner, Elke (June 2023, 2023 ACM Conference on Fairness, Accountability, and Transparency)

   In social choice, traditional Kemeny rank aggregation combines the preferences of voters, expressed as rankings, into a single consensus ranking without consideration for how this ranking may unfairly affect marginalized groups (i.e., racial or gender). Developing fair rank aggregation methods is critical due to their societal influence in applications prioritizing job applicants, funding proposals, and scheduling medical patients. In this work, we introduce the Fair Exposure Kemeny Aggregation Problem (FairExp-kap) for combining vast and diverse voter preferences into a single ranking that is not only a suitable consensus, but ensures opportunities are not withheld from marginalized groups. In formalizing FairExp-kap, we extend the fairness of exposure notion from information retrieval to the rank aggregation context and present a complimentary metric for voter preference representation. We design algorithms for solving FairExp-kap that explicitly account for position bias, a common ranking-based concern that end-users pay more attention to higher ranked candidates. epik solves FairExp-kap exactly by incorporating non-pairwise fairness of exposure into the pairwise Kemeny optimization; while the approximate epira is a candidate swapping algorithm, that guarantees ranked candidate fairness. Utilizing comprehensive synthetic simulations and six real-world datasets, we show the efficacy of our approach illustrating that we succeed in mitigating disparate group exposure unfairness in consensus rankings, while maximally representing voter preferences. 

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5. On the Pros and Cons of Active Learning for Moral Preference Elicitation

   https://doi.org/10.1609/AIES.V7I1.31673  

   Keswani, Vijay; Conitzer, Vincent; Heidari, Hoda; Schaich_Borg, Jana; Sinnott-Armstrong, Walter (October 2024, Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society)

   Computational preference elicitation methods are tools used to learn people’s preferences quantitatively in a given context. Recent works on preference elicitation advocate for active learning as an efficient method to iteratively construct queries (framed as comparisons between context-specific cases) that are likely to be most informative about an agent’s underlying preferences. In this work, we argue that the use of active learning for moral preference elicitation relies on certain assumptions about the underlying moral preferences, which can be violated in practice. Specifically, we highlight the following common assumptions (a) preferences are stable over time and not sensitive to the sequence of presented queries, (b) the appropriate hypothesis class is chosen to model moral preferences, and (c) noise in the agent’s responses is limited. While these assumptions can be appropriate for preference elicitation in certain domains, prior research on moral psychology suggests they may not be valid for moral judgments. Through a synthetic simulation of preferences that violate the above assumptions, we observe that active learning can have similar or worse performance than a basic random query selection method in certain settings. Yet, simulation results also demonstrate that active learning can still be viable if the degree of instability or noise is relatively small and when the agent’s preferences can be approximately represented with the hypothesis class used for learning. Our study highlights the nuances associated with effective moral preference elicitation in practice and advocates for the cautious use of active learning as a methodology to learn moral preferences. 

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