Ranking systems that combine expert scores with crowd votes often cannot observe the crowd-vote signal directly; only the elimination outcomes it produces are recorded. Recovering the latent signal from these censored outcomes is an inverse problem with a vast space of solutions that fit the data equally well. This paper presents a probabilistic inverse framework for latent preference recovery and a bilevel scheme for fairness-aware ranking optimization. A truncated-volume probability constraint restricts the solution space to geometrically valid configurations, removing more than 99 percent of invalid candidates, and adaptive Bayesian Markov chain Monte Carlo sampling recovers the per-round crowd-vote distribution with calibrated uncertainty. An entropy-weighted multi-criteria evaluation compares ranking-based and proportion-based fusion, and a hybrid mechanism holds the fairness score above 0.3 across the fairness, engagement, and stability axes. An interaction model confirms a positive synergy between expert and crowd signals, with a coefficient near +0.27 at p below 0.05. A bilevel optimizer, with particle swarm search outside and an adaptive scoring model inside, reaches a 99.71 percent elimination-prediction hit rate under steady-state noise, retains 85 percent of engagement, and improves fairness by about 40 percent. Sensitivity analysis confirms a stable operating region.

Zhenheng Fu. Latent Preference Inference and Bilevel Fairness Optimization for Hybrid Expert-Crowd Ranking Systems. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 7: 84-91. https://doi.org/10.25236/AJCIS.2026.090711.

[1] R. T. Melstrom and C. Reeling, "Modeling recreation and tourism demand using crowdsourced data: An application to visitation statistics from the eBird project", Tourism Management, 2026, Vol. 112, p105276

[2] B. Wu, J. Huang and S. Yu, "'X of information' continuum: A survey on AI-driven multi-dimensional metrics for next-generation networked systems", IEEE Communications Surveys & Tutorials, 2026, Vol. 28, p5307-5344

[3] M. Ayadi, N. Masmoudi, L. Almuqren, H. Saeed Alshahrani and R. Oudah Aljohani, "Designing a novel CNN-LSTM-based model for Arabic handwritten character recognition for the visually impaired person", Journal of Disability Research, 2025, Vol. 4 (1), p20240080

[4] K. Manoj and M. Iyapparaja, "Tamil handwritten character recognition: A comprehensive review of recent innovations and progress", Algorithms, 2025, Vol. 16 (8)

[5] A. Lakhdari, A. Abusafia, S. T. T. Lui and A. Bouguettaya, "Preference-aware crowdsourcing of IoT energy services", Proc. International Conference on Service-Oriented Computing, 2025, p396-412

[6] B. Wu, Z. Cai, W. Wu and X. Yin, "AoI-aware resource management for smart health via deep reinforcement learning", IEEE Access, 2023, Vol. 11, p81180-81195

[7] A. Chai-Allah, J. Hermes, A. De La Foye, Z. S. Venter, F. Joly, G. Brunschwig et al., "Assessing recreationists' preferences of the landscape and species using crowdsourced images and machine learning", Landscape and Urban Planning, 2025, Vol. 257, p105315

[8] B. Wu and W. Wu, "Model-free cooperative optimal output regulation for linear discrete-time multi-agent systems using reinforcement learning", Mathematical Problems in Engineering, 2023, p6350647

[9] H. Suyal, A. Singh and S. N. Shivhare, "FLCLA: Four-level crowdsourced label aggregation", IEEE Transactions on Consumer Electronics, 2025

[10] M. Iloska, J. A. Boscoboinik, Q. Wu and M. F. Bugallo, "Particle Markov chain Monte Carlo approach to inference in transient surface kinetics", Journal of Chemical Theory and Computation, 2025, Vol. 21 (1), p5-16

[11] H. Wu and Y. Cao, "Complementary phase interleaving-based fringe order recognition for temporal phase unwrapping", Pattern Recognition, 2025, Vol. 157, p110937

[12] D. Jawla and J. Kelleher, "Layer wise scaled Gaussian priors for Markov chain Monte Carlo sampled deep Bayesian neural networks", Frontiers in Artificial Intelligence, 2025, Vol. 8, p1444891

[13] M. Trigka and E. Dritsas, "A comprehensive survey of machine learning techniques and models for object detection", Sensors, 2025, Vol. 25 (1), p214

[14] E. Edozie, A. N. Shuaibu, U. K. John and B. O. Sadiq, "Comprehensive review of recent developments in visual object detection based on deep learning", Artificial Intelligence Review, 2025, Vol. 58 (9), p277

[15] Y. Xiuwu, J. Shiqi and L. Yong, "Non-uniform WSN clustering routing protocol based on non-cooperative game", Wireless Personal Communications, 2025, Vol. 140 (1), p561-590

[16] PV. Pagire, M. Chavali and A. Kale, "A comprehensive review of object detection with traditional and deep learning methods", Signal Processing, 2025, Vol. 237, p110075

[17] J. A. Vrugt and C. G. Diks, "The learning rate is not a constant: Sandwich-adjusted Markov chain Monte Carlo simulation", Entropy, 2025, Vol. 27 (10), p999

[18] B. Wu, J. Huang, Q. Duan, L. Dong and Z. Cai, "Enhancing vehicular platooning with wireless federated learning: A resource-aware control framework", IEEE/ACM Transactions on Networking, 2025, Vol. 33 (1), p1-16

[19] D. P. Bertsekas, "Dynamic programming and optimal control", Athena Scientific, Belmont, MA, 4th ed., 2017, Vol. 1

[20] B. Wu, J. Huang and Q. Duan, "FedTD3: An accelerated learning approach for UAV trajectory planning", Proc. Int. Conf. on Wireless Artificial Intelligent Computing Systems and Applications (WASA), 2025, p13-24

[21] J. H. Lee, M. Kim, S. Lee and C. Kang, "GAN-based image restoration for enhancing object detection in projector-camera systems", IEEE Access, 2025

[22] A. Bechar, R. Medjoudj, Y. Elmir, Y. Himeur and A. Amira, "Federated and transfer learning for cancer detection based on image analysis", Neural Computing and Applications, 2025, Vol. 37 (4), p2239-2284

[23] B. Wu, J. Huang and Q. Duan, "Real-time intelligent healthcare enabled by federated digital twins with AoI optimization", IEEE Network, 2025, p1

[24] V. Giglioni, J. Poole, R. Mills, I. Venanzi, F. Ubertini and K. Worden, "Transfer learning in bridge monitoring: Laboratory study on domain adaptation for population-based SHM of multispan continuous girder bridges", Mechanical Systems and Signal Processing, 2025, Vol. 224, p112151

[25] D. Pan, B.-N. Wu, Y.-L. Sun and Y.-P. Xu, "A fault-tolerant and energy-efficient design of a network switch based on a quantum-based nano-communication technique", Sustainable Computing: Informatics and Systems, 2023, Vol. 37, p100827

[26] B. Wu, Z. Ding and J. Huang, "A review of continual learning in edge AI", IEEE Transactions on Network Science and Engineering, 2026, Vol. 13, p6571-6588