If you are having trouble accessing any of these papers online, this webpage from UW Library may be of assistance: https://uwaterloo.ca/library/services/get-access-anywhere Papers on Trust 1. K. Vodrahalli et al; Do humans trust advice more if it comes from AI? An analysis of human-AI interactions; Proceedings of AIES 2022; 10 pages; 2022. https://arxiv.org/pdf/2107.07015.pdf 2. J. Templeton and T. Tran; A cluster-based integrated trust establishment model for intelligent agents; Proceedings of Trust workshop at AAMAS 2021; 12 pages. https://ceur-ws.org/Vol-3022/paper8.pdf 3. S. Adhikari and P. Gmytrasiewicz; Telling friend from foe - towards a Bayesian approach to sincerity and deception; Proc. of Trust workshop at AAMAS 2021; 15 pages. https://ceur-ws.org/Vol-3022/paper7.pdf 4. F. Yang et al. How do visual explanations foster appropriate trust in machine learning? Proceedings of IUI 2020; 12 pages. https://dl.acm.org/doi/10.1145/3377325.3377480 5. S. Sebo et al. The ripple effects of vulnerability: the effects of a robot's vulnerable behavior on trust in human-robot teams; Proceedings of HRI 2018; 9 pages. https://scazlab.yale.edu/sites/default/files/files/strohkorb%20sebo_HRI18.pdf 6. A. Sapienza and R. Falcone; Studying citizens' trust to monitor measures acceptance during Covid-19 pandemic; Proc. of Trust workshop at AAMAS 2021; 12 pages. https://ceur-ws.org/Vol-3022/paper1.pdf 7. J. Bentahar et al. Quantitative group trust: a two-stage verification approach; Proceedings of AAMAS 2022; 9 pages. https://dl.acm.org/doi/abs/10.5555/3535850.3535863 8. S. Sirur and T. Muller; Properties of reputation lag attack strategies; Proceedings of AAMAS 2022; 8 pages. https://dl.acm.org/doi/abs/10.5555/3535850.3535985 9. L. Zeynalvand et al.; COBRA: Context-aware Bernoulli neural networks for reputation assessment; Proceedings of AAAI 2020; 9 pages. https://arxiv.org/abs/1912.08446 10. H. Fang et al; A trust model stemmed from the diffusion theory for opinion evaluation; Proceedings of AAMS 2013; 8 pages. https://www.ifaamas.org/Proceedings/aamas2013/docs/p805.pdf 11. G. Guo et al; Trust SVD: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings; Proceedings of AAAI 2015; 7 pages; 2015. https://personal.ntu.edu.sg/zhangj/paper/aaai15-guo.pdf 12. Z. Zahedi et al; Trust-aware planning: modeling trust evolution in longitudinal human-robot interaction; Proceedings of XAIP workshop at ICAPS 2021; 8 pages; 2021. https://arxiv.org/abs/2105.01220 13. M. Yin et al; Understanding the effect of accuracy on trust in machine learning models; Proceedings of CHI 2019; 12 pages; 2019. https://dl.acm.org/doi/fullHtml/10.1145/3290605.3300509 https://www.jennwv.com/papers/accuracy-trust.pdf 14. K. Yu et al; User trust dynamics: an investigation driven by differences in system performance; Proceedings of IUI 2017; 10 pagest; 2017. https://shlomo-berkovsky.github.io/files/pdf/IUI17b.pdf 15. G. Venkatadari et al.; Strengthening weak identities through inter-domain trust transfer; Proceedings of IW3C2 2016; 2016; 11 pages. https://www.lix.polytechnique.fr/~goga/papers/transfer_trust_WWW2016.pdf 16. H. Lakkaraju and O. Bastani; "How do I fool you?": manipulating user trust via misleading black box explanations; Proceedings of AIES 2020; 7 pages; 2020. https://www.aies-conference.com/2020/wp-content/papers/182.pdf 17. D. Di Scala and P. Yolum; PCCART: Reinforcing trust in multiuser privacy agreement systems; Proceedings of AAMAS 2023; 9 pages; 2023. https://arxiv.org/pdf/2302.13650v1.pdf 18. S. Mehrotra et al.; More similar values, more trust? - the effect of value similarity on trust in human-agent interaction; Proceedings of AIES 2021; 7 pages; 2021. https://dl.acm.org/doi/pdf/10.1145/3461702.3462576 19. P. Gutierrez et al; Trust-based community assessment; Pattern Recognition Letters; 2015; 11 pages. https://digital.csic.es/bitstream/10261/130828/1/PRL(67)_49-58.pdf 20. A. Coates et al; Simulating the impact of personality on fake news; Proceedings of Trust workshop at AAMAS 2021; 12 pages; 2021. https://ceur-ws.org/Vol-3022/paper6.pdf 21. C. Buntain and J. Golbeck; Automatically identifying fake news in popular Twitter threads; Smart Cloud conference 2017; 8 pages. https://arxiv.org/pdf/1705.01613.pdf 22. O. Varol et al.; Online human-bot interactions: detection, estimation and characterization; Proceedings of ICWSM 2017; 10 pages. https://ojs.aaai.org/index.php/ICWSM/article/view/14871/14721 23. D. Calvaresi et al; Reputation management in multi-agent systems using permissioned blockchain technology; Proceedings of 2018 Conference on Web Intelligence; 8 pages; 2018/ https://www.researchgate.net/publication/328692648_Reputation_Management_in_Mult i-Agent_Systems_Using_Permissioned_Blockchain_Technology/link/5bdc43034585150b2b 993e96/download 24. E. Parhizkar et al; Combining direct trust and indirect trust in multi-agent systems; Proceedings of IJCAI 2020; 7 pages; 2020. https://www.ijcai.org/proceedings/2020/0044.pdf 25. P. Zhou et al; A priori trust inference with context-aware stereotypical deep learning; Knowledge-Based Systems, Vol. 88, Iss. C, pp.97-106; 2015. https://dl.acm.org/doi/abs/10.1016/j.knosys.2015.08.003 https://personal.ntu.edu.sg/zhangj/paper/kbs-zhou.pdf 26. S. Jiang et al.; An evolutionary model for constructing robust trust networks; Proceedings of AAMAS 2013; 2013. https://www.ifaamas.org/Proceedings/aamas2013/docs/p813.pdf 27. M. Cheng et al; A general trust framework for multi-agent systems; Proceedings of AAMAS 2021; 2021. https://www.ifaamas.org/Proceedings/aamas2021/pdfs/p332.pdf 28. Y. Zhao et al.; Seeing isn't believing: towards more robust adversarial attack against real world object detectors; Proceedings of ACM SIGSAC conference 2019; 2019; 16 pages. https://arxiv.org/pdf/1812.10217.pdf 29. P. Shiwaswamy and D. Garcia-Garcia; Adversary or friend? An adversarial approach to improving recommender systems; Proceedings of ACM Rec Sys confefence 2022; 2022; 9 pages. https://dl.acm.org/doi/pdf/10.1145/3523227.3546784 30. M. Sensoy et al; Reasoning about uncertain information and conflict resolution through trust revision; Proceedings of AAMAS 2013; 2013; 8 pages. https://dl.acm.org/doi/10.5555/2484920.2485053 https://www.ri.cmu.edu/publications/reasoning-about-uncertain-information-and-co nflict-resolution-through-trust-revision/ Papers on Explainability 1. U. Soni et al; Not all users are the same: providing personalized explanations for sequential decision making problems; Proceedings of XAIP workshop at ICAPS 2021; 8 pages; 2021. https://arxiv.org/pdf/2106.12207.pdf 2. C. Wan et al.; Explainability's gain is optimality's loss? How explanations bias decision making; Proceedings of AI, Ethics and Society (AIES) 2022 conference; 10 pages; 2022. https://arxiv.org/pdf/2206.08705.pdf 3. E. Sklar and M. Zhar; Explanation through argumentation; Proceedings of HAI 2018; https://dl.acm.org/doi/pdf/10.1145/3284432.3284470 4. A. Bell et al; It's just not that simple: An empirical study of the accuracy-explainability trade-off in machine learning for public policy; Proceedings of the Fairness, Accountability and Transparency 2022 Conference; https://dl.acm.org/doi/pdf/10.1145/3531146.3533090 5. M. Persiani and T. Hellstrom; The mirror agent model: a Bayesian architecture for interpretable agent behaviour; Proceedings of Extraamas workshop at AAMAS 2022; 14 pages. https://www.diva-portal.org/smash/get/diva2:1656416/FULLTEXT02 6. J. Gajcin and I. Dusparic; ReCCoVER: detecting causal confusion for explainable reinforcement learning; Proceedings of Extraamas workshop at AAMAS 2022; 2022. https://arxiv.org/pdf/2203.11211.pdf 7. V. Ordonez et al; Integration of local and global features explanation via CIU and explainable layers for improving global rules generation in ECLAIRE; Proc. Extraamas workshop at AAMAS 2022; 18 pages. https://www.researchgate.net/publication/361560732_Integration_of_local_and_global_features_explanation_with_global_rules_extraction_and_generation_tools 8. M. Brandao et al.; Explainability in multi-agent path/motion planning: user study-driven taxonomy and requirements; Proceedings of AAAMS 2022; 9 pages. https://www.martimbrandao.com/papers/Brandao2022-aamas.pdf https://dl.acm.org/doi/10.5555/3535850.3535871 9. A. Georgara and C. Sierra; Building contrastive explanations for multi-agent team formation; Proceedings of AAMAS 2022; 9 pages; 2022. https://ifaamas.org/Proceedings/aamas2022/pdfs/p516.pdf 10. A. Jacq et al; Lazy-MDPs: towards interpretable reinforcement learning by learning when to act; Proceedings of AAMAS 2022; 14 pages. https://arxiv.org/abs/2203.08542 11. P. Qian and V. Unhelkar; Evaluating the role of interactivity on improving transparency in autonomous agents; Proceedings of AAMAS 2022; 9 pages. https://www.ifaamas.org/Proceedings/aamas2022/pdfs/p1083.pdf 12. R. Agrawal, N. Ajmeri and M. Singh; Socially intelligent genetic agents for the emergence of explicit norms; Proceedings of IJCAI 2022; 7 pages. https://www.ijcai.org/proceedings/2022/0002.pdf 13. J. Dai et al; Fairness via Explanation Quality: evaluating disparities in the quality of post-hoc explanations; Proceedings of AIES 2022; 12 pages. https://arxiv.org/pdf/2205.07277.pdf 14. F. Barsotti et al.; Transparency, detection and imitation in strategic classification; Proceedings of IJCAI 2022; 9 pages; https://www.ijcai.org/proceedings/2022/0010.pdf 15. A. Balagopalan et al.; The road to explainability is paved with bias: measuring the fairness of explanations; Proceedings of FAT-Conference 2022; 13 pages. https://dl.acm.org/doi/pdf/10.1145/3531146.3533179 16. A. Bunt et al; Are explanations always important? A study of deployed, low-cost intelligent interactive systems; Proceedings of IUI 2012; 2012; 10 pages. http://hci.cs.umanitoba.ca/assets/publication_files/2012-IUI-Andrea-Explanations .pdf 17. Q. Li et al; Optimal local explainer aggregratoin for interpretable prediction; Proceedings of AAAI 2022; 9 pages. https://arxiv.org/abs/2003.09466 https://ojs.aaai.org/index.php/AAAI/article/view/21458 18. D. Rajapaksha and C. Bergmeir; LIMREF: local interpretable model agnostic rule-based explanations for forecasting, with an application to electricity smart meter data; Proceedings of AAAI 2022; 9 pages. https://arxiv.org/abs/2202.07766 19. W. Jin et al; Evaluating explainable AI on a multi-modal medical imagining task: can existing algorithms fulfill clinical requirements? Proceedings of AAAI 2022; 9 pages. https://arxiv.org/pdf/2203.06487.pdf 20. F. Mosca and J. Such; ELVIRA: an explainable agent for value and utility-driven nultiuser privacy; Proceedings of AAMAS 2021; 9 pages. https://www.ifaamas.org/Proceedings/aamas2021/pdfs/p916.pdf 21. X. Wang et al; A reinforcement learning framework for explainable recommendation; Proceedings of IEEE Conference on Data Mining 2018; 10 pages; 2018. https://www.microsoft.com/en-us/research/uploads/prod/2018/08/main.pdf 22. L. Shang et al; A duo-generative approach to explainable multimodal Covid-19 misinformation detectoin; Proceedings of WWW 2022; 8 pages; 2022. https://dl.acm.org/doi/10.1145/3485447.3512257 23. D. Slack et al; LIME and SHAP: Adversarial Attacks on Post Hoc Explanation Methods; Proceedings of AIES 2020; 7 pages; 2020. https://www.aies-conference.com/2020/wp-content/papers/174.pdf 24. R. Mothilal et al; Towards unifying feature attribution and counterfactual explanations: different means to the same end; Proceedings of AIES 2021; 15 pages; 2021. https://arxiv.org/pdf/2011.04917.pdf 25. K. Framling et al; Comparison of contextual importance and utility with LIME and Shapley values; Proceedings of EXTRAAMAS workshop at AAMAS 2021; 15 pages; 2021. https://dl.acm.org/doi/abs/10.1007/978-3-030-82017-6_3 https://acris.aalto.fi/ws/portalfiles/portal/76979599/SCI_Fr_mling_Comparison_of _Contextual_Importance_and_Utility_with_LIME_and_Shapley_Values.pdf 26. Y. Lyu et al.; DIME: Fine-grained interpretations of multimodal models via disentangled local explanations; Proceedings of AIES 2022; 2022; 14 pages. https://arxiv.org/pdf/2203.02013.pdf 27. X. Dai et al; Counterfactual explanations for prediction and diagnosis in XAI; Proceedings of AIES 2022; 2022; 12 pages. https://dl.acm.org/doi/pdf/10.1145/3514094.3534144 28. J. van der Waa et al; Evaluating XAI: a comparison of rule-based and example-based explanations; Artificial Intelligence Volume 291; 2021; 19 pages. https://reader.elsevier.com/reader/sd/pii/S0004370220301533?token=D9AC71EC5CEC6E 7EF2635995B6690F06DEC8E8AE295674E91D8967A15370B8495C2FC10770D3CC9591D12ED2174A0D 2B&originRegion=us-east-1&originCreation=20230521131011 29. B. Gyevnar et al; Causal explanations for stochastic sequential multi-agent decision-making; Proceedings of EXTRAAMAS workshop at AAMAS 2023; 2023; 8 pages. https://arxiv.org/pdf/2302.10809.pdf 30. L. Ibrahim et al; Do explanations improve the quality of AI-assisted human decisions? An algorithm-in-the-loop analysis of factual and counterfactual explanations; Proceedings of AAMAS 2023; 2023; 9 pages. https://www.southampton.ac.uk/~eg/AAMAS2023/pdfs/p326.pdf 31. J.Zhang et al.; Model debiasing via gradient-based explanation on representation; Proceedings of AIES 2023; 11 pages. https://arxiv.org/pdf/2305.12178.pdf 32. A. Schwarzchild et al.; Reckoning with the disagreement problem: explanation consensus as a training objective; Proceedings of AIES 2023; 8 pages (plus appendix). https://arxiv.org/pdf/2303.13299.pdf 33. A. Artelt and B. Hammer; Explain it in the same way: model-agnostic group fairness of counterfactual explanations; Proceedings of IJCAI 2023 XAI workshop; 13 pages. https://arxiv.org/pdf/2211.14858.pdf 34. W. Zhang et al.; Neuro-symbolic interpretable collaborative filtering for attribute-based recommendation; Proceedings of WWW 2022; 10 pages. https://dl.acm.org/doi/10.1145/3485447.3512042 https://web.archive.org/web/20220504023001id_/https://dl.acm.org/doi/pdf/10.1145/3485447.3512042 Papers on Social Networks 1. P. Berenbrink et al.; Asynchronous opinion dynamics in social networks; Proceedings of AAMAS 2022; 9 pages. https://www.ifaamas.org/Proceedings/aamas2022/pdfs/p109.pdf 2. M. Irfan et al; Cascades and overexposure in social networks: the budgeted case; Proceedings of AAMAS 2022; 9 pages. https://ifaamas.org/Proceedings/aamas2022/pdfs/p642.pdf 3. D. Zhang and A. Carver; Segregation in social networks of heterogeneous agents acting under incomplete information; Proceedings of AAMAS 2022; 9 pages. https://www.ifaamas.org/Proceedings/aamas2022/pdfs/p1455.pdf 4. S. Alouf-Heffetz et al; How should we vote? A comparison of voting systems within social networks; Proceedings of IJCAI 2022; 9 pages. https://www.ijcai.org/proceedings/2022/0005.pdf 5. A. Abouzeid et al.; Socially fair mitigation of misinformation on social networks via constraint stochastic optimization; Proceedings of AAAI 2022; 9 pages. https://arxiv.org/abs/2203.12537 6. T. Le et al; Socialbots on fire: modeling adversarial behaviors of socialbots via multi-agent hierarchical reinforcement learning; Proceedings of ACM The Web conference 2022; 10 pages; 2022. https://arxiv.org/pdf/2110.10655.pdf 7. C. Schweimer et al; Generating simple directed social network graphs for information spreading; Proceedings of ACM The Web conference 2022; 11 pages; 2022. https://arxiv.org/pdf/2205.02485.pdf 8. W. Xu et al; Evidence-aware fake news detection with graph neural networks; Proceedings of ACM the Web conference 2022; 10 pages; 2022. https://arxiv.org/pdf/2201.06885.pdf 9. J. Song et al.; "I Have No Text in my Post": using visual hints to model user emotions in social media; Proceedings of ACM The Web 2022; 9 pages; 2022. https://dl.acm.org/doi/pdf/10.1145/3485447.3512009 10. M. Sun et al; DDGCN: Dual dynamic graph convolutional networks for rumour detection on social media; Proceedings of AAAI 2022; 9 pages; 2022. https://ojs.aaai.org/index.php/AAAI/article/view/20385 11. Z-Y Dou et al; Harnessing social media to identify homeless youth at risk of substance use; Proceedings of AAAI 2021; 2021. https://ojs.aaai.org/index.php/AAAI/article/view/17732 12. J. Li and Y. Ning Anti-Asian hate speech detection via data augmented semantic relation inference; Proceedings of ICWSM 2022 conference; 11 pages; 2022. https://ojs.aaai.org/index.php/ICWSM/article/view/19319/19091 13. F. Cinus et al; The effect of people recommendation on echo chambers and polarization; Proceedings of ICSWM 2022 conference; 12 pages; 2022. https://ojs.aaai.org/index.php/ICWSM/article/view/19275/19047 14. H. Sarmiento; Identifying and characterizing new expressions of community framing during polarization; Proceedings of ICWSM 2022 conference; 11 pages; 2022. https://ojs.aaai.org/index.php/ICWSM/article/view/19339/19111 15. Q. Kong et al; Slipping to the extreme: a mixed method to explain how extreme opinions infiltrate online discussions; Proceedings of ICSWM 2022 conference; 12 pages; 2022. https://ojs.aaai.org/index.php/ICWSM/article/view/19312/19084 16. A. Tsang et al; Group-fairness in influence maximization; Proceedings of IJCAI 2019; 9 pages; 2019. https://people.scs.carleton.ca/~alantsang/files/groupfair19.pdf 17. H. Kamarthi et al; Influence maximization in unknown social networks: learning policies for effective graph sampling; Proceedings of AAMAS 2020; 10 pages; 2020. https://arxiv.org/pdf/1907.11625.pdf 18. X. Tong et al.; What are people talking about in #BlackLivesMatter and #StopAsianHate? Exploring and categorizing twitter topics emerging in online social movements through the latent Dirichlet allocation model; Proceedings of AIES 2022; 16 pages; 2022. https://arxiv.org/pdf/2205.14725.pdf 19. K. Garimella et al; Political polarization in online news consumption; Proceedings of ICWSM 2021; 11 pages; 2021. https://ojs.aaai.org/index.php/ICWSM/article/view/18049/17852 20. A. Guimaraes and G. Weikum; X-posts explained: analyzing and predicting controversial contributions in thematically diverse Reddit forums; Proceedings of ICWSM 2021; 10 pages; 2021. https://ojs.aaai.org/index.php/ICWSM/article/view/18050/17853 21. A. Mashhadi et al; No walk in the park: the viability and fairness of social media analysis for parks and recreation; Proceedings of ICWSM 2021; 12 pages; 2021. https://ojs.aaai.org/index.php/ICWSM/article/view/18071/17874 22. K. Solovev and N. Prollochs; Moral emotions shape the virality of Covid-19 misinformation on social media; Proceedings of ACM the Web 2022; 12 pages; 2022. https://dl.acm.org/doi/pdf/10.1145/3485447.3512266 23. L. Tian et al.; DUCK: Rumour detection on social media by modelling user and comment propagation networks; Proceedings of NAACL 2022; 11pages; 2022. https://aclanthology.org/2022.naacl-main.364.pdf 24. A. Sharma et al; Towards facilitating emphathetic conversations in online mental health support: a reinforcement learning approach; Proceedings of WWW 2021; 12 pages; 2021. https://arxiv.org/pdf/2101.07714.pdf 25. G. Gadek et al; Topical cohesion of communities on Twitter; Proceedings of KES2017; 10 pages; 2017. https://reader.elsevier.com/reader/sd/pii/S1877050917315284?token=C6F6778159192D F0FF9AF2C16337149A25838C681F013A02D4F7FE3D18832C563EA961F6AC93A90BF67267162278A0 84&originRegion=us-east-1&originCreation=20230312114750 26. L. Derczynski et al.; SemEval-2017 Task 8: Rumour Eval: Determining rumour veracity and support for rumours; Proceedings of 11th international workshop on semantic evaluations (SemEval-2017); 2017; pg.69-76. https://aclanthology.org/S17-2006.pdf 27. S. Balasubramanian et al; Leaders or followers? A temporal analysis of tweets from IRA trolls; Proceedings of ICWSM 2022; https://arxiv.org/pdf/2204.01790.pdf 28. F. Cinus et al; The effect of people recommenders on echo chambers and polarization; Proceedings of ICWSM 2022; https://www.isi.it/media/604 29. S. Aref and Z. Neal; Detecting coalitions by optimally partitioning signed networks of political collaboration; Nature Scientific Reports 10, Article number 1506; 2020; 10 pages. https://www.nature.com/articles/s41598-020-58471-z 30. N. Alon et al; How robust is the wisdom of the crowds? Proceedings of IJCAI 2015; 2015; 7 pages. https://www.cs.tau.ac.il/~nogaa/PDFS/ijcai15.pdf 31. M. Choi et al; Analyzng the engagement of social relationships during life event shocks in social media; Proceedings of ICWSM 2023; 12 pages. https://arxiv.org/pdf/2302.07951.pdf 32. J. Jiang et al.; Retweet-BERT: political leaning detection using language features and information diffusion on social networks; Proceedings of ICWSM 2023; 11 pages; https://arxiv.org/pdf/2207.08349.pdf 33. C. Davies et al; Multi-scale user migration on Reddit; ICWSM 2021 workshop on Cybersocial threats; 9 pages. https://workshop-proceedings.icwsm.org/pdf/2021_13.pdf 34. D. Hettiachchi et al.; How crowd worker factors influence subjective annotations: a study of tagging misogynistic hate speech in Tweets; Proceedings of HCOMP 2023; 13 pages. https://arxiv.org/pdf/2309.01288.pdf 35. S. Feng et al.; From pretraining data to language models to downstream tasks: tracking the trails of political biases leading to unfair NLP models; Proceedings of ACL 2023; 24 pages (10 pages plus references and appendices) https://arxiv.org/pdf/2305.08283.pdf 36. H. Ghanadian et al; ChatGPT for suicide risk assessment on social media: quantitative evaluation of model performance, potentials and limitations; ACL 2023 workshop on Social media analysis; 12 pages. https://arxiv.org/pdf/2306.09390.pdf 37. R. Baten et al.; Predicting future location categories of users in a large social platform; Proceedings of ICSWM 2023; 12 pages. https://ojs.aaai.org/index.php/ICWSM/article/view/22125/21904