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1. Learning Behavioral Soft Constraints from Demonstrations

   Glazier, Arie ; Loreggia, Andrea ; Mattei, Nicholas ; Rahgooy, Taher ; Rossi, Francesca ; Venable, Brent ( January 2021 , Workshop on Safe and Robust Control of Uncertain Systems at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021))

   Many real-life scenarios require humans to make difficult trade-offs: do we always follow all the traffic rules or do we violate the speed limit in an emergency? These scenarios force us to evaluate the trade-off between collective norms and our own personal objectives. To create effective AI-human teams, we must equip AI agents with a model of how humans make trade-offs in complex, constrained environments. These agents will be able to mirror human behavior or to draw human attention to situations where decision making could be improved. To this end, we propose a novel inverse reinforcement learning (IRL) method for learning implicit hard and soft constraints from demonstrations, enabling agents to quickly adapt to new settings. In addition, learning soft constraints over states, actions, and state features allows agents to transfer this knowledge to new domains that share similar aspects.
2. Learning to Design Fair and Private Voting Rules

   https://doi.org/10.1613/jair.1.13734  

   Mohsin, Farhad ; Liu, Ao ; Chen, Pin-Yu ; Rossi, Francesca ; Xia, Lirong ( September 2022 , Journal of Artificial Intelligence Research)

   Voting is used widely to identify a collective decision for a group of agents, based on their preferences. In this paper, we focus on evaluating and designing voting rules that support both the privacy of the voting agents and a notion of fairness over such agents. To do this, we introduce a novel notion of group fairness and adopt the existing notion of local differential privacy. We then evaluate the level of group fairness in several existing voting rules, as well as the trade-offs between fairness and privacy, showing that it is not possible to always obtain maximal economic efficiency with high fairness or high privacy levels. Then, we present both a machine learning and a constrained optimization approach to design new voting rules that are fair while maintaining a high level of economic efficiency. Finally, we empirically examine the effect of adding noise to create local differentially private voting rules and discuss the three-way trade-off between economic efficiency, fairness, and privacy.This paper appears in the special track on AI & Society.
3. Planning for Automated Vehicles with Human Trust

   https://doi.org/10.1145/3561059  

   Sheng, Shili ; Pakdamanian, Erfan ; Han, Kyungtae ; Wang, Ziran ; Lenneman, John ; Parker, David ; Feng, Lu ( October 2022 , ACM Transactions on Cyber-Physical Systems)

   Recent work has considered personalized route planning based on user profiles, but none of it accounts for human trust. We argue that human trust is an important factor to consider when planning routes for automated vehicles. This article presents a trust-based route-planning approach for automated vehicles. We formalize the human-vehicle interaction as a partially observable Markov decision process (POMDP) and model trust as a partially observable state variable of the POMDP, representing the human’s hidden mental state. We build data-driven models of human trust dynamics and takeover decisions, which are incorporated in the POMDP framework, using data collected from an online user study with 100 participants on the Amazon Mechanical Turk platform. We compute optimal routes for automated vehicles by solving optimal policies in the POMDP planning and evaluate the resulting routes via human subject experiments with 22 participants on a driving simulator. The experimental results show that participants taking the trust-based route generally reported more positive responses in the after-driving survey than those taking the baseline (trust-free) route. In addition, we analyze the trade-offs between multiple planning objectives (e.g., trust, distance, energy consumption) via multi-objective optimization of the POMDP. We also identify a set of open issues and implicationsmore »for real-world deployment of the proposed approach in automated vehicles.« less
4. ALLOCATING RESPONSIBILITY IN CONTENT MODERATION: A FUNCTIONAL FRAMEWORK

   Deirdre K. Mulligan ; Kenneth A. Bamberger ( October 2022 , Berkeley technology law journal)

   This Article develops a framework for both assessing and designing content moderation systems consistent with public values. It argues that moderation should not be understood as a single function, but as a set of subfunctions common to all content governance regimes. By identifying the particular values implicated by each of these subfunctions, it explores the appropriate ways the constituent tasks might best be allocated-specifically to which actors (public or private, human or technological) they might be assigned, and what constraints or processes might be required in their performance. This analysis can facilitate the evaluation and design of content moderation systems to ensure the capacity and competencies necessary for legitimate, distributed systems of content governance. Through a combination of methods, legal schemes delegate at least a portion of the responsibility for governing online expression to private actors. Sometimes, statutory schemes assign regulatory tasks explicitly. In others, this delegation often occurs implicitly, with little guidance as to how the treatment of content should be structured. In the law's shadow, online platforms are largely given free rein to configure the governance of expression. Legal scholarship has surfaced important concerns about the private sector's role in content governance. In response, private platforms engaged inmore »content moderation have adopted structures that mimic public governance forms. Yet, we largely lack the means to measure whether these forms are substantive, effectively infusing public values into the content moderation process, or merely symbolic artifice designed to deflect much needed public scrutiny. This Article's proposed framework addresses that gap in two ways. First, the framework considers together all manner of legal regimes that induce platforms to engage in the function of content moderation. Second, it focuses on the shared set of specific tasks, or subfunctions, involved in the content moderation function across these regimes. Examining a broad range of content moderation regimes together highlights the existence of distinct common tasks and decision points that together constitute the content moderation function. Focusing on this shared set of subfunctions highlights the different values implicated by each and the way they can be "handed off' to human and technical actors to perform in different ways with varying normative and political implications. This Article identifies four key content moderation subfunctions: (1) definition of policies, (2) identification of potentially covered content, (3) application of policies to specific cases, and (4) resolution of those cases. Using these four subfunctions supports a rigorous analysis of how to leverage the capacities and competencies of government and private parties throughout the content moderation process. Such attention also highlights how the exercise of that power can be constrained-either by requiring the use of particular decision-making processes or through limits on the use of automation-in ways that further address normative concerns. Dissecting the allocation of subfunctions in various content moderation regimes reveals the distinct ethical and political questions that arise in alternate configurations. Specifically, it offers a way to think about four key questions: (1) what values are most at issue regarding each subfunction; (2) which activities might be more appropriate to delegate to particular public or private actors; (3) which constraints must be attached to the delegation of each subfunction; and (4) where can investments in shared content moderation infrastructures support relevant values? The functional framework thus provides a means for both evaluating the symbolic legal forms that firms have constructed in service of content moderation and for designing processes that better reflect public values.« less
5. “Improving Learning & Reducing Time: A Constrained Action-Based Reinforcement Learning Approach.”

   Shen, S. ( January 2018 , In Proceedings of 26th ACM International Conference on User Modeling, Adaptation, and Personalization Conference (ACM UMAP-2018))

   Constrained action-based decision-making is one of the most challenging decision-making problems. It refers to a scenario where an agent takes action in an environment not only to maximize the expected cumulative reward but where it is subject to certain actionbased constraints; for example, an upper limit on the total number of certain actions being carried out. In this work, we construct a general data-driven framework called Constrained Action-based Partially Observable Markov Decision Process (CAPOMDP) to induce effective pedagogical policies. Specifically, we induce two types of policies: CAPOMDP-LG using learning gain as reward with the goal of improving students’ learning performance, and CAPOMDP-Time using time as reward for reducing students’ time on task. The effectiveness ofCAPOMDP-LG is compared against a random yet reasonable policy and the effectiveness of CAPOMDP-Time is compared against both a Deep Reinforcement Learning induced policy and a random policy. Empirical results show that there is an Aptitude Treatment Interaction effect: students are split into High vs. Low based on their incoming competence; while no significant difference is found among the High incoming competence groups, for the Low groups, students following CAPOMDP-Time indeed spent significantly less time than those using the two baseline policies and students following CAPOMDP-LGmore »significantly outperform their peers on both learning gain and learning efficiency.« less