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Providing Explanations for Recommendations in Reciprocal Environments [article]

Akiva Kleinerman, Ariel Rosenfeld, Sarit Kraus
2018 arXiv   pre-print
Automated platforms which support users in finding a mutually beneficial match, such as online dating and job recruitment sites, are becoming increasingly popular. These platforms often include recommender systems that assist users in finding a suitable match. While recommender systems which provide explanations for their recommendations have shown many benefits, explanation methods have yet to be adapted and tested in recommending suitable matches. In this paper, we introduce and extensively
more » ... e and extensively evaluate the use of "reciprocal explanations" -- explanations which provide reasoning as to why both parties are expected to benefit from the match. Through an extensive empirical evaluation, in both simulated and real-world dating platforms with 287 human participants, we find that when the acceptance of a recommendation involves a significant cost (e.g., monetary or emotional), reciprocal explanations outperform standard explanation methods which consider the recommendation receiver alone. However, contrary to what one may expect, when the cost of accepting a recommendation is negligible, reciprocal explanations are shown to be less effective than the traditional explanation methods.
arXiv:1807.01227v1 fatcat:spvdh4iidzed7ikvze2se2wmgi

What Should We Optimize in Participatory Budgeting? An Experimental Study [article]

Ariel Rosenfeld, Nimrod Talmon
2021 arXiv   pre-print
Participatory Budgeting (PB) is a process in which voters decide how to allocate a common budget; most commonly it is done by ordinary people – in particular, residents of some municipality – to decide on a fraction of the municipal budget. From a social choice perspective, existing research on PB focuses almost exclusively on designing computationally-efficient aggregation methods that satisfy certain axiomatic properties deemed "desirable" by the research community. Our work complements this
more » ... k complements this line of research through a user study (N = 215) involving several experiments aimed at identifying what potential voters (i.e., non-experts) deem fair or desirable in simple PB settings. Our results show that some modern PB aggregation techniques greatly differ from users' expectations, while other, more standard approaches, provide more aligned results. We also identify a few possible discrepancies between what non-experts consider desirable and how they perceive the notion of "fairness" in the PB context. Taken jointly, our results can be used to help the research community identify appropriate PB aggregation methods to use in practice.
arXiv:2111.07308v1 fatcat:v4rdlr5xdzgalirfql4owog75m

Automation of Android Applications Testing Using Machine Learning Activities Classification [article]

Ariel Rosenfeld, Odaya Kardashov, Orel Zang
2017 arXiv   pre-print 5 Rosenfeld  ... 
arXiv:1709.00928v1 fatcat:s2imj7qfoze3toouc57oax7ofq

Justified Representation for Perpetual Voting

Laurent Bultaeu, Noam Hazon, Rutvik Page, Ariel Rosenfeld, Nimrod Talmon
2021 IEEE Access  
ARIEL ROSENFELD received the B.Sc. degree (magna cum laude) in computer science and economics from Tel-Aviv University, Israel, and the Ph.D. degree in computer science from Bar-Ilan University, Israel  ...  He is currently a Senior Lecturer with Ariel University, Israel.  ... 
doi:10.1109/access.2021.3095087 fatcat:wn57llgm4bec7nldxi7garf5ja

Big Data Analytics and AI in Mental Healthcare [article]

Ariel Rosenfeld, David Benrimoh, Caitrin Armstrong, Nykan Mirchi, Timothe Langlois-Therrien, Colleen Rollins, Myriam Tanguay-Sela, Joseph Mehltretter, Robert Fratila, Sonia Israel, Emily Snook, Kelly Perlman, Akiva Kleinerman (+4 others)
2019 arXiv   pre-print
Mental health conditions cause a great deal of distress or impairment; depression alone will affect 11% of the world's population. The application of Artificial Intelligence (AI) and big-data technologies to mental health has great potential for personalizing treatment selection, prognosticating, monitoring for relapse, detecting and helping to prevent mental health conditions before they reach clinical-level symptomatology, and even delivering some treatments. However, unlike similar
more » ... e similar applications in other fields of medicine, there are several unique challenges in mental health applications which currently pose barriers towards the implementation of these technologies. Specifically, there are very few widely used or validated biomarkers in mental health, leading to a heavy reliance on patient and clinician derived questionnaire data as well as interpretation of new signals such as digital phenotyping. In addition, diagnosis also lacks the same objective 'gold standard' as in other conditions such as oncology, where clinicians and researchers can often rely on pathological analysis for confirmation of diagnosis. In this chapter we discuss the major opportunities, limitations and techniques used for improving mental healthcare through AI and big-data. We explore both the computational, clinical and ethical considerations and best practices as well as lay out the major researcher directions for the near future.
arXiv:1903.12071v1 fatcat:lxzhoy76qjaahezvq3344udn2q

Intelligent agent supporting human–multi-robot team collaboration

Ariel Rosenfeld, Noa Agmon, Oleg Maksimov, Sarit Kraus
2017 Artificial Intelligence  
The number of multi-robot systems deployed in field applications has risen dramatically over the years. Nevertheless, supervising and operating multiple robots at once is a difficult task for a single operator to execute. In this paper we propose a novel approach for utilizing advising automated agents for assisting an operator to better manage a team of multiple robots in complex environments. We introduce the Myopic Advice Optimization (MYAO) Problem and exemplify its implementation using an
more » ... mentation using an agent for the Search And Rescue (SAR) task. Our intelligent advising agent was evaluated through extensive field trails, with 44 non-expert human operators and 10 low-cost mobile robots, in simulation and physical deployment, and showed a significant improvement in both team performance and the operator's satisfaction.
doi:10.1016/j.artint.2017.08.005 fatcat:6rbnkphz7zfxzaqpivpf6jfaxm

Leveraging human knowledge in tabular reinforcement learning: A study of human subjects [article]

Ariel Rosenfeld, Moshe Cohen, Matthew E. Taylor, Sarit Kraus
2018 arXiv   pre-print
Acknowledgment This article extends our previous reports from AAMAS 2017 (Rosenfeld et al. 2017b ) (short paper) and IJCAI 2017 (Rosenfeld et al. 2017a ) (full paper) in several major aspects: First,  ...  Then, in (Rosenfeld et al. 2017a) , the study was extended to include an additional 16 non-expert designers who implemented the QS-learning and QA-learning conditions as discussed in Experiment 1 (Section  ...  ROSENFELD, M. COHEN, M. E. TAYLOR AND S. KRAUS executed in random order.  ... 
arXiv:1805.05769v1 fatcat:ezenp2mskbhbjf6dgoswgeqrvi

Journal subject classification: intra- and inter-system discrepancies in Web Of Science and Scopus [article]

Shir Aviv-Reuven, Ariel Rosenfeld
2021 arXiv   pre-print
Journal classification into subject categories is an important aspect in scholarly research evaluation as well as in bibliometric analysis. Journal classification systems use a variety of (partially) overlapping and non-exhaustive subject categories which results in many journals being classified into more than a single subject category. As such, discrepancies are likely to be encountered within any given system and between different systems. In this study, we set to examine both types of
more » ... both types of discrepancies in the two most widely used indexing systems - Web Of Science and Scopus. We use known distance measures, as well as logical set theory to examine and compare the category schemes defined by these systems. Our results demonstrate significant discrepancies within each system where a higher number of classified categories correlates with increased range and variance of rankings within them, and where redundant categories are found. Our results also show significant discrepancies between the two system. Specifically, very few categories in one system are "similar" to categories in the second system, where "similarity" is measured by subset & interesting categories and minimally covering categories. Taken jointly, our findings suggest that both types of discrepancies are systematic and cannot be easily disregarded when relying on these subject classification systems.
arXiv:2107.12222v1 fatcat:4bbourfk2zbtpbbo2ycbhujenm

LBA: Online Learning-Based Assignment of Patients to Medical Professionals

Hanan Rosemarin, Ariel Rosenfeld, Steven Lapp, Sarit Kraus
2021 Sensors  
Central to any medical domain is the challenging patient to medical professional assignment task, aimed at getting the right patient to the right medical professional at the right time. This task is highly complex and involves partially conflicting objectives such as minimizing patient wait-time while providing maximal level of care. To tackle this challenge, medical institutions apply common scheduling heuristics to guide their decisions. These generic heuristics often do not align with the
more » ... t align with the expectations of each specific medical institution. In this article, we propose a novel learning-based online optimization approach we term Learning-Based Assignment (LBA), which provides decision makers with a tailored, data-centered decision support algorithm that facilitates dynamic, institution-specific multi-variate decisions, without altering existing medical workflows. We adapt our generic approach to two medical settings: (1) the assignment of patients to caregivers in an emergency department; and (2) the assignment of medical scans to radiologists. In an extensive empirical evaluation, using real-world data and medical experts' input from two distinctive medical domains, we show that our proposed approach provides a dynamic, robust and configurable data-driven solution which can significantly improve upon existing medical practices.
doi:10.3390/s21093021 pmid:33923098 fatcat:ck24u46lp5enjaqwkepgmpmq2y

Optimizing Traffic Enforcement: From the Lab to the Roads [chapter]

Ariel Rosenfeld, Oleg Maksimov, Sarit Kraus
2017 Lecture Notes in Computer Science  
Road accidents are the leading causes of death of youths and young adults worldwide. Efficient traffic enforcement has been conclusively shown to reduce high-risk driving behaviors and thus reduce accidents. Today, traffic police departments use simplified methods for their resource allocation (heuristics, accident hotspots, etc.). To address this potential shortcoming, in [23], we introduced a novel algorithmic solution, based on efficient optimization of the allocation of police resources,
more » ... olice resources, which relies on the prediction of accidents. This prediction can also be used for raising public awareness regarding road accidents. However, significant challenges arise when instantiating the proposed solution in real-world security settings. This paper reports on three main challenges: 1) Data-centric challenges; 2) Police-deployment challenges; and 3) Challenges in raising public awareness. We mainly focus on the data-centric challenge, highlighting the data collection and analysis, and provide a detailed description of how we tackled the challenge of predicting the likelihood of road accidents. We further outline the other two challenges, providing appropriate technical and methodological solutions including an open-access application for making our prediction model accessible to the public. Recently, the World Health Organization (WHO) has released a report on road traffic injuries and how they can be reduced [29] . The WHO mentioned that governments
doi:10.1007/978-3-319-68711-7_1 fatcat:dvmu6o23mjf2bbmjytnmcyxg7a

Optimal Auctions Capturing Constraints in Sponsored Search [chapter]

Esteban Feuerstein, Pablo Ariel Heiber, Matías Lopez-Rosenfeld, Marcelo Mydlarz
2009 Lecture Notes in Computer Science  
Most sponsored search auctions use the Generalized Second Price (GSP) rule. Given the GSP rule, they try to give an optimal allocation, an easy task when the only need is to allocate ads to slots. However, when other practical conditions must be fulfilled -such as budget constraints, exploration of the performance of new ads, etc.-optimal allocations are hard to obtain. We provide a method to optimally allocate ads to slots under the practical conditions mentioned above. Our auctions are
more » ... auctions are stochastic, and can be applied in tandem with different pricing rules, among which we highlight two: an intuitive generalization of GSP and VCG payments.
doi:10.1007/978-3-642-02158-9_17 fatcat:risdviuw4bfebkveyqpjgrhwzu

Advice Provision for Energy Saving in Automobile Climate-Control System

Amos Azaria, Ariel Rosenfeld, Sarit Kraus, Claudia V. Goldman, Omer Tsimhoni
2015 The AI Magazine  
See Rosenfeld et al. (2015) for a report about these new models and results.  ...  Rosenfeld et al. (2012) have showed that learning drivers' behavior can improve the use of the ACC system.  ...  Ariel Rosenfeld is a Ph.D. candidate at Bar-Ilan University, Israel. He received his B.Sc. in computer science and economics from Tel Aviv University, Israel in 2013.  ... 
doi:10.1609/aimag.v36i3.2603 fatcat:ahwb5pzv2zdwpoeoccbgzjlp2a

ACAT: A Novel Machine-Learning-Based Tool for Automating Android Application Testing [chapter]

Ariel Rosenfeld, Odaya Kardashov, Orel Zang
2017 Lecture Notes in Computer Science  
Mobile applications are being used every day by more than half of the world's population to perform a great variety of tasks. With the increasingly widespread usage of these applications, the need arises for efficient techniques to test them. Many frameworks allow automating the process of application testing, however existing frameworks mainly rely on the application developer for providing testing scripts for each developed application, thus preventing reuse of these tests for similar
more » ... for similar applications. In this demonstration, we present a novel tool for the automation of testing Android applications by leveraging machine learning techniques and reusing popular test scenarios. We discuss and demonstrate the potential benefits of our tool in an empirical study where we show it outperforms standard methods in realistic settings.
doi:10.1007/978-3-319-70389-3_14 fatcat:mtql6sms5jghbn2rjz266tabru

Optimal Cruiser-Drone Traffic Enforcement Under Energy Limitation

Ariel Rosenfeld, Oleg Maksimov, Sarit Kraus
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
., 2018; Rosenfeld and Kraus, ] . Police Perspective.  ...  As for eff, which measures the effectiveness of enforcement on any e t for any H t , it has been argued that the eff is submodular in H t (e.g., [Rosenfeld and Kraus, ; Elvik et al., 2009] ).  ... 
doi:10.24963/ijcai.2018/535 dblp:conf/ijcai/RosenfeldMK18 fatcat:42e67wrqozf65nvdbdxhgapucy

Editorial: ML and AI Safety, Effectiveness and Explainability in Healthcare

David Benrimoh, Sonia Israel, Robert Fratila, Caitrin Armstrong, Kelly Perlman, Ariel Rosenfeld, Adam Kapelner
2021 Frontiers in Big Data  
doi:10.3389/fdata.2021.727856 fatcat:cdatocpgcjf65gatqjicawmaui
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