Predicting Human Decision-Making: From Prediction to Action

Rosenfeld, Ariel; SaritKraus,

doi:10.2200/s00820ed1v01y201712aim036

Cited by 56 publications

(39 citation statements)

References 223 publications

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“…In this work we used a generalized explanation method, which did not differentiate between users. We intend to extend this research and build a fully-personalized user model [22], which will model the user's considerations in a RRS and provide explanations accordingly.…”

Section: Discussionmentioning

confidence: 99%

Providing explanations for recommendations in reciprocal environments

Kleinerman‬‏

Rosenfeld

Kraus

2018

Proceedings of the 12th ACM Conference on Recommender Systems

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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 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.

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Section: Discussionmentioning

confidence: 99%

Providing explanations for recommendations in reciprocal environments

Kleinerman‬‏

Rosenfeld

Kraus

2018

Proceedings of the 12th ACM Conference on Recommender Systems

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“…For example, Turing (2017) points out that big data and machine learning work based on algorithms designed to enable self-learning and adaptation. The problem with these algorithms, however, is that they can be biased, as a result of human-generated data-based subjective judgment, as opposed to the objective mathematical equations that underly machine learning, however efforts are being made in this regard, particularly in creating algorithms that can report how they got to specific answers (Agrawal, Gans and Goldfarb, 2018;Rosenfeld and Kraus, 2018). Therefore, it is necessary for specific algorithms that are listed in the public domain of important algorithms and have the political valence to establish a system of governance and regulation, while being an upgrade from intuition-based decision making (Gillespie, 2017;Selbst and Barocas, 2018).…”

Section: Technological Challenges and Public Value Theorymentioning

confidence: 99%

Best Practices of the Social Innovations in the Framework of the E-Government Evolution

Roblek¹,

Bach²,

Meško³

et al. 2020

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The social innovations have been together with the advanced technology in the 21 st century, taking an essential role in social structures and their informatization processes. Information technology has become an indispensable factor not only in industry and service businesses, but also in governing systems at the micro (cities and regions) and macro (state and nations) levels. The information processes, which includes automation, have in the last few years an important impact on the transformation from "classical" governance into the "smart governance". In the paper are presented the best practices which show how could social innovations, together with the advanced technology also lead to the several democratic changes in the urban environment. It can be concluded that it will in many ways reorganize public decisionmakings, create changes in democratic processes that are in accordance with socioeconomic and technological development, and will represent the basis for the emergence of the so-called the smartest social community and the ensuing novel processes of organization and operation.

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“…Similarly, an AI-based solution for controlling a robot's speed and direction changes, may be divided into explicit ES rules for computing rotation and acceleration parameters depending on the nature of the payload, path conditions and urgency, while 'smaller' AI-based solutions may be used to detect 'only' the input to these rules, i.e., the payload properties, path conditions or scheduling constraints expected by the user (see, e.g., (Rosenfeld and Kraus 2018)).…”

Section: Transitioning Ai Components Towards Esmentioning

confidence: 99%

Labor Division with Movable Walls: Composing Executable Specifications with Machine Learning and Search (Blue Sky Idea)

Harel

Marron

Rosenfeld

et al. 2019

AAAI

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Artificial intelligence (AI) techniques, including, e.g., machine learning, multi-agent collaboration, planning, and heuristic search, are emerging as ever-stronger tools for solving hard problems in real-world applications. Executable specification techniques (ES), including, e.g., Statecharts and scenario-based programming, is a promising development approach, offering intuitiveness, ease of enhancement, compositionality, and amenability to formal analysis. We propose an approach for integrating AI and ES techniques in developing complex intelligent systems, which can greatly simplify agile/spiral development and maintenance processes. The approach calls for automated detection of whether certain goals and sub-goals are met; a clear division between sub-goals solved with AI and those solved with ES; compositional and incremental addition of AI-based or ES-based components, each focusing on a particular gap between a current capability and a well-stated goal; and, iterative refinement of sub-goals solved with AI into smaller sub-sub-goals where some are solved with ES, and some with AI. We describe the principles of the approach and its advantages, as well as key challenges and suggestions for how to tackle them.

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Predicting Human Decision-Making: From Prediction to Action

Cited by 56 publications

References 223 publications

Providing explanations for recommendations in reciprocal environments

Providing explanations for recommendations in reciprocal environments

Best Practices of the Social Innovations in the Framework of the E-Government Evolution

Labor Division with Movable Walls: Composing Executable Specifications with Machine Learning and Search (Blue Sky Idea)

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