85,391 research outputs found

    Responsible Scoring Mechanisms Through Function Sampling

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    Human decision-makers often receive assistance from data-driven algorithmic systems that provide a score for evaluating objects, including individuals. The scores are generated by a function (mechanism) that takes a set of features as input and generates a score.The scoring functions are either machine-learned or human-designed and can be used for different decision purposes such as ranking or classification. Given the potential impact of these scoring mechanisms on individuals' lives and on society, it is important to make sure these scores are computed responsibly. Hence we need tools for responsible scoring mechanism design. In this paper, focusing on linear scoring functions, we highlight the importance of unbiased function sampling and perturbation in the function space for devising such tools. We provide unbiased samplers for the entire function space, as well as a Īø\theta-vicinity around a given function. We then illustrate the value of these samplers for designing effective algorithms in three diverse problem scenarios in the context of ranking. Finally, as a fundamental method for designing responsible scoring mechanisms, we propose a novel approach for approximating the construction of the arrangement of hyperplanes. Despite the exponential complexity of an arrangement in the number of dimensions, using function sampling, our algorithm is linear in the number of samples and hyperplanes, and independent of the number of dimensions

    ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems

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    Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest. The learning machine leverages big data to find examples that maximize the training value of its interaction with the teacher. When the teacher is restricted to labeling examples selected by the machine, this problem is an instance of active learning. When the teacher can provide additional information to the machine (e.g., suggestions on what examples or predictive features should be used) as the learning task progresses, then the problem becomes one of interactive learning. To accommodate the two-way communication channel needed for efficient interactive learning, the teacher and the machine need an environment that supports an interaction language. The machine can access, process, and summarize more examples than the teacher can see in a lifetime. Based on the machine's output, the teacher can revise the definition of the task or make it more precise. Both the teacher and the machine continuously learn and benefit from the interaction. We have built a platform to (1) produce valuable and deployable models and (2) support research on both the machine learning and user interface challenges of the interactive learning problem. The platform relies on a dedicated, low-latency, distributed, in-memory architecture that allows us to construct web-scale learning machines with quick interaction speed. The purpose of this paper is to describe this architecture and demonstrate how it supports our research efforts. Preliminary results are presented as illustrations of the architecture but are not the primary focus of the paper

    An Evaluation of the Sustainability of Global Tuna Stocks Relative to Marine Stewardship Council Criteria

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    The Marine Stewardship Council (MSC) has established a program whereby a fishery may be certified as being sustainable. The sustainability of a fishery is defined by MSC criteria which are embodied in three Principles: relating to the status of the stock, the ecosystem of which the stock is a member and the fishery management system. Since many of these MSC criteria are comparable for global tuna stocks, the MSC scoring system was used to evaluate nineteen stocks of tropical and temperate tunas throughout the world and to evaluate the management systems of the Regional Fishery Management Organizations (RFMO) associated with these stocks

    Computationally designed libraries of fluorescent proteins evaluated by preservation and diversity of function

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    To determine which of seven library design algorithms best introduces new protein function without destroying it altogether, seven combinatorial libraries of green fluorescent protein variants were designed and synthesized. Each was evaluated by distributions of emission intensity and color compiled from measurements made in vivo. Additional comparisons were made with a library constructed by error-prone PCR. Among the designed libraries, fluorescent function was preserved for the greatest fraction of samples in a library designed by using a structure-based computational method developed and described here. A trend was observed toward greater diversity of color in designed libraries that better preserved fluorescence. Contrary to trends observed among libraries constructed by error-prone PCR, preservation of function was observed to increase with a library's average mutation level among the four libraries designed with structure-based computational methods

    Interactive Data Exploration with Smart Drill-Down

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    We present {\em smart drill-down}, an operator for interactively exploring a relational table to discover and summarize "interesting" groups of tuples. Each group of tuples is described by a {\em rule}. For instance, the rule (a,b,ā‹†,1000)(a, b, \star, 1000) tells us that there are a thousand tuples with value aa in the first column and bb in the second column (and any value in the third column). Smart drill-down presents an analyst with a list of rules that together describe interesting aspects of the table. The analyst can tailor the definition of interesting, and can interactively apply smart drill-down on an existing rule to explore that part of the table. We demonstrate that the underlying optimization problems are {\sc NP-Hard}, and describe an algorithm for finding the approximately optimal list of rules to display when the user uses a smart drill-down, and a dynamic sampling scheme for efficiently interacting with large tables. Finally, we perform experiments on real datasets on our experimental prototype to demonstrate the usefulness of smart drill-down and study the performance of our algorithms

    Pathways to Accountability II

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    This report summarises the results of the 2009-2010 review process on the One World Trust Global Accountability Framework and the piloting of the draft framework during 2011, and presents the full One World Trust Pathways to Accountability II indicator framework. Our work in this field work is motivated by a concern about the persisting weakness and insufficient effectiveness of global organisations from all sectors in responding to the challenge of delivering global public goods to citizens and communities, the very people whom they claim to serve and benefit, and who are most often dependent on them
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