Discrimination-aware classification

Classifier construction is one of the most researched topics within the data mining and machine learning communities. Literally thousands of algorithms have been proposed. The quality of the learned models, however, depends critically on the quality of the training data. No matter which classifier inducer is applied, if the training data is incorrect, poor models will result. In this thesis, we study cases in which the input data is discriminatory and we are supposed to learn a classifier that optimizes accuracy, but does not discriminate in its predictions. Such situations occur naturally as artifacts of the data collection process when the training data is collected from different sources with different labeling criteria, when the data is generated by a biased decision process, or when the sensitive attribute, e.g., gender serves as a proxy for unobserved features. In many situations, a classifier that detects and uses the racial or gender discrimination is undesirable for legal reasons. The concept of discrimination is illustrated by the next example: Throughout the years, an employment bureau recorded various parameters of job candidates. Based on these parameters, the company wants to learn a model for partially automating the matchmaking between a job and a job candidate. A match is labeled as successful if the company hires the applicant. It turns out, however, that the historical data is biased; for higher board functions, Caucasian males are systematically being favored. A model learned directly on this data will learn this discriminatory behavior and apply it over future predictions. From an ethical and legal point of view it is of course unacceptable that a model discriminating in this way is deployed. Our proposed solutions to the discrimination problem fall into two broad categories. First, we propose pre-processing methods to remove the discrimination from the training dataset. Second, we propose solutions to the discrimination problem by directly pushing the non-discrimination constraints into classification models and post-processing of built models. We further studied the discrimination-aware classification paradigm in the presence of explanatory attributes that were correlated with the sensitive attribute, e.g., low income may be explained by the low education level. In such a case, as we show, not all discrimination can be considered bad. Therefore, we introduce a new way of measuring discrimination, by explicitly splitting it up into explainable and bad discrimination and propose methods to remove the bad discrimination only. We tried our discrimination-aware methods over real world data sets. We observed in our experiments that our methods show promising results and clearly outperform the traditional classification model w.r.t. accuracy discrimination trade-off. To conclude, we believe that discrimination-aware classification is a new and exciting area of research addressing a societally relevant problem

Discrimination-aware classification

Classifier construction is one of the most researched topics within the data mining and machine learning communities. Literally thousands of algorithms have been proposed. The quality of the learned models, however, depends critically on the quality of the training data. No matter which classifier inducer is applied, if the training data is incorrect, poor models will result. In this thesis, we study cases in which the input data is discriminatory and we are supposed to learn a classifier that optimizes accuracy, but does not discriminate in its predictions. Such situations occur naturally as artifacts of the data collection process when the training data is collected from different sources with different labeling criteria, when the data is generated by a biased decision process, or when the sensitive attribute, e.g., gender serves as a proxy for unobserved features. In many situations, a classifier that detects and uses the racial or gender discrimination is undesirable for legal reasons. The concept of discrimination is illustrated by the next example: Throughout the years, an employment bureau recorded various parameters of job candidates. Based on these parameters, the company wants to learn a model for partially automating the matchmaking between a job and a job candidate. A match is labeled as successful if the company hires the applicant. It turns out, however, that the historical data is biased; for higher board functions, Caucasian males are systematically being favored. A model learned directly on this data will learn this discriminatory behavior and apply it over future predictions. From an ethical and legal point of view it is of course unacceptable that a model discriminating in this way is deployed. Our proposed solutions to the discrimination problem fall into two broad categories. First, we propose pre-processing methods to remove the discrimination from the training dataset. Second, we propose solutions to the discrimination problem by directly pushing the non-discrimination constraints into classification models and post-processing of built models. We further studied the discrimination-aware classification paradigm in the presence of explanatory attributes that were correlated with the sensitive attribute, e.g., low income may be explained by the low education level. In such a case, as we show, not all discrimination can be considered bad. Therefore, we introduce a new way of measuring discrimination, by explicitly splitting it up into explainable and bad discrimination and propose methods to remove the bad discrimination only. We tried our discrimination-aware methods over real world data sets. We observed in our experiments that our methods show promising results and clearly outperform the traditional classification model w.r.t. accuracy discrimination trade-off. To conclude, we believe that discrimination-aware classification is a new and exciting area of research addressing a societally relevant problem

Supporting the tutor in the design and support of adaptive e-learning

The further development and deployment of e-learning faces a number of threats. First, in order to meet the increasing demands of learners, staff have to develop and plan a wide and complex variety of learning activities that, in line with contemporary pedagogical models, adapt to the learners’ individual needs. Second, the deployment of e-learning, and therewith the freedom to design the appropriate kind of activities is bound by strict economical conditions, i.e. the amount of time available to staff to support the learning process. In this thesis two models have been developed and implemented that each address a different need. The first model covers the need to support the design task of staff, the second one the need to support the staff in supervising and giving guidance to students' learning activities. More specifically, the first model alleviates the design task by offering a set of connected design and runtime tools that facilitate adaptive e-learning. The second model alleviates the support task by invoking the knowledge and skills of fellow-students. Both models have been validated in near-real-world task settings

Energy-Efficient Software

The energy consumption of ICT is growing at an unprecedented pace. The main drivers for this growth are the widespread diffusion of mobile devices and the proliferation of datacenters, the most power-hungry IT facilities. In addition, it is predicted that the demand for ICT technologies and services will increase in the coming years. Finding solutions to decrease ICT energy footprint is and will be a top priority for researchers and professionals in the field. As a matter of fact, hardware technology has substantially improved throughout the years: modern ICT devices are definitely more energy efficient than their predecessors, in terms of performance per watt. However, as recent studies show, these improvements are not effectively reducing the growth rate of ICT energy consumption. This suggests that these devices are not used in an energy-efficient way. Hence, we have to look at software. Modern software applications are not designed and implemented with energy efficiency in mind. As hardware became more and more powerful (and cheaper), software developers were not concerned anymore with optimizing resource usage. Rather, they focused on providing additional features, adding layers of abstraction and complexity to their products. This ultimately resulted in bloated, slow software applications that waste hardware resources -- and consequently, energy. In this dissertation, the relationship between software behavior and hardware energy consumption is explored in detail. For this purpose, the abstraction levels of software are traversed upwards, from source code to architectural components. Empirical research methods and evidence-based software engineering approaches serve as a basis. First of all, this dissertation shows the relevance of software over energy consumption. Secondly, it gives examples of best practices and tactics that can be adopted to improve software energy efficiency, or design energy-efficient software from scratch. Finally, this knowledge is synthesized in a conceptual framework that gives the reader an overview of possible strategies for software energy efficiency, along with examples and suggestions for future research

Paving the Way for Lifelong Learning:Facilitating competence development through a learning path specification

Janssen, J. (2010). Paving the Way for Lifelong Learning. Facilitating competence development through a learning path specification. September, 17, 2010, Heerlen, The Netherlands: Open University of the Netherlands, CELSTEC. SIKS Dissertation Series No. 2010-36. ISBN 978-90-79447-43-5Efficient and effective lifelong learning requires that learners can make well informed decisions regarding the selection of a learning path, i.e. a set of learning actions that help attain particular learning goals. In recent decades a strong emphasis on lifelong learning has led educational provision to expand and to become more varied and flexible. Besides, the role of informal learning has become increasingly acknowledged. In light of these developments this thesis addresses the question: How to support learners in finding their way through all available options and selecting a learning path that best fit their needs? The thesis describes two different approaches regarding the provision of way finding support, which can be considered complementary. The first, inductive approach proposes to provide recommendations based on indirect social interaction: analysing the paths followed by other learners and feeding this information back as advice to learners facing navigational decisions. The second, prescriptive approach proposes to use a learning path specification to describe both the contents and the structure of any learning path in a formal and uniform way. This facilitates comparison and selection of learning paths across institutions and systems, but also enables automated provision of way finding support for a chosen learning path. Moreover, it facilitates automated personalisation of a learning path, i.e. adapting the learning path to the needs of a particular learner. Following the first approach a recommender system was developed and tested in an experimental setting. Results showed use of the system significantly enhanced effectiveness of learning. In line with the second approach a learning path specification was developed and validated in three successive evaluations. Firstly, an investigation of lifelong learners’ information needs. Secondly, an evaluation of the specification through a reference (sample) implementation: a tool to describe learning paths according to the specification. Finally, an evaluation of the use and purpose of this tool involving prospective end-users: study advisors and learning designers. Following the various evaluations the Learning Path Specification underwent some changes over time. Results described in this thesis show that the proposed approach of the Learning Path Specification and the reference implementation were well received by end-users.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

Navigation Support for Learners in Informal Learning Networks

Learners increasingly use the Internet as source to find suitable information for their learning needs. This especially applies to informal learning that takes place during daily activities that are related to work and private life. Unfortunately, the Internet is overwhelming which makes it difficult to get an overview and to select the most suitable information. Navigation support may help to reduce time and costs involved selecting suitable information on the Internet. Promising technologies are recommender systems known from e-commerce systems like Amazon.com. They match customers with a similar taste of products and create a kind ‘neighborhood’ of likeminded customers. They look for related products purchased by the neighbors and recommend these to the current customer. In this thesis we explore the application of recommender systems to offer personalized navigation support to learners in informal Learning Networks. A model of a recommender system for informal Learning Networks is proposed that takes into account pedagogical characteristics and combines them with collaborative filtering algorithms. Which learning activities are most suitable depends on needs, preferences and goals of individual learners. Following this approach we have conducted two empirical studies. The results of these studies showed that the application of recommender systems for navigation support in informal Learning Networks is promising when supporting learners to select most suitable learning activities according to their individual needs, preferences and goals. Based on these results we introduce a technical prototype which allows us to offer navigation support to lifelong learners in informal Learning Networks

Design and Implementation Strategies for IMS Learning Design

SIKS Dissertation Series No. 2008-27The IMS Learning Design (LD) specification, which has been released in February 2003, is a generic and flexible language for describing the learning practice and underlying learning designs using a formal notation which is computer-interpretable. It is based on a pedagogical meta-model (Koper & Manderveld, 2004) and supports the use of a wide range of pedagogies. It supports adaptation of individual learning routes and orchestrates interactions between users in various learning and support roles. A formalized learning design can be applied repeatedly in similar situations with different persons and contexts. Yet because IMS Learning Design is a fairly complex and elaborate specification, it can be difficult to grasp; furthermore, designing and implementing a runtime environment for the specification is far from straightforward. That IMS Learning Design makes use of other specifications and e-learning services adds further to this complexity for both its users and the software developers. For this new specification to succeed, therefore, a reference runtime implementation was needed. To this end, this thesis addresses two research and development issues. First, it investigates research into and development of a reusable reference runtime environment for IMS Learning Design. The resulting runtime, called CopperCore, provides a reference both for users of the specification and for software developers. The latter can reuse the design principles presented in this thesis for their own implementations, or reuse the CopperCore product through the interfaces provided. Second, this thesis addresses the integration of other specifications and e-learning services during runtime. It presents an architecture and implementation (CopperCore Service Integration) which provides an extensible lightweight solution to the problem. Both developments have been tested through real-world use in projects carried out by the IMS Learning Design community. The results have generally been positive, and have led us to conclude that we successfully addressed both the research and development issues. However, the results also indicate that the LD tooling lacks maturity, particularly in the authoring area. Through close integration of CopperCore with a product called the Personal Competence Manager, we demonstrate that a complementary approach to authoring in IMS Learning Design solves some of these issues