Alan Hartman

University of Haifa, Information Systems, Faculty Member

Afeka, Service Management and Engineering, Adjunct

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Alan Hartman is a senior lecturer at the University of Haifa, Department of Information Systems and an adjunct professor at the Afeka Academic College of Engineering, Department of Service Engineering. After a post-doctoral fellowship at the University of Waterloo in Canada, Alan joined the IBM Haifa Research Lab in 1983. Since then, his industrial research focused on storage technologies, mathematical optimization, hardware and software verification, and model based software, systems and services engineering. In parallel, he continued his pure mathematical research into combinatorial configurations. He spent almost 3 years at the IBM India research lab in Bangalore serving as the Services Science, Management, and Engineering (SSME) focal point for the IBM India Re-search Laboratory. He has held visiting positions in the Mathematics Department at the University of Toronto and at Telstra Research Labs. He has also coordinated and man-aged several European Commission research projects (AGEDIS, MODELWARE, MODELPLEX, COCKPIT). Alan has a Ph.D. in mathematics from the University of Newcastle in Australia, an M.Sc. in mathematics from the Technion – Israel Institute of Technology, and a B.Sc. in mathematics from Monash University in Australia. He has published over 70 research papers and holds 17 patents.
Supervisors: Roger Eggleton and Haim Hanani

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Papers by Alan Hartman

Method, system, and computer program product for automated test generation for non-deterministic software using state transition rules

Mitigating Bias in Algorithmic Systems - A Fish-Eye View

Mitigating bias in algorithmic systems is a critical issue drawing attention across communities w... more Mitigating bias in algorithmic systems is a critical issue drawing attention across communities within the information and computer sciences. Given the complexity of the problem and the involvement of multiple stakeholders – including developers, end-users and third-parties – there is a need to understand the landscape of the sources of bias, and the solutions being proposed to address them. This survey provides a "fish-eye view," examining approaches across four areas of research. The literature describes three steps toward a comprehensive treatment – bias detection, fairness management and explainability management – and underscores the need to work from within the system as well as from the perspective of stakeholders in the broader context.

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UML 2 Semantics Symposium: Formal Semantics for UML

The purpose of this symposium, held in conjunction with MoDELS 2006, was to present the current s... more The purpose of this symposium, held in conjunction with MoDELS 2006, was to present the current state of research of the UML 2 Semantics Project. Equally important to receiving feedback from an audience of experts was the opportunity to invite researchers in the field to discuss their own work related to a formal semantics for the Unified Modeling Language. This symposium is a follow-on to our first workshop, held in conjunction with ECMDA 2005.

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Method, system, and computer program product for automated test generation for non-deterministic software using state transition rules

Intersections of Steiner quadruple systems

Discrete Mathematics, 1992

Discrete Mathematics 104 (1992) 227244 227 NorthHolland Intersections of Steiner quadruple system... more

“End to End” Towards a Framework for Reducing Biases and Promoting Transparency of Algorithmic Systems

2019 14th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), 2019

Algorithms play an increasing role in our everyday lives. Recently, the harmful potential of bias... more Algorithms play an increasing role in our everyday lives. Recently, the harmful potential of biased algorithms has been recognized by researchers and practitioners. We have also witnessed a growing interest in ensuring the fairness and transparency of algorithmic systems. However, so far there is no agreed upon solution and not even an agreed terminology. The proposed research defines the problem space, solution space and a prototype of comprehensive framework for the detection and reducing biases in algorithmic systems.

A test execution environment running abstract tests for distributed software proceedings of sea 2002

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Educating Software and AI Stakeholders About Algorithmic Fairness, Accountability, Transparency and Ethics

International Journal of Artificial Intelligence in Education, 2021

This paper discusses educating stakeholders of algorithmic systems (systems that apply Artificial... more This paper discusses educating stakeholders of algorithmic systems (systems that apply Artificial Intelligence/Machine learning algorithms) in the areas of algorithmic fairness, accountability, transparency and ethics (FATE). We begin by establishing the need for such education and identifying the intended consumers of educational materials on the topic. We discuss the topics of greatest concern and in need of educational resources; we also survey the existing materials and past experiences in such education, noting the scarcity of suitable material on aspects of fairness in particular. We use an example of a college admission platform to illustrate our ideas. We conclude with recommendations for further work in the area and report on the first steps taken towards achieving this goal in the framework of an academic graduate seminar course, a graduate summer school, an embedded lecture in a software engineering course, and a workshop for high school teachers.

FairUMAP 2019 Chairs' Welcome Overview

Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization

It is our great pleasure to welcome you to the Second FairUMAP workshop at UMAP 2019. This full-d... more It is our great pleasure to welcome you to the Second FairUMAP workshop at UMAP 2019. This full-day workshop brings together researchers working at the intersection of user modeling, adaptation, and personalization on one hand, and bias, fairness and transparency in algorithmic systems on the other hand. The workshop was motivated by the observation that these two fields increasingly impact one another. Personalization has become a ubiquitous and essential part of systems that help users find relevant information in today's highly complex, information-rich online environments. Machine learning techniques applied to big data, as done by recommender systems, and user modeling in general, are key enabling technologies that allow intelligent systems to learn from users and adapt their output to users' needs and preferences. However, there has been a growing recognition that these underlying technologies raise novel ethical, legal, and policy challenges. It has become apparent that a single-minded focus on user characteristics has obscured other important and beneficial outcomes such systems must be able to deliver. System properties such as fairness, transparency, balance, and other social welfare considerations are not captured by typical metrics based on which data-driven personalized models are optimized. Indeed, widely-used personalization systems in popular sites such as Facebook, Google News and YouTube have been heavily criticized for personalizing information delivery too heavily at the cost of these other objectives.

Conceptual Modeling Approaches for Services Science

Over the last several years,services sciencehas emerged as an effective means to understand servi... more Over the last several years,services sciencehas emerged as an effective means to understand services and the socio-technical systems in which they are de- ployed. This systemic view requires a genuinely interdisciplinary approach to the study of services. In this chapter, we review a number of significant approaches to analyze, understand and model service systems, with an emphasis on showing sim- ilarities and differences that highlight the many aspects of a rich service ecosystem. The goal of this chapter is to provide developers with an overall perspective on such rich service system models, as a basis for choosing those which mostly fit their own needs.

From UML Activities to TAAL - Towards Behaviour-Preserving Model Transformations

Lecture Notes in Computer Science, 2008

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Workshop on Domain specific approaches to software test automation in conjunction with the 6th ESEC/FSE joint meeting - DOSTA '07

Welcome to the Workshop on Domain-Specific Approaches to Software Test Automation --- DoSTA 2007.... more Welcome to the Workshop on Domain-Specific Approaches to Software Test Automation --- DoSTA 2007. This is the first time a workshop dedicated for domain-specific issues in software test automation is being organized. We are expecting a day full of exciting presentations and fruitful discussions. We hope you can use this opportunity to network with your colleagues in this emerging field. There were paper submissions from seven countries: Brazil, Finland, France, Italy, Japan, Luxembourg, and the United States. Each paper was read by at least three reviewers. Six papers were accepted for presentation at the workshop and publication in these proceedings.

Algorithmic aspects of integral designs

Australas. J Comb., 1998

Alan Hartman Discrete Optimization Consulting Pty. Ltd. 1 Irene Place, Prahran, VIC. 3181 Austral... more Alan Hartman Discrete Optimization Consulting Pty. Ltd. 1 Irene Place, Prahran, VIC. 3181 Australia IBM Israel Science and Technology MATAM Haifa, 31905 Israel email: alan_hartman@vneUbm.com June 1997 A t-design with parameters t (v, k, A.) is a set X of cardinality v, together with a collection of subsets of X of size k called blocks. The number of blocks containing any particular t-subset of X is a constant A.. The simplest t-designs are regular multi-graphs (t=1, k=2, v= number of vertices, A. = degree of regularity). Necessary conditions on the four parameters for the existence of t-designs are simply obtained by counting the number of blocks containing a particular i-subset of X for any 0 ::; i ::; t. In general these necessary conditions are not sufficient for the existence of t-designs. However these simple counting conditions are sufficient for the existence of so-called "integral designs" which are an extension of the definition of t-designs, allowing blocks to ha...

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Model Driven Architecture - Foundations and Applications, 4th European Conference, ECMDA-FA 2008, Berlin, Germany, June 9-13, 2008. Proceedings

Ecmdafa, 2008

... Doucet Philippa Conmy Cedric Dumoulin Michelle L. Crane Hajo Eichler Cesar de Moura Filho Vin... more