Roman Lukyanenko - Academia.edu (original) (raw)
Papers by Roman Lukyanenko
BMC Bioinformatics
Background All aspects of our society, including the life sciences, need a mechanism for people w... more Background All aspects of our society, including the life sciences, need a mechanism for people working within them to represent the concepts they employ to carry out their research. For the information systems being designed and developed to support researchers and scientists in conducting their work, conceptual models of the relevant domains are usually designed as both blueprints for a system being developed and as a means of communication between the designer and developer. Most conceptual modelling concepts are generic in the sense that they are applied with the same understanding across many applications. Problems in the life sciences, however, are especially complex and important, because they deal with humans, their well-being, and their interactions with the environment as well as other organisms. Results This work proposes a “systemist” perspective for creating a conceptual model of a life scientist’s problem. We introduce the notion of a system and then show how it can be...
Information Systems Research
Elevator Pitch A quiet revolution is happening in the offices, cubicles, and boardrooms of the wo... more Elevator Pitch A quiet revolution is happening in the offices, cubicles, and boardrooms of the world. Non-IT professionals are becoming empowered by leveraging organizational data for analytics. We support this movement by offering a powerful way to make data more usable via a combination of graphical conceptual models with narratives. Longer Version We are witnessing a quiet revolution—the rise of empowered users. These non-IT professionals increasingly seek to leverage the ever-expanding amount of organizational data for analytics to support their initiatives, decisions, and actions. All too often, however, the enthusiasm of these users collides against the harsh reality—many of them lack sophisticated IT skills, and they struggle to find/access relevant data, understand their meaning, and extract and adapt them to meet their needs. We propose a powerful way to support empowered users with a combination of conceptual models and narratives. Conceptual models are diagrams that accur...
Electronic Markets
With the rise of artificial intelligence (AI), the issue of trust in AI emerges as a paramount so... more With the rise of artificial intelligence (AI), the issue of trust in AI emerges as a paramount societal concern. Despite increased attention of researchers, the topic remains fragmented without a common conceptual and theoretical foundation. To facilitate systematic research on this topic, we develop a Foundational Trust Framework to provide a conceptual, theoretical, and methodological foundation for trust research in general. The framework positions trust in general and trust in AI specifically as a problem of interaction among systems and applies systems thinking and general systems theory to trust and trust in AI. The Foundational Trust Framework is then used to gain a deeper understanding of the nature of trust in AI. From doing so, a research agenda emerges that proposes significant questions to facilitate further advances in empirical, theoretical, and design research on trust in AI.
MIS Quarterly, 2019
As crowdsourced user-generated content becomes an important source of data for organizations, a p... more As crowdsourced user-generated content becomes an important source of data for organizations, a pressing
question is how to ensure that data contributed by ordinary people outside of traditional organizational
boundaries is of suitable quality to be useful for both known and unanticipated purposes. This research
examines the impact of different information quality management strategies, and corresponding data collection
design choices, on key dimensions of information quality in crowdsourced user-generated content. We conceptualize
a contributor-centric information quality management approach focusing on instance-based data
collection. We contrast it with the traditional consumer-centric fitness-for-use conceptualization of information
quality that emphasizes class-based data collection. We present laboratory and field experiments conducted
in a citizen science domain that demonstrate trade-offs between the quality dimensions of accuracy, completeness
(including discoveries), and precision between the two information management approaches and their
corresponding data collection designs. Specifically, we show that instance-based data collection results in
higher accuracy, dataset completeness, and number of discoveries, but this comes at the expense of lower
precision. We further validate the practical value of the instance-based approach by conducting an applicability
check with potential data consumers (scientists, in our context of citizen science). In a follow-up study,
we show, using human experts and supervised machine learning techniques, that substantial precision gains
on instance-based data can be achieved with post-processing. We conclude by discussing the benefits and
limitations of different information quality and data collection design choices for information quality in
crowdsourced user-generated content.
Data & Knowledge Engineering
Americas Conference on Information Systems, 2021
Artificial Intelligence is increasingly driven by powerful but often opaque machine learning algo... more Artificial Intelligence is increasingly driven by powerful but often opaque machine learning algorithms. These black-box algorithms achieve high performance but are not explainable to humans in a systematic and interpretable manner, a challenge known as Explainable AI (XAI). Informed by a synthesis of two converging literature streams on information systems development and psychology, we propose a new XAI approach termed Basic Explainable AI and a subsequent research agenda. We propose four research directions that focus on providing explanations by proactively considering the target audience\u27s mental models and making the explanations maximally accessible to heterogeneous nonexpert users
Original citation Lukyanenko, R., Samuel, B. M., Castellanos, A. & Maddah, M. 2016. On semantics-... more Original citation Lukyanenko, R., Samuel, B. M., Castellanos, A. & Maddah, M. 2016. On semantics-contingent syntax for conceptual modelling. In: Parsons, J., Tuunanen, T., Venable, J. R., Helfert, M., Donnellan, B., & Kenneally, J. (eds.) Breakthroughs and Emerging Insights from Ongoing Design Science Projects: Research-in-progress papers and poster presentations from the 11th International Conference on Design Science Research in Information Systems and Technology (DESRIST) 2016. St. John, Canada, 23-25 May. pp. 98-99
Much of conceptual modeling research over recent times has been guided by a seminal research agen... more Much of conceptual modeling research over recent times has been guided by a seminal research agenda developed by Wand and Weber (2002), which identified twenty-two research opportunities. In this paper, we explore whether existing research has provided sufficient answers to these questions. Our findings from a review of the literature show a dialectic: several of the opportunities noted in 2002 have been addressed substantially while others have been entirely neglected. We also found several path breaking studies that addressed problems not spotted by the initial framework. To stimulate a forward-looking wave of conceptual modeling research, we provide a new framework that draws the attention of conceptual modeling research to the interplay between digital representations and outcomes.
Although much research continues to be carried out on modeling of information systems, there has ... more Although much research continues to be carried out on modeling of information systems, there has been a lack ofwork that relates the activities ofmodeling to human mental models. With the increased emphasis on machine learning systems, model development remains an important issue. In this research, we propose a framework for progressing from humanmental models to machine learning models and implementation via the use of conceptual models. The framework is illustrated by an application to a citizen science project. Recommendations for the use of the framework are proposed.
Lecture Notes in Computer Science, 2021
Lecture Notes in Computer Science, 2016
As crowdsourced user-generated content becomes an important source of data for organizations, a p... more As crowdsourced user-generated content becomes an important source of data for organizations, a pressing question is how to ensure that data contributed by ordinary people outside of traditional organizational boundaries is of suitable quality to be useful for both known and unanticipated purposes. This research examines the impact of different information quality management strategies, and corresponding data collection design choices, on key dimensions of information quality in crowdsourced user-generated content. We conceptualize a contributor-centric information quality management approach focusing on instance-based data collection. We contrast it with the traditional consumer-centric fitness-for-use conceptualization of information quality that emphasizes class-based data collection. We present laboratory and field experiments conducted in a citizen science domain that demonstrate trade-offs between the quality dimensions of accuracy, completeness (including discoveries), and precision between the two information management approaches and their corresponding data collection designs. Specifically, we show that instance-based data collection results in higher accuracy, dataset completeness, and number of discoveries, but this comes at the expense of lower precision. We further validate the practical value of the instance-based approach by conducting an applicability check with potential data consumers (scientists, in our context of citizen science). In a follow-up study, we show, using human experts and supervised machine learning techniques, that substantial precision gains on instance-based data can be achieved with post-processing. We conclude by discussing the benefits and limitations of different information quality and data collection design choices for information quality in crowdsourced user-generated content.
rose.geog.mcgill.ca
Page 1. Proceedings of Spatial Knowledge and Information - Canada (SKI-Canada) 2011, March 3-6 in... more Page 1. Proceedings of Spatial Knowledge and Information - Canada (SKI-Canada) 2011, March 3-6 in Fernie BC, Canada. Volume 1 Proceedings Editor Renee Sieber Executive Committee Scott Bell, University of Saskatchewan ...
ACM Transactions on Management Information Systems, 2017
Recent decade has seen a dramatic change in the information systems landscape that alters the way... more Recent decade has seen a dramatic change in the information systems landscape that alters the ways we design and interact with information technologies, including such developments as the rise of business analytics, user-generated content, and NoSQL databases, to name just a few. These changes challenge conceptual modeling research to offer innovative solutions tailored to these environments. Conceptual models typically represent classes (categories, kinds) of objects rather than concrete specific objects, making the class construct a critical medium for capturing domain semantics. While representation of classes may differ between grammars, a common design assumption is what we term different semantics same syntax (D3S). Under D3S, all classes are depicted using the same syntactic symbols. Following recent findings in psychology, we introduce a novel assumption semantics-contingent syntax (SCS) whereby syntactic representations of classes in conceptual models may differ based on th...
Communications of the ACM, 2022
Opening the black box or Pandora's Box?
BMC Bioinformatics
Background All aspects of our society, including the life sciences, need a mechanism for people w... more Background All aspects of our society, including the life sciences, need a mechanism for people working within them to represent the concepts they employ to carry out their research. For the information systems being designed and developed to support researchers and scientists in conducting their work, conceptual models of the relevant domains are usually designed as both blueprints for a system being developed and as a means of communication between the designer and developer. Most conceptual modelling concepts are generic in the sense that they are applied with the same understanding across many applications. Problems in the life sciences, however, are especially complex and important, because they deal with humans, their well-being, and their interactions with the environment as well as other organisms. Results This work proposes a “systemist” perspective for creating a conceptual model of a life scientist’s problem. We introduce the notion of a system and then show how it can be...
Information Systems Research
Elevator Pitch A quiet revolution is happening in the offices, cubicles, and boardrooms of the wo... more Elevator Pitch A quiet revolution is happening in the offices, cubicles, and boardrooms of the world. Non-IT professionals are becoming empowered by leveraging organizational data for analytics. We support this movement by offering a powerful way to make data more usable via a combination of graphical conceptual models with narratives. Longer Version We are witnessing a quiet revolution—the rise of empowered users. These non-IT professionals increasingly seek to leverage the ever-expanding amount of organizational data for analytics to support their initiatives, decisions, and actions. All too often, however, the enthusiasm of these users collides against the harsh reality—many of them lack sophisticated IT skills, and they struggle to find/access relevant data, understand their meaning, and extract and adapt them to meet their needs. We propose a powerful way to support empowered users with a combination of conceptual models and narratives. Conceptual models are diagrams that accur...
Electronic Markets
With the rise of artificial intelligence (AI), the issue of trust in AI emerges as a paramount so... more With the rise of artificial intelligence (AI), the issue of trust in AI emerges as a paramount societal concern. Despite increased attention of researchers, the topic remains fragmented without a common conceptual and theoretical foundation. To facilitate systematic research on this topic, we develop a Foundational Trust Framework to provide a conceptual, theoretical, and methodological foundation for trust research in general. The framework positions trust in general and trust in AI specifically as a problem of interaction among systems and applies systems thinking and general systems theory to trust and trust in AI. The Foundational Trust Framework is then used to gain a deeper understanding of the nature of trust in AI. From doing so, a research agenda emerges that proposes significant questions to facilitate further advances in empirical, theoretical, and design research on trust in AI.
MIS Quarterly, 2019
As crowdsourced user-generated content becomes an important source of data for organizations, a p... more As crowdsourced user-generated content becomes an important source of data for organizations, a pressing
question is how to ensure that data contributed by ordinary people outside of traditional organizational
boundaries is of suitable quality to be useful for both known and unanticipated purposes. This research
examines the impact of different information quality management strategies, and corresponding data collection
design choices, on key dimensions of information quality in crowdsourced user-generated content. We conceptualize
a contributor-centric information quality management approach focusing on instance-based data
collection. We contrast it with the traditional consumer-centric fitness-for-use conceptualization of information
quality that emphasizes class-based data collection. We present laboratory and field experiments conducted
in a citizen science domain that demonstrate trade-offs between the quality dimensions of accuracy, completeness
(including discoveries), and precision between the two information management approaches and their
corresponding data collection designs. Specifically, we show that instance-based data collection results in
higher accuracy, dataset completeness, and number of discoveries, but this comes at the expense of lower
precision. We further validate the practical value of the instance-based approach by conducting an applicability
check with potential data consumers (scientists, in our context of citizen science). In a follow-up study,
we show, using human experts and supervised machine learning techniques, that substantial precision gains
on instance-based data can be achieved with post-processing. We conclude by discussing the benefits and
limitations of different information quality and data collection design choices for information quality in
crowdsourced user-generated content.
Data & Knowledge Engineering
Americas Conference on Information Systems, 2021
Artificial Intelligence is increasingly driven by powerful but often opaque machine learning algo... more Artificial Intelligence is increasingly driven by powerful but often opaque machine learning algorithms. These black-box algorithms achieve high performance but are not explainable to humans in a systematic and interpretable manner, a challenge known as Explainable AI (XAI). Informed by a synthesis of two converging literature streams on information systems development and psychology, we propose a new XAI approach termed Basic Explainable AI and a subsequent research agenda. We propose four research directions that focus on providing explanations by proactively considering the target audience\u27s mental models and making the explanations maximally accessible to heterogeneous nonexpert users
Original citation Lukyanenko, R., Samuel, B. M., Castellanos, A. & Maddah, M. 2016. On semantics-... more Original citation Lukyanenko, R., Samuel, B. M., Castellanos, A. & Maddah, M. 2016. On semantics-contingent syntax for conceptual modelling. In: Parsons, J., Tuunanen, T., Venable, J. R., Helfert, M., Donnellan, B., & Kenneally, J. (eds.) Breakthroughs and Emerging Insights from Ongoing Design Science Projects: Research-in-progress papers and poster presentations from the 11th International Conference on Design Science Research in Information Systems and Technology (DESRIST) 2016. St. John, Canada, 23-25 May. pp. 98-99
Much of conceptual modeling research over recent times has been guided by a seminal research agen... more Much of conceptual modeling research over recent times has been guided by a seminal research agenda developed by Wand and Weber (2002), which identified twenty-two research opportunities. In this paper, we explore whether existing research has provided sufficient answers to these questions. Our findings from a review of the literature show a dialectic: several of the opportunities noted in 2002 have been addressed substantially while others have been entirely neglected. We also found several path breaking studies that addressed problems not spotted by the initial framework. To stimulate a forward-looking wave of conceptual modeling research, we provide a new framework that draws the attention of conceptual modeling research to the interplay between digital representations and outcomes.
Although much research continues to be carried out on modeling of information systems, there has ... more Although much research continues to be carried out on modeling of information systems, there has been a lack ofwork that relates the activities ofmodeling to human mental models. With the increased emphasis on machine learning systems, model development remains an important issue. In this research, we propose a framework for progressing from humanmental models to machine learning models and implementation via the use of conceptual models. The framework is illustrated by an application to a citizen science project. Recommendations for the use of the framework are proposed.
Lecture Notes in Computer Science, 2021
Lecture Notes in Computer Science, 2016
As crowdsourced user-generated content becomes an important source of data for organizations, a p... more As crowdsourced user-generated content becomes an important source of data for organizations, a pressing question is how to ensure that data contributed by ordinary people outside of traditional organizational boundaries is of suitable quality to be useful for both known and unanticipated purposes. This research examines the impact of different information quality management strategies, and corresponding data collection design choices, on key dimensions of information quality in crowdsourced user-generated content. We conceptualize a contributor-centric information quality management approach focusing on instance-based data collection. We contrast it with the traditional consumer-centric fitness-for-use conceptualization of information quality that emphasizes class-based data collection. We present laboratory and field experiments conducted in a citizen science domain that demonstrate trade-offs between the quality dimensions of accuracy, completeness (including discoveries), and precision between the two information management approaches and their corresponding data collection designs. Specifically, we show that instance-based data collection results in higher accuracy, dataset completeness, and number of discoveries, but this comes at the expense of lower precision. We further validate the practical value of the instance-based approach by conducting an applicability check with potential data consumers (scientists, in our context of citizen science). In a follow-up study, we show, using human experts and supervised machine learning techniques, that substantial precision gains on instance-based data can be achieved with post-processing. We conclude by discussing the benefits and limitations of different information quality and data collection design choices for information quality in crowdsourced user-generated content.
rose.geog.mcgill.ca
Page 1. Proceedings of Spatial Knowledge and Information - Canada (SKI-Canada) 2011, March 3-6 in... more Page 1. Proceedings of Spatial Knowledge and Information - Canada (SKI-Canada) 2011, March 3-6 in Fernie BC, Canada. Volume 1 Proceedings Editor Renee Sieber Executive Committee Scott Bell, University of Saskatchewan ...
ACM Transactions on Management Information Systems, 2017
Recent decade has seen a dramatic change in the information systems landscape that alters the way... more Recent decade has seen a dramatic change in the information systems landscape that alters the ways we design and interact with information technologies, including such developments as the rise of business analytics, user-generated content, and NoSQL databases, to name just a few. These changes challenge conceptual modeling research to offer innovative solutions tailored to these environments. Conceptual models typically represent classes (categories, kinds) of objects rather than concrete specific objects, making the class construct a critical medium for capturing domain semantics. While representation of classes may differ between grammars, a common design assumption is what we term different semantics same syntax (D3S). Under D3S, all classes are depicted using the same syntactic symbols. Following recent findings in psychology, we introduce a novel assumption semantics-contingent syntax (SCS) whereby syntactic representations of classes in conceptual models may differ based on th...
Communications of the ACM, 2022
Opening the black box or Pandora's Box?