lm2020.bib

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@article{Saenz:TSE2020:supplementary,
  author = {Carlos Sáenz-Adán and Beatriz Pérez and Francisco J. García-Izquierdo and Luc Moreau},
  title = {Integrating Provenance Capture and UML with UML2PROV: Principles and Experience (supplementary material)},
  journal = {IEEE Transactions on Software Engineering},
  year = {2020},
  optkey = {},
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  local = {papers/uml2prov-tse2020-supplementary.pdf},
  doi = {10.1109/TSE.2020.2977016},
  url = {https://zenodo.org/record/3701784},
  export = {yes},
  abstract = {In response to the increasing calls for algorithmic accountability, UML2PROV is a novel approach to address the existing gap between application design, where models are described by UML diagrams, and provenance design, where generated provenance is meant to describe an application’s flows of data, processes and responsibility, enabling greater accountability of this application. The originality of UML2PROV is that designers are allowed to follow their preferred software engineering methodology to create the UML Diagrams for their application, while UML2PROV takes the UML diagrams as a starting point to automatically generate: (1) the design of the provenance to be generated (expressed as PROV templates); and (2) the software library for collecting runtime values of interest (encoded as variable-value associations known as bindings), which can be deployed in the application without developer intervention. At runtime, the PROV templates combined with the bindings are used to generate high-quality provenance suitable for subsequent consumption. UML2PROV is rigorously defined by an extensive set of 17 patterns mapping UML diagrams to provenance templates, and is accompanied by a reference implementation based on Model Driven Development techniques. A systematic evaluation of UML2PROV uses quantitative data and qualitative arguments to show the benefits and trade-offs of applying UML2PROV for software engineers seeking to make applications provenance-aware. In particular, as the UML design drives both the design and capture of provenance, we discuss how the levels of detail in UML designs affect aspects such as provenance design generation, application instrumentation, provenance capability maintenance, storage and run-time overhead, and quality of the generated provenance. Some key lessons are learned such as: starting from a non-tailored UML design leads to the capture of more provenance than required to satisfy provenance requirements and therefore, increases the overhead unnecessarily; alternatively, if the UML design is tailored to focus on addressing provenance requirements, only relevant provenance gets to be collected, resulting in lower overheads.}
}

@article{Canal:CLSR20,
  title = {Building Trust in Human-Machine Partnerships},
  abstract = {Artificial intelligence (AI) is bringing radical change to our lives. Fostering trust in this technology requires the technology to be transparent, and one route to transparency is to make the decisions that are reached by AIs explainable to the humans that interact with them. This paper lays out an exploratory approach to developing explainability and trust, describing the specific technologies that we are adopting, the social and organizational context in which we are working, and some of the challenges that we are addressing.},
  author = {Gerard Canal and Rita Borgo and Andrew Coles and Archibald Drake and Dong Huynh and Perry Keller and Senka Krivic and Paul Luff and Quratul-Ain Mahesar and Luc Moreau and Simon Parsons and Menisha Patel and Elizabeth Sklar},
  year = {2020},
  month = nov,
  day = {1},
  doi = {10.1016/j.clsr.2020.105489},
  local = {papers/clsr2020.pdf},
  eprints = {https://kclpure.kcl.ac.uk/portal/en/publications/building-trust-in-humanmachine-partnerships(78b31101-ca91-40ff-b27c-33712d8a141f).html},
  language = {English},
  journal = {Computer Law \& Security Review},
  export = {yes},
  issn = {0267-3649},
  publisher = {Elsevier Limited}
}

@inbook{Moreau-JSONLD:IPAW2020,
  title = {The PROV-JSONLD Serialization: A JSON-LD Representation for the PROV Data Model},
  abstract = {Provenance is information about entities, activities, and people involved in producing a piece of data or thing, which can be used to form assessments about the data or thing's quality, reliability or trustworthiness. PROV-DM is the conceptual data model that forms a basis for the W3C provenance (PROV) family of specifications. In this paper, we propose a new serialization for PROV in JSON called PROV-JSONLD. It provides a light-weight representation of PROV expressions in JSON, which is suitable to be processed by web applications, while maintaining a natural encoding that is familiar with PROV practitioners. In addition, PROV-JSONLD exploits JSON-LD to define a semantic mapping that conforms to the PROV-O specification and, hence, the encoded PROV expressions can be readily processed as Linked Data. Finally, we show that the serialization is also efficiently processable in our evaluation. Overall, PROV-JSONLD is designed to be suitable for interchanging provenance in Web and Linked Data applications, to offer a natural encoding of provenance for its targeted audience, and to allow for fast processing.},
  keywords = {Provenance, PROV, JSON, JSON-LD, Linked Data, Standardisation, Serialization},
  author = {Luc Moreau and Huynh, {Trung Dong}},
  year = {2020},
  editor = {Glavic, Boris and Braganholo, Vanessa and Koop, David},
  booktitle = {Provenance and Annotation of Data and Processes, 8th International Provenance and Annotation Workshop, IPAW 2020},
  year = {2021},
  publisher = {Springer International Publishing},
  pages = {51--67},
  month = may,
  day = {5},
  export = {yes},
  doi = {10.1007/978-3-030-80960-7_4},
  local = {papers/Moreau-prov-jsonld-ipaw2020.pdf},
  eprints = {https://kclpure.kcl.ac.uk/portal/en/publications/the-provjsonld-serialization(c361b79d-b53b-47b1-86e7-6e807eed7305).html}
}

@inbook{Kohan:IPAW2020,
  title = {Incremental Inference of Provenance Types},
  abstract = {Long-running applications nowadays are increasingly instrumented to continuously log provenance. In that context, we observe an emerging need for processing fragments of provenance continuously produced by applications. Thus, there is an increasing requirement for a mode of incremental processing of provenance, while the application is still running, to replace batch processing of a complete provenance dataset available only after the application has completed. A process of particular interest is summarising provenance graphs, which has been proposed as an effective way of extracting key features of provenance and storing them in an efficient manner. To that goal, summarisation makes use of provenance types, which, in loose terms, are an encoding of the neighbourhood of nodes.This paper shows that the process of creating provenance summaries of continuously provided data can benefit from a mode of incremental processing of provenance types. We also introduce the concept of a library of types to reduce the need for storing copies of the same string representations for types multiple times. Further, we show that the computational complexity associated with the task of inferring types is, in most common cases, the best possible: only new nodes have to be processed. We also identify and analyse the exception scenarios. Finally, although our library of types, in theory, can be exponentially large, we present empirical results that show it is in practice quite compact.},
  author = {{Kohan Marzag{\~a}o}, David and Huynh, {Trung Dong} and Luc Moreau},
  year = {2020},
  language = {English},
  booktitle = {8th International Provenance and Annotation Workshop (IPAW2020)},
  export = {yes},
  pages = {145--162},
  doi = {10.1007/978-3-030-80960-7_9},
  local = {papers/Kohan-ipaw2020.pdf},
  eprints = {https://kclpure.kcl.ac.uk/portal/en/publications/incremental-inference-of-provenance-types(f07886e0-595c-46e2-a36f-079b5afb5c6b).html}
}

@techreport{impactscience:2020,
  author = {Lyndsay McAteer},
  title = {Impact Evaluation of PROV. A Provenance Standard Published by the World Wide Web Consortium},
  institution = {Impact Science},
  year = {2020},
  export = {yes},
  optkey = {},
  opttype = {},
  optnumber = {},
  optaddress = {},
  optmonth = jul,
  optnote = {},
  url = {https://www.impact.science/wp-content/uploads/2020/08/Evaluation-of-Impact-of-PROV.pdf},
  optannote = {}
}

@inproceedings{Tsakalakis:WebSci20,
  author = {Tsakalakis, Niko and Carmichael, Laura and Stalla-Bourdillon, Sophie and Moreau, Luc and Huynh, Dong and Helal, Ayah},
  title = {Explanations for AI: Computable or Not?},
  year = {2020},
  isbn = {9781450379946},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  eprints = {https://kclpure.kcl.ac.uk/portal/en/publications/explanations-for-ai-computable-or-not(fb72091c-faf4-4481-a514-2cc269106ad2).html},
  local = {papers/Tsakalakis_WebSci20},
  doi = {10.1145/3394332.3402900},
  abstract = {Automated decision making continues to be used for a variety of purposes within a multitude of sectors. Ultimately, what makes a ‘good’ explanation is a focus not only for the designers and developers of AI systems, but for many disciplines, including law, philosophy, psychology, history, sociology and human-computer interaction. Given that the generation of compliant, valid and effective explanations for AI requires a high-level of critical, interdisciplinary thinking and collaboration, this area is therefore of particular interest for Web Science. The workshop ‘Explanations for AI: Computable or Not?’ (exAI’20) aims to bring together researchers, practitioners and representatives of those subjected to socially-sensitive decision-making to exchange ideas, methods and challenges as part of an interdisciplinary discussion on explanations for AI. It is hoped that this workshop will build a cross-sectoral, multi-disciplinary and international network of people focusing on explanations for AI, and an agenda to drive this work forward. exAI’20 will hold two position paper sessions, where the panel members and workshop attendees will debate the following key issues in an interactive dialogue: The sessions are hoped to stimulate a lively debate on whether explanations for AI are computable or not by providing time for an interactive discussion after each paper. The discussion will uncover key arguments for and against the computability of explanations for AI related to socially-sensitive decision-making. An introductory keynote from the team behind the project PLEAD (Provenance-Driven & Legally Grounded Explanations for Automated Decisions) will present use cases, scenarios and the practical experience of explanations for AI. The keynote will serve as a starting point for the discussions during the paper sessions about the rationale, technologies and/or organisations measures used; and, accounts from different perspectives – e.g. software designers, implementers and those subject to automated decision-making. By the end of this workshop, attendees will have gained a good insight into the critiques and the advantages of explanations for AI, including the extent in which explanations can or should be made computable. They will have the opportunity to participate and inform the discussions on complex topics about AI explainability, such as the legal requirements for explanations, the extent in which data ethics may drive explanations for AI, reflections on the similarities and differences of explanations for AI decisions and manual decisions, as well as what makes a ‘good’ explanation and the etymology of explanations for socially-sensitive decisions. exAI’20 is supported by the Engineering and Physical Sciences Research Council [grant number EP/S027238/1]. We would like to thank the organizers of the Web Science 2019 conference for agreeing to host our workshop and for their support.},
  booktitle = {12th ACM Conference on Web Science Companion},
  pages = {77},
  numpages = {1},
  keywords = {AI, computable explanations, decision making, explainability},
  location = {Southampton, United Kingdom},
  export = {yes},
  series = {WebSci '20}
}