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  • E-mailMarija.Slavkovik@uib.no
  • Phone+47 55 58 23 77
  • Visitor Address
    Fosswinckelsgt. 6
    Room 
    613
  • Postal Address
    Postboks 7802
    5020 Bergen

Marija Slavkovik is an associate professor at the University of Bergen in Norway. Her area of research is Artificial Intelligence (AI) with expertese in collective reasoning. Slavkovik is active in the AI subdisciplines of: multi-agent systems, machine ethics and computational social choice.

Slavkovik believes that the world can be improved by automating away the borring, repetitive and dangerous human tasks and that AI has a crutial role to play towards this goal. In AI, the big problem she hopes to solve is the efficient self-coordination of systems of artificial intelligent agents.

In machine ethics, Slavkovik is active in engineering machine ethics problems - How can we build autonomous systems and artificial agents that behave ethically? Want to know what is happening in machine ethics since it stopped being an SF-only topic? There is a tutorial for that. Slavkovik co-organised a Dagstuhl Seminar in 2019 on this topic. She is also one of the guest editors of the Special Issue on Ethics for Autonomous Systems of the AI Journal.  

Slavkovik is  the vice-chair of the Norwegian Artificial Intelligence Society and member of the informal advisory group on Ethical, Legal, Social Issues  of CLAIRE. She is in the education committee of NORA curently working on developing a national phd course on AI ethics. 

In computational social choice and multi-agent syste, Slavkovik is particularly active  in Judgment Aggregation. If you are wondering what this is there is a tutorial for that. Her new passion in this field is looking for ways to consider  social network interaction of  agents and what impact that can have on collective reasoning and decision-making, particularly in aggregation. For more on what social network analysis has to do with AI go here. 

Slavkovik was the chair and host of the 16th European Conference on Multi-Agent Systems EUMAS held December 6-7, 2018 in Bergen. Here are the proceedings.  She is also in the board of EURAMAS.

 

Marija is an active  speaker on issues of AI and Ethics. Links to some given talks, articles and interviews. 

Video & audio

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Doctoral students (main superviser)

Past students

  • Flavio Tisi (co-supervision with Sonja Smets). 
  • Einar Søreide Johansen 
  • Hanna Kubacka (co-supervision with Jan-Joachim Rückmann). Related publication: Predicting the winners of Borda, Kemeny and Dodgson elections with supervised machine learning  [pdf]

Courses:

  • Spring 2021 INFO383 Research topics in AI ethics. 
  • Automn 2020 INFO282 Knowledge representation and reasoning.
  • Spring 2020 INFO381 Research Topics in AI. The topic of the course is AI Ethics.  Detailed program.
  • Autumn 2019 INFO283 Basic Algorithms in Artificial Intelligence. 
  • Spring 2019 INFO284 Machine Learning.
  • Spring 2017 INFO381 Research Topics in AI. The topic of the course is Machine Ethics. Detailed program.
  • Autumn 2016, 2017, 2018 INFO125 Data Management. 

Office hours are by appointment. 

 

For the freshest list of publications visit Marija's home page, and to see how other people use Marija's publications visit her Google Scholar profile page.

 

Academic article
  • Show author(s) 2020. The Complexity Landscape of Outcome Determination in Judgment Aggregation. . The journal of artificial intelligence research. 687-731.
  • Show author(s) 2019. Improving Judgment Reliability in Social Networks via Jury Theorems. Lecture Notes in Computer Science (LNCS). 230-243.
  • Show author(s) 2019. Autonomous yet moral machines. CEUR Workshop Proceedings.
  • Show author(s) 2019. Aggregating Probabilistic Judgments. Electronic Proceedings in Theoretical Computer Science. 273-292.
  • Show author(s) 2018. Classifying the autonomy and morality of artificial agents. CEUR Workshop Proceedings. 67-83.
  • Show author(s) 2018. Aggregation of probabilisitic logically related judgments. NIKT: Norsk IKT-konferanse for forskning og utdanning.
  • Show author(s) 2017. `How did they know?' Model-checking for analysis of information leakage in social networks. Lecture Notes in Computer Science (LNCS). 42-59.
  • Show author(s) 2017. The Norwegian Oil Fund Investment Decider N.O.F.I.D. NOKOBIT - Norsk konferanse for organisasjoners bruk av informasjonsteknologi.
  • Show author(s) 2017. Implementing Asimov’s First Law of Robotics. NIKT: Norsk IKT-konferanse for forskning og utdanning.
  • Show author(s) 2017. A partial taxonomy of judgment aggregation rules and their properties. Social Choice and Welfare. 327-356.
  • Show author(s) 2017. A modified Vickrey auction with regret minimization for uniform alliance decisions. Studies in Computational Intelligence. 61-72.
  • Show author(s) 2016. Iterative judgment aggregation. Frontiers in Artificial Intelligence and Applications. 1528-1536.
  • Show author(s) 2016. Formal verification of ethical choices in autonomous systems. Robotics and Autonomous Systems. 1-14.
  • Show author(s) 2016. Agenda Separability in Judgment Aggregation. Proceedings of the AAAI Conference on Artificial Intelligence. 1016-1022.
  • Show author(s) 2015. An abstract formal basis for digital crowds. Distributed and parallel databases. 3-31.
  • Show author(s) 2014. Not all judgment aggregation should be neutral. CEUR Workshop Proceedings. 198-211.
  • Show author(s) 2014. Measuring Dissimilarity between Judgment Sets. Lecture Notes in Computer Science (LNCS). 609-617.
  • Show author(s) 2014. How Hard is it to compute majority-preserving judgment aggregation rules? Frontiers in Artificial Intelligence and Applications. 501-506.
  • Show author(s) 2014. Ethical Choice in Unforeseen Circumstances. Lecture Notes in Computer Science (LNCS). 433-445.
  • Show author(s) 2014. A weakening of independence in judgment aggregation: agenda separability. Frontiers in Artificial Intelligence and Applications. 1055-1056.
Report
  • Show author(s) 2016. Engineering Moral Agents - from Human Morality to Artificial Morality (Dagstuhl Seminar 16222). 5. 5. .
Lecture
  • Show author(s) 2014. A tutorial in judgment aggregation.
Academic lecture
  • Show author(s) 2019. What we talk about when we talk about AI and Ethics.
  • Show author(s) 2019. Building Jiminy Cricket: An Architecture for Moral Agreements Among Stakeholders.
  • Show author(s) 2019. April 22 – 26 , 2019, Dagstuhl Seminar 19171 Ethics and Trust: Principles, Verification and Validation.
  • Show author(s) 2019. Answer Set Programming for Judgment Aggregation.
  • Show author(s) 2017. Who's a good robot.
  • Show author(s) 2017. Towards moral autonomous systems.
  • Show author(s) 2017. Engineering Machine Ethics.
Editorial
  • Show author(s) 2014. JA4AI - Judgment Aggregation for Artificial Intelligence (Dagstuhl Seminar 14202). Dagstuhl Reports. 27-39.
Academic anthology/Conference proceedings
  • Show author(s) 2019. Multi-Agent Systems - 16th European Conference, EUMAS 2018, Bergen, Norway, December 6-7, 2018, Revised Selected Papers. Lecture Notes in Computer Science 11450. Springer Nature.
Popular scientific article
  • Show author(s) 2017. Condorcet's jury theorem and the truth on the web. Vox publica.
Feature article
  • Show author(s) 2018. Machines That Know Right And Cannot Do Wrong: The Theory and Practice of Machine Ethics. The IEEE Intelligent Informatics Bulletin. 8-11.
  • Show author(s) 2016. Dagstuhl Manifesto - Engineering Moral Machines. Informatik-Spektrum.
Academic chapter/article/Conference paper
  • Show author(s) 2020. Teaching AI Ethics: Observations and Challenges.
  • Show author(s) 2020. Predicting the Winners of Borda, Kemeny and Dodgson Elections with Supervised Machine Learning. 19 pages.
  • Show author(s) 2020. Model-Checking Information Diffusion in Social Networks with PRISM. 18 pages.
  • Show author(s) 2020. Circumvention by design - dark patterns in cookie consent for online news outlets. 1 pages.
  • Show author(s) 2020. Bias mitigation with AIF360: A comparative study.
  • Show author(s) 2020. Addressing the ethical principles of the Norwegian National Strategy for AI in a kindergarten allocation system.
  • Show author(s) 2019. The Complexity of Elections with Rational Actors. 3 pages.
  • Show author(s) 2019. Answer Set Programming for Judgment Aggregation. 7 pages.
  • Show author(s) 2018. On the Distinction between Implicit and Explicit Ethical Agency. 7 pages.
  • Show author(s) 2017. Formal Models of Conflicting Social Influence. 17 pages.
  • Show author(s) 2017. Complexity Results for Aggregating Judgments using Scoring or Distance-Based Procedures. 10 pages.
  • Show author(s) 2014. A Judgment Set Similarity Measure Based on Prime Implicants. 2 pages.
  • Show author(s) 2013. Some complexity results for distance-based judgment aggregation. 13 pages.
  • Show author(s) 2013. Judgment Aggregation Rules and Voting Rules. 14 pages.

More information in national current research information system (CRIStin)

Ongoing: 

  • MediaFutures: Research Centre for Responsible Media Technology & Innovation. Role: co-leader of WP2 – User Modeling, Personalisation & Engagement.

  • Better Video workows via Real - Time Collaboration and AI - Techniques in TV and New Media. Funded by The Research Council of Norway. Type of project: User-driven Research based Innovation (BIA). Grant: NOK 8.4 million. Role: main supervisor of one of the two doctoral students hired on this project.
  • The Machine Ethics Challenge to Artificial Intelligence and Society. Funded by the Strategic Programme for International Research Collaboration of the Univeristy of Bergen. Grant: NOK 75.000. Role: PI. The grant will support the establishment of a highly interdisciplinary international network of collaborators on the topic of machine ethics.

 

PAST

Marija Slavkovik is the project manager of a SAMKUL grant whose goal is to prepare funding proposals to explore the machine ethics issues in modern journalism. The first meeting of the network is held in Bergen, November 29-30. Results here.

Through the support of SPIRE from the Faculty of Social Sciences at the University of BergenMarija Slavkovik  was working to  establish an  international research network  that engages in developing the interdisciplinary research area of logic-based methods for  social network analysis in artificial intelligence (AI). Results here.

Research groups

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