@misc{RyoJeschkeRilligetal.2020,
  author    = {Ryo, Masahiro and Jeschke, Jonathan M. and Rillig, Matthias C. and Heger, Tina},
  title     = {Machine learning with the hierarchy-of-hypotheses (HoH) approach discovers novel pattern in studies on biological invasions},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1171},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-51764},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-517643},
  pages     = {66 -- 73},
  year      = {2020},
  abstract  = {Research synthesis on simple yet general hypotheses and ideas is challenging in scientific disciplines studying highly context-dependent systems such as medical, social, and biological sciences. This study shows that machine learning, equation-free statistical modeling of artificial intelligence, is a promising synthesis tool for discovering novel patterns and the source of controversy in a general hypothesis. We apply a decision tree algorithm, assuming that evidence from various contexts can be adequately integrated in a hierarchically nested structure. As a case study, we analyzed 163 articles that studied a prominent hypothesis in invasion biology, the enemy release hypothesis. We explored if any of the nine attributes that classify each study can differentiate conclusions as classification problem. Results corroborated that machine learning can be useful for research synthesis, as the algorithm could detect patterns that had been already focused in previous narrative reviews. Compared with the previous synthesis study that assessed the same evidence collection based on experts' judgement, the algorithm has newly proposed that the studies focusing on Asian regions mostly supported the hypothesis, suggesting that more detailed investigations in these regions can enhance our understanding of the hypothesis. We suggest that machine learning algorithms can be a promising synthesis tool especially where studies (a) reformulate a general hypothesis from different perspectives, (b) use different methods or variables, or (c) report insufficient information for conducting meta-analyses.},
  language  = {en}
}
@misc{EbersHochRosenkranzetal.2021,
  author    = {Ebers, Martin and Hoch, Veronica R. S. and Rosenkranz, Frank and Ruschemeier, Hannah and Steinr{\"o}tter, Bj{\"o}rn},
  title     = {The European Commission's proposal for an Artificial Intelligence Act},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Rechtswissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Rechtswissenschaftliche Reihe},
  number    = {8},
  doi       = {10.25932/publishup-59682},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-596824},
  pages     = {16},
  year      = {2021},
  abstract  = {On 21 April 2021, the European Commission presented its long-awaited proposal for a Regulation "laying down harmonized rules on Artificial Intelligence", the so-called "Artificial Intelligence Act" (AIA). This article takes a critical look at the proposed regulation. After an introduction (1), the paper analyzes the unclear preemptive effect of the AIA and EU competences (2), the scope of application (3), the prohibited uses of Artificial Intelligence (AI) (4), the provisions on high-risk AI systems (5), the obligations of providers and users (6), the requirements for AI systems with limited risks (7), the enforcement system (8), the relationship of the AIA with the existing legal framework (9), and the regulatory gaps (10). The last section draws some final conclusions (11).},
  language  = {en}
}