Model Reporting for Certifiable AI: A Proposal from Merging EU Regulation into AI Development

Despite large progress in Explainable and Safe AI, practitioners suffer from a lack of regulation and standards for AI safety. In this work we merge recent regulation efforts by the European Union and first proposals for AI guidelines with recent trends in research: data and model cards. We propose the use of standardized cards to document AI applications throughout the development process. Our main contribution is the introduction of use-case and operation cards, along with updates for data and model cards to cope with regulatory requirements. We reference both recent research as well as the source of the regulation in our cards and provide references to additional support material and toolboxes whenever possible. The goal is to design cards that help practitioners develop safe AI systems throughout the development process, while enabling efficient third-party auditing of AI applications, being easy to understand, and building trust in the system. Our work incorporates insights from interviews with certification experts as well as developers and individuals working with the developed AI applications.Comment: 54 pages, 1 figure, to be submitte

Eleven years’ data of grassland management in Germany

The 150 grassland plots were located in three study regions in Germany, 50 in eachregion. The dataset describes the yearly grassland management for each grassland plotusing 116 variables.General information includes plot identifier, study region and survey year. Additionally,grassland plot characteristics describe the presence and starting year of drainage andwhether arable farming had taken place 25 years before our assessment, i.e. between1981 and 2006. In each year, the size of the management unit is given which, in somecases, changed slightly across years.Mowing, grazing and fertilisation were systematically surveyed:Mowing is characterised by mowing frequency (i.e. number of cuts per year), dates ofcutting and different technical variables, such as type of machine used or usage ofconditioner.For grazing, the livestock species and age (e.g. cattle, horse, sheep), the number ofanimals, stocking density per hectare and total duration of grazing were recorded. As aderived variable, the mean grazing intensity was then calculated by multiplying thelivestock units with the duration of grazing per hectare [LSU days/ha]. Different grazingperiods during a year, partly involving different herds, were summed up to an annualgrazing intensity for each grassland.For fertilisation, information on the type and amount of different types of fertilisers wasrecorded separately for mineral and organic fertilisers, such as solid farmland manure,slurry and mash from a bioethanol factory. Our fertilisation measures neglect dung droppedby livestock during grazing. For each type of fertiliser, we calculated its total nitrogencontent, derived from chemical analyses by the producer or agricultural guidelinesAll three management types, mowing, fertilisation and grazing, were used to calculate acombined land use intensity index (LUI) which is frequently used to define a measure forthe land use intensity. Here, fertilisation is expressed as total nitrogen per hectare [kg N/ha], but does not consider potassium and phosphorus.Information on additional management practices in grasslands was also recorded includinglevelling, to tear-up matted grass covers, rolling, to remove surface irregularities, seedaddition, to close gaps in the sward.New informationInvestigating the relationship between human land use and biodiversity is important tounderstand if and how humans affect it through the way they manage the land and todevelop sustainable land use strategies. Quantifying land use (the ‘X’ in such graphs) canbe difficult as humans manage land using a multitude of actions, all of which may affectbiodiversity, yet most studies use rather simple measures of land use, for example, bycreating land use categories such as conventional vs. organic agriculture. Here, we providedetailed data on grassland management to allow for detailed analyses and thedevelopment of land use theory. The raw data have already been used for > 100 papers onthe effect of management on biodiversity (e.g. Manning et al. 2015)