835 research outputs found
Give me 3W1H: A Bibliometric View on Accountable AI
Accountability is crucial to make stakeholders of Artificial Intelligence (AI)-based systems justify their actions, thereby explaining the harm such systems cause to AI users. Due to the importance of accountability in the context of AI, accountability was introduced into IS research through literature reviews. Therefore, while IS research’s understanding of accountability covers the necessary depth, it comes at the expense of its essential breadth. Using a bibliometric analysis with 19,978 English-language papers, we shed light on the essential breadth posing three W- and one H-questions (When, What, Whereof, and How). Therefore, we contribute to IS research by highlighting the urgent need to revise existing definitions of accountability in the context of AI and establish them in IS research. We argue that a missing revision leads to non-transferrable findings within IS research. Accordingly, this study serves as a starting point for adapting definitions and creating a shared understanding in IS research
How AI Developers’ Perceived Accountability Shapes Their AI Design Decisions
While designing artificial intelligence (AI)-based systems, AI developers usually have to justify their design decisions and, thus, are accountable for their actions and how they design AI-based systems. Crucial facets of AI (i.e., autonomy, inscrutability, and learning) notably cause potential accountability issues that AI developers must consider in their design decisions, which has received little attention in prior literature. Drawing on self-determination theory and accountability literature, we conducted a scenario-based survey (n=132). We show that AI developers who perceive themselves as accountable tend to design AI-based systems to be less autonomous and inscrutable but more capable of learning when deployed. Our mediation analyses suggest that perceived job autonomy can partially explain these direct effects. Therefore, AI design decisions depend on individual and organizational settings and must be considered from different perspectives. Thus, we contribute to a better understanding of the effects of AI developers’ perceived accountability when designing AI-based systems
A Coupled Stochastic-Deterministic Method for the Numerical Solution of Population Balance Systems
In this thesis, a new algorithm for the numerical solution of population balance systems is proposed and applied within two simulation projects. The regarded systems stem from chemical engineering. In particular, crystallization processes in fluid environment are regarded. The descriptive population balance equations are extensions of the classical Smoluchowski coagulation equation, of which they inherit the numerical difficulties introduced with the coagulation integral, especially in regard of higher dimensional particle models.
The new algorithm brings together two different fields of numerical mathematics and scientific computing, namely a stochastic particle simulation based on a Markov process Monte—Carlo method, and (deterministic) finite element schemes from computational fluid dynamics.
Stochastic particle simulations are approved methods for the solution of population balance equations. Their major advantages are the inclusion of microscopic information into the model while offering convergence against solutions of the macroscopic equation, as well as numerical efficiency and robustness. The embedding of a stochastic method into a deterministic flow simulation offers new possibilities for the solution of coupled population balance systems, especially in regard of the microscopic details of the interaction of particles.
In the thesis, the new simulation method is first applied to a population balance system that models an experimental tube crystallizer which is used for the production of crystalline aspirin. The device is modeled in an axisymmetric two-dimensional fashion. Experimental data is reproduced in moderate computing time. Thereafter, the method is extended to three spatial dimensions and used for the simulation of an experimental, continuously operated fluidized bed crystallizer. This system is fully instationary, the turbulent flow is computed on-the-fly.
All the used methods from the simulation of the Navier—Stokes equations, the simulation of convection-diffusion equations, and of stochastic particle simulation are introduced, motivated and discussed extensively. Coupling phenomena in the regarded population balance systems and the coupling algorithm itself are discussed in great detail. Furthermore, own results about the efficient numerical solution of the Navier—Stokes equations are presented, namely an assessment of fast solvers for discrete saddle point problems, and an own interpretation of the classical domain decompositioning method for the parallelization of the finite element method
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Simulations of an ASA flow crystallizer with a coupled stochastic-deterministic approach
A coupled solver for population balance systems is presented, where the
flow, temperature, and concentration equations are solved with finite element
methods, and the particle size distribution is simulated with a stochastic
simulation algorithm, a so-called kinetic Monte-Carlo method. This novel
approach is applied for the simulation of an axisymmetric model of a tubular
flow crystallizer. The numerical results are compared with experimental data
Simulations of an ASA flow crystallizer with a coupled stochastic-deterministic approach
A coupled solver for population balance systems is presented, where the flow, temperature, and concentration equations are solved with finite element methods, and the particle size distribution is simulated with a stochastic simulation algorithm, a so-called kinetic Monte-Carlo method. This novel approach is applied for the simulation of an axisymmetric model of a tubular flow crystallizer. The numerical results are compared with experimental data
The Role of Process and Outcome Accountability Claims for Shaping AI Developers’ Perceived Accountability
As accountability becomes increasingly important for developers of artificial intelligence (AI)-based systems, governance mechanisms such as AI principles or audits are often criticized for not sufficiently influencing AI developers. Therefore, we examine how visualized arguments in user interfaces (UIs) of integrated development environments (IDEs) can increase AI developers’ perceived accountability. Combining construal level theory and Toulmin’s model of argumentation, four UI design artifacts were developed, each containing a claim of process or outcome accountability with or without monitoring and evaluation tools that act as claim-supporting data. Results of an online experiment with 164 AI developers show that claiming process accountability increases AI developers’ perceived accountability more than claiming outcome accountability, both without supporting data. However, when supporting data are available, both claims increase AI developers’ perceived accountability comparably effectively. The study’s results highlight the theoretical and practical usefulness of visualized arguments in UIs of IDEs to promote AI developers’ accountability
An assessment of solvers for saddle point problems emerging from the incompressible Navier--Stokes equations
Efficient incompressible flow simulations, using inf-sup stable pairs of finite element spaces, require the application of efficient solvers for the arising linear saddle point problems. This paper presents an assessment of different solvers: the sparse direct solver UMFPACK, the flexible GMRES (FGMRES) method with different coupled multigrid preconditioners, and FGMRES with Least Squares Commutator (LSC) preconditioners. The assessment is performed for steady-state and time-dependent flows around cylinders in 2d and 3d. Several pairs of inf-sup stable finite element spaces with second order velocity and first order pressure are used. It turns out that for the steady-state problems often FGMRES with an appropriate multigrid preconditioner was the most efficient method on finer grids. For the time-dependent problems, FGMRES with LSC preconditioners that use an inexact iterative solution of the velocity subproblem worked best for smaller time steps
O2S: Open-source open shuttle
Currently, commercially available intelligent transport robots that are
capable of carrying up to 90kg of load can cost \1500. Furthermore,
O2S offers a simple yet robust framework for contextualizing simultaneous
localization and mapping (SLAM) algorithms, an essential prerequisite for
autonomous robot navigation. The robustness and performance of the O2S were
validated through real-world and simulation experiments. All the design,
construction and software files are freely available online under the GNU GPL
v3 license at https://doi.org/10.17605/OSF.IO/K83X7. A descriptive video of O2S
can be found at https://osf.io/v8tq2
Asian Middle Classes - Drivers of Political Change? Asia Policy Brief 2014/06, November 2014
Asia watchers have been kept exceptionally busy by recent political developments in the region. An unprecedented landslide victory in India’s general elections,
pro-democracy protests in Hong Kong, close elections in Indonesia, a coup in Thailand – the list goes on. As unrelated as these events appear, analysts may find a missing link among a social group that is currently exploding in numbers: Asia’s middle classes. Often discussed simply in terms of its economic potential,
Asia’s middle-income population is also flexing its political muscle. A closer look at its influence throughout the region in recent months seems to confirm for the field of politics what economists have known for some time: The rise of the Asian middle classes constitutes one of the most fundamental transformations
of our time. The consequences remain to be seen
ACCOUNTABILITY INCONGRUENCE AND ITS EFFECTS ON AI DEVELOPERS’ JOB SATISFACTION
Developers of Artificial Intelligence (AI)-based systems are increasingly urged to assume accountability for their development decisions, referring to the degree to which they must justify underlying algorithms and their outcomes on demand. Thereby, AI developers often have to juxtapose how much accountability they self-attribute to them and how much accountability they perceive others attribute to them, creating intrapersonal perceptual accountability (in)congruence with unknown consequences for their job satisfaction. Building on perceptual congruence research and algorithmic accountability literature, we conducted an online survey of 87 AI developers about their experiences in AI-based systems development projects. Our results show that the lower the incongruence between self-attributed and others-attributed accountability, the higher the job satisfaction of AI developers. Moreover, we find that AI developers’ role ambiguity mediates this effect. Our study contributes to a more nuanced understanding of AI developers’ perceived accountability, with essential insights for defining job roles and understanding AI developers
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