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- Asatiani, Aleksandre, 1987, et al.
(författare)
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Challenges of Explaining the Behavior of Black-Box AI Systems
- 2020
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Ingår i: MIS Quarterly Executive. - : Association for Information Systems. - 1540-1960. ; 19:4, s. 259-278
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Tidskriftsartikel (refereegranskat)abstract
- There are many examples of problems resulting from inscrutable AI systems, so there is a growing need to be able to explain how such systems produce their outputs. Draw- ing on a case study at the Danish Business Authority, we provide a framework and recommendations for addressing the many challenges of explaining the behavior of black-box AI systems. Our findings will enable organizations to successfully develop and deploy AI systems without causing legal or ethical problems.
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| 2. |
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| 3. |
- Asatiani, Aleksandre, 1987, et al.
(författare)
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Sociotechnical Envelopment of Artificial Intelligence: An Approach to Organizational Deployment of Inscrutable Artificial Intelligence Systems
- 2021
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Ingår i: Journal of the Association for Information Systems. - : Association for Information Systems. - 1536-9323 .- 1536-9323. ; 22:2
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Tidskriftsartikel (refereegranskat)abstract
- The paper presents an approach for implementing inscrutable (i.e., nonexplainable) artificial intelligence (AI) such as neural networks in an accountable and safe manner in organizational settings. Drawing on an exploratory case study and the recently proposed concept of envelopment, it describes a case of an organization successfully “enveloping” its AI solutions to balance the performance benefits of flexible AI models with the risks that inscrutable models can entail. The authors present several envelopment methods—establishing clear boundaries within which the AI is to interact with its surroundings, choosing and curating the training data well, and appropriately managing input and output sources—alongside their influence on the choice of AI models within the organization. This work makes two key contributions: It introduces the concept of sociotechnical envelopment by demonstrating the ways in which an organization’s successful AI envelopment depends on the interaction of social and technical factors, thus extending the literature’s focus beyond mere technical issues. Secondly, the empirical examples illustrate how operationalizing a sociotechnical envelopment enables an organization to manage the trade-off between low explainability and high performance presented by inscrutable models. These contributions pave the way for more responsible, accountable AI implementations in organizations, whereby humans can gain better control of even inscrutable machine-learning models.
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| 5. |
- Maaranen, Heikki, et al.
(författare)
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On initial populations of a genetic algorithm for continuous optimization problems
- 2007
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Ingår i: Journal of Global Optimization. - : Springer Science and Business Media LLC. - 0925-5001 .- 1573-2916. ; 37:3, s. 405-436
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Tidskriftsartikel (refereegranskat)abstract
- Genetic algorithms are commonly used metaheuristics for global optimization, but there has been very little research done on the generation of their initial population. In this paper, we look for an answer to the question whether the initial population plays a role in the performance of genetic algorithms and if so, how it should be generated. We show with a simple example that initial populations may have an effect on the best objective function value found for several generations. Traditionally, initial populations are generated using pseudo random numbers, but there are many alternative ways. We study the properties of different point generators using four main criteria: the uniform coverage and the genetic diversity of the points as well as the speed and the usability of the generator. We use the point generators to generate initial populations for a genetic algorithm and study what effects the uniform coverage and the genetic diversity have on the convergence and on the final objective function values. For our tests, we have selected one pseudo and one quasi random sequence generator and two spatial point processes: simple sequential inhibition process and nonaligned systematic sampling. In numerical experiments, we solve a set of 52 continuous test functions from 16 different function families, and analyze and discuss the results.
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