Presentatie voor het symposium rondom het kennislab mobiliteitstransitie.
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The user experience of our daily interactions is increasingly shaped with the aid of AI, mostly as the output of recommendation engines. However, it is less common to present users with possibilities to navigate or adapt such output. In this paper we argue that adding such algorithmic controls can be a potent strategy to create explainable AI and to aid users in building adequate mental models of the system. We describe our efforts to create a pattern library for algorithmic controls: the algorithmic affordances pattern library. The library can aid in bridging research efforts to explore and evaluate algorithmic controls and emerging practices in commercial applications, therewith scaffolding a more evidence-based adoption of algorithmic controls in industry. A first version of the library suggested four distinct categories of algorithmic controls: feeding the algorithm, tuning algorithmic parameters, activating recommendation contexts, and navigating the recommendation space. In this paper we discuss these and reflect on how each of them could aid explainability. Based on this reflection, we unfold a sketch for a future research agenda. The paper also serves as an open invitation to the XAI community to strengthen our approach with things we missed so far.
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An efficient four-step biotransformation-mediated synthesis of (1S)-1-(2,6-dichloro-3-fluorophenyl)ethanol in enantiomerically pure form is described. This compound is a key intermediate required for the preparation of PF-2341066, a potent inhibitor of c-Met/ALK that is currently in clinical development. The described synthesis was used to manufacture 6 kg of the title compound and can also be employed to produce the corresponding (1R)-enantiomer. © 2010 Elsevier Ltd. All rights reserved.
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