Guide to Streamlining Series: Making Streamlining Stick

You have decided to streamline your grantmaking process -- congratulations! Your organization could be just beginning to explore ways to make your application and reporting requirements less burdensome to grantees. Or you might have a team deeply engaged in a change process already. This framework illustrates the four basic phases that many grantmakers move through as they streamline and suggests activities and questions that can propel your process forward

Information Outlook, December 2006

Volume 10, Issue 12https://scholarworks.sjsu.edu/sla_io_2006/1011/thumbnail.jp

The Pragmatic Turn in Explainable Artificial Intelligence (XAI)

In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will lack a well-defined goal. Aside from providing a clearer objective for XAI, focusing on understanding also allows us to relax the factivity condition on explanation, which is impossible to fulfill in many machine learning models, and to focus instead on the pragmatic conditions that determine the best fit between a model and the methods and devices deployed to understand it. After an examination of the different types of understanding discussed in the philosophical and psychological literature, I conclude that interpretative or approximation models not only provide the best way to achieve the objectual understanding of a machine learning model, but are also a necessary condition to achieve post hoc interpretability. This conclusion is partly based on the shortcomings of the purely functionalist approach to post hoc interpretability that seems to be predominant in most recent literature

Information Outlook, October 2006

Volume 10, Issue 10https://scholarworks.sjsu.edu/sla_io_2006/1009/thumbnail.jp

Knowledge exchange, matching, and agglomeration

Despite wide recognition of their significant role in explaining sustained growth and economic development, uncompensated knowledge spillovers have not yet been fully modeled with a microeconomic foundation. The main purpose of this paper is to illustrate the exchange of knowledge as well as its consequences on agglomerative activity in a general-equilibrium search-theoretic framework. Agents, possessing differentiated types of knowledge, search for partners to exchange ideas and create new knowledge in order to improve production efficacy. When individuals’ types of knowledge are too diverse, a match is less likely to generate significant innovations. We demonstrate the extent of agglomeration has significant implications for the patterns of information flows in economies. Further, by simultaneously determining the patterns of knowledge exchange and the spatial agglomeration of an economy we identify additional channels for interaction between agglomerative activity and knowledge exchange. Finally, contrary to previous work in spatial agglomeration, our model suggests that agglomerative environments may be either under-specialized and under-populated or over-specialized and over-populated relative to the social optimum.Econometric models