Interpretability of deep learning models: A survey of results

Deep neural networks have achieved near-human accuracy levels in various types of classification and prediction tasks including images, text, speech, and video data. However, the networks continue to be treated mostly as black-box function approximators, mapping a given input to a classification output. The next step in this human-machine evolutionary process-incorporating these networks into mission critical processes such as medical diagnosis, planning and control-requires a level of trust association with the machine output. Typically, statistical metrics are used to quantify the uncertainty of an output. However, the notion of trust also depends on the visibility that a human has into the working of the machine. In other words, the neural network should provide human-understandable justifications for its output leading to insights about the inner workings. We call such models as interpretable deep networks. Interpretability is not a monolithic notion. In fact, the subjectivity of an interpretation, due to different levels of human understanding, implies that there must be a multitude of dimensions that together constitute interpretability. In addition, the interpretation itself can be provided either in terms of the low-level network parameters, or in terms of input features used by the model. In this paper, we outline some of the dimensions that are useful for model interpretability, and categorize prior work along those dimensions. In the process, we perform a gap analysis of what needs to be done to improve model interpretability

Prima facie reasons to question enclosed intellectual property regimes and favor open-source regimes for germplasm

In principle, intellectual property protections (IPPs) promote and protect important but costly investment in research and development. However, the empirical reality of IPPs has often gone without critical evaluation, and the potential of alternative approaches to lend equal or greater support for useful innovation is rarely considered. In this paper, we review the mounting evidence that the global intellectual property regime (IPR) for germplasm has been neither necessary nor sufficient to generate socially beneficial improvements in crop plants and maintain agrobiodiversity. Instead, based on our analysis, the dominant global IPR appears to have contributed to consolidation in the seed industry while failing to genuinely engage with the potential of alternatives to support social goods such as food security, adaptability, and resilience. The dominant IPR also constrains collaborative and cumulative plant breeding processes that are built upon the work of countless farmers past and present. Given the likely limits of current IPR, we propose that social goods in agriculture may be better supported by alternative approaches, warranting a rapid move away from the dominant single-dimensional focus on encouraging innovation through ensuring monopoly profits to IPP holders

The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes

Black Girls Speak STEM: Counterstories of Informal and Formal Learning Experiences

This study presents the interpretations and perceptions of Black girls who participated in I AM STEM – a community-based informal science, technology, engineering, and mathematics (STEM) program. Using narrative inquiry, participants generated detailed accounts of their informal and formal STEM learning experiences. Critical race methodology informed this research to portray the dynamic and complex experiences of girls of color, whose stories have historically been silenced and misrepresented. The data sources for this qualitative study included individual interviews, student reflection journals, samples of student work, and researcher memos, which were triangulated to produce six robust counterstories. Excerpts of the counterstories are presented in this article. The major findings of this research revealed that I AM STEM ignited an interest in STEM learning through field trips and direct engagement in scientific phenomena that allowed the girls to become agentic in continuing their engagement in STEM activities throughout the year. This call to awaken the voices of Black girls to speak casts light on their experiences and challenges as STEM learners ⎯ from their perspectives. The findings confirm that when credence and counterspaces are given to Black girls, they are poised to reveal their luster toward STEM learning. This study provided a space for Black girls to reflect on their STEM learning experiences, formulate new understandings, and make connections between the informal and formal learning environments within the context of their everyday lives, thus offering a more holistic approach to STEM learning that occurs across settings and over a lifetime