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The “job talk” is a standard element of faculty recruiting. How audiences treat candidates for faculty positions during job talks could have disparate impact on protected groups, including women. We annotated 156 job talks from five engineering and science departments for 13 categories of questions and comments. All departments were ranked in the top 10 by US News & World Report. We find that differences in the number, nature, and total duration of audience questions and comments are neither material nor statistically significant. For instance, the median difference (by gender) in the duration of questioning ranges from zero to less than two minutes in the five departments. Moreover, in some departments, candidates who were interrupted more often were more likely to be offered a position, challenging the premise that interruptions are necessarily prejudicial. These results are specific to the departments and years covered by the data, but they are broadly consistent with previous research, which found differences comparable in magnitude. However, those studies concluded that the (small) differences were statistically significant. We present evidence that the nominal statistical significance is an artifact of using inappropriate hypothesis tests. We show that it is possible to calibrate those tests to obtain a properP-value using randomization. 

There has been significant recent work on data-driven algorithms for learning general-purpose grasping policies. However, these policies can consis- tently fail to grasp challenging objects which are significantly out of the distribution of objects in the training data or which have very few high quality grasps. Moti- vated by such objects, we propose a novel problem setting, Exploratory Grasping, for efficiently discovering reliable grasps on an unknown polyhedral object via sequential grasping, releasing, and toppling. We formalize Exploratory Grasping as a Markov Decision Process where we assume that the robot can (1) distinguish stable poses of a polyhedral object of unknown geometry, (2) generate grasp can- didates on these poses and execute them, (3) determine whether each grasp is successful, and (4) release the object into a random new pose after a grasp success or topple the object after a grasp failure. We study the theoretical complexity of Exploratory Grasping in the context of reinforcement learning and present an efficient bandit-style algorithm, Bandits for Online Rapid Grasp Exploration Strategy (BORGES), which leverages the structure of the problem to efficiently discover high performing grasps for each object stable pose. BORGES can be used to complement any general-purpose grasping algorithm with any grasp modality (parallel-jaw, suction, multi-fingered, etc) to learn policies for objects in which they exhibit persistent failures. Simulation experiments suggest that BORGES can significantly outperform both general-purpose grasping pipelines and two other online learning algorithms and achieves performance within 5% of the optimal policy within 1000 and 8000 timesteps on average across 46 challenging objects from the Dex-Net adversarial and EGAD! object datasets, respectively. Initial physical experiments suggest that BORGES can improve grasp success rate by 45% over a Dex-Net baseline with just 200 grasp attempts in the real world. See https://tinyurl.com/exp-grasping for supplementary material and videos.