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1. You Say Brutal, I Say Thursday: Isn't It Obvious?

   https://doi.org/10.51272/pmena.42.2020-175  

   Sword, S; Marshall, A.; Young, M. (May 2021, PME-NA)
2. It is not what you say but how you say it: Evidence from Russian shows robust effects of the structural prior on noisy channel inferences.

   https://doi.org/10.1037/xlm0001244  

   Poliak, Moshe; Ryskin, Rachel; Braginsky, Mika; Gibson, Edward (May 2023, Journal of Experimental Psychology: Learning, Memory, and Cognition)
3. Poster Session: You say brutal, I say Thursday: Isn't it obvious?

   Sword, S.; Marshall, A.; Young, M. (January 2020, Mathematics Education Across Cultures: Proceedings 42nd PME-NA)

   Sacristán, A.; Cortés-Zavala, J. & (Ed.)

   Programmatic collaborations involving mathematicians and educators in the U.S. have been valuable but complex (e.g., Heaton & Lewis, 2011; Bass, 2005; Bass & Ball, 2014). Sultan & Artzt (2005) offer necessary conditions (p.53) including trust and helpfulness. Articles in Fried & Dreyfus (2014) and Bay-Williams (2012) describe outcomes from similarly collaborative efforts; however, there is a gap in the literature in attending to how race and gender intersect with issues of professional status, culture, and standards of practice. Arbaugh, McGraw and Peterson (2020) contend that “the fields of mathematics education and mathematics need to learn how to learn from each other - to come together to build a whole that is greater than the sum of its parts” (p. 155). Further, they posit that the two must “learn to honor and draw upon expertise related to both similarities and differences” across disciplines, or cultures. We argue that in order to do this, we must also take into account race, gender, language. For example, words like trust or helpfulness can read very differently when viewed from personal and professional culture, gender, or racial lenses. This poster shares personal vignettes from the perspective of three collaborators – one black male mathematician, one white female mathematics educator, and one white woman who was trained as a mathematician but works as a mathematics educator - illustrating some of the complexity of collaboration. 

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4. Hiring is Broken: What Do Developers Say About Technical Interviews?

   https://doi.org/10.1109/VLHCC.2019.8818836  

   Behroozi, Mahnaz; Parnin, Chris; Barik, Titus (October 2019, IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC))

   Technical interviews-a problem-solving form of interview in which candidates write code-are commonplace in the software industry, and are used by several well-known companies including Facebook, Google, and Microsoft. These interviews are intended to objectively assess candidates and determine fit within the company. But what do developers say about them?To understand developer perceptions about technical interviews, we conducted a qualitative study using the online social news website, Hacker News-a venue for software practitioners. Hacker News posters report several concerns and negative perceptions about interviews, including their lack of real-world relevance, bias towards younger developers, and demanding time commitment. Posters report that these interviews cause unnecessary anxiety and frustration, requiring them to learn arbitrary, implicit, and obscure norms. The findings from our study inform inclusive hiring guidelines for technical interviews, such as collaborative problem-solving sessions. 

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5. What Is It You Really Want of Me? Generalized Reward Learning with Biased Beliefs about Domain Dynamics

   https://doi.org/10.1609/aaai.v34i03.5630  

   Gong, Ze; Zhang, Yu (June 2020, Proceedings of the AAAI Conference on Artificial Intelligence)

   Reward learning as a method for inferring human intent and preferences has been studied extensively. Prior approaches make an implicit assumption that the human maintains a correct belief about the robot's domain dynamics. However, this may not always hold since the human's belief may be biased, which can ultimately lead to a misguided estimation of the human's intent and preferences, which is often derived from human feedback on the robot's behaviors. In this paper, we remove this restrictive assumption by considering that the human may have an inaccurate understanding of the robot. We propose a method called Generalized Reward Learning with biased beliefs about domain dynamics (GeReL) to infer both the reward function and human's belief about the robot in a Bayesian setting based on human ratings. Due to the complex forms of the posteriors, we formulate it as a variational inference problem to infer the posteriors of the parameters that govern the reward function and human's belief about the robot simultaneously. We evaluate our method in a simulated domain and with a user study where the user has a bias based on the robot's appearances. The results show that our method can recover the true human preferences while subject to such biased beliefs, in contrast to prior approaches that could have misinterpreted them completely. 

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