Are Students Blind to Their Ethical Blind Spots? An Exploration of Why Ethics Education Should Focus on Self-Perception Biases

2017 ◽  
Vol 41 (4) ◽  
pp. 539-574 ◽  
Author(s):  
Kathleen A. Tomlin ◽  
Matthew L. Metzger ◽  
Jill Bradley-Geist ◽  
Tracy Gonzalez-Padron

Ethics blind spots, which have become a keystone of the emerging behavioral ethics literature, are essentially biases, heuristics, and psychological traps. Though students typically recognize that ethical challenges exist in the world at large, they often fail to see when they are personally prone to ethics blind spots. This creates an obstacle for ethics education—inducing students to act in an ethical manner when faced with real challenges. Grounded in the social psychology literature, we suggest that a meta-bias, the bias blind spot, should be addressed to facilitate student recognition of real-world ethical dilemmas and their own susceptibility to biases. We present a roadmap for an ethics education training module, developed to incorporate both ethics blind spots and self-perception biases. After completing the module, students identified potential ethical challenges in their real-world team projects and reflected on their susceptibility to ethical transgressions. Qualitative student feedback supports the value of this training module beyond traditional ethics education approaches. Lessons for management and ethics educators include (a) the value of timely, in-context ethics interventions and (b) the need for student self-reflection (more so than emphasis on broad ethical principles). Future directions are discussed.

Author(s):  
Ramya Ramakrishnan ◽  
Ece Kamar ◽  
Besmira Nushi ◽  
Debadeepta Dey ◽  
Julie Shah ◽  
...  

Simulators are being increasingly used to train agents before deploying them in real-world environments. While training in simulation provides a cost-effective way to learn, poorly modeled aspects of the simulator can lead to costly mistakes, or blind spots. While humans can help guide an agent towards identifying these error regions, humans themselves have blind spots and noise in execution. We study how learning about blind spots of both can be used to manage hand-off decisions when humans and agents jointly act in the real-world in which neither of them are trained or evaluated fully. The formulation assumes that agent blind spots result from representational limitations in the simulation world, which leads the agent to ignore important features that are relevant for acting in the open world. Our approach for blind spot discovery combines experiences collected in simulation with limited human demonstrations. The first step applies imitation learning to demonstration data to identify important features that the human is using but that the agent is missing. The second step uses noisy labels extracted from action mismatches between the agent and the human across simulation and demonstration data to train blind spot models. We show through experiments on two domains that our approach is able to learn a succinct representation that accurately captures blind spot regions and avoids dangerous errors in the real world through transfer of control between the agent and the human.


2020 ◽  
Vol 67 ◽  
pp. 191-234 ◽  
Author(s):  
Ramya Ramakrishnan ◽  
Ece Kamar ◽  
Debadeepta Dey ◽  
Eric Horvitz ◽  
Julie Shah

Agents trained in simulation may make errors when performing actions in the real world due to mismatches between training and execution environments. These mistakes can be dangerous and difficult for the agent to discover because the agent is unable to predict them a priori. In this work, we propose the use of oracle feedback to learn a predictive model of these blind spots in order to reduce costly errors in real-world applications. We focus on blind spots in reinforcement learning (RL) that occur due to incomplete state representation: when the agent lacks necessary features to represent the true state of the world, and thus cannot distinguish between numerous states. We formalize the problem of discovering blind spots in RL as a noisy supervised learning problem with class imbalance. Our system learns models for predicting blind spots within unseen regions of the state space by combining techniques for label aggregation, calibration, and supervised learning. These models take into consideration noise emerging from different forms of oracle feedback, including demonstrations and corrections. We evaluate our approach across two domains and demonstrate that it achieves higher predictive performance than baseline methods, and also that the learned model can be used to selectively query an oracle at execution time to prevent errors. We also empirically analyze the biases of various feedback types and how these biases influence the discovery of blind spots. Further, we include analyses of our approach that incorporate relaxed initial optimality assumptions. (Interestingly, relaxing the assumptions of an optimal oracle and an optimal simulator policy helped our models to perform better.) We also propose extensions to our method that are intended to improve performance when using corrections and demonstrations data.


2021 ◽  
Author(s):  
Paweł Niszczota ◽  
Magdalena Pawlak ◽  
Michal Bialek

Extant research suggests that processing information in a second language (L2) affects decision making, possibly by affecting metacognition. We hypothesized that processing in L2 will reduce the bias blind spot effect, whereby people (on average) erroneously think that they are less susceptible to biases than others. In Experiment 1, participants assessed their susceptibility and the susceptibility of others to 13 psychological and 7 economic biases, in either L1 (Polish) or L2 (English). In Experiment 2, participants assessed the 7 most severe bias blind spots from Experiment 1. We recruited 500 participants for each experiment via Prolific (832 overall, after exclusions). The main hypothesis and moderators were tested via mixed-model regressions. In Experiment 1, participants showed an overall bias blind spot, which decreased in the L2 condition, but only for psychological biases. In Experiment 2, we replicated the L2-bias blind spot attenuation effect. An exploratory analysis suggests that the effect of L2 is the result of both lower ratings of other-susceptibility and higher ratings of self-susceptibility. Our study provides unique insights on how L2 affects metacognition. We are the first to study how use of L2 can attenuate the bias blind spot. Our findings provide rare support for the psychological distancing (‘birds-eye view’) explanation for the foreign language effect. Bilinguals using L2 showed some resilience to the bias blind spot, suggesting metacognition is language-dependent. Using L2 can be considered as a debiasing technique.


Author(s):  
Kathryn L. Bollich-Ziegler

Despite the strong intuition that people know themselves well, much research in self-perception demonstrates the biases present when evaluating one’s own personality traits. What specifically are these blind spots in self-perceptions? Are self-perceptions always disconnected from reality? And under what circumstances might other people actually be more accurate about the self? The self–other knowledge asymmetry (SOKA) model suggests that because individuals and others differ in their susceptibility to biases or motivations and in the information they have access to, self- and other-knowledge will vary by trait. The present chapter outlines when and why other-perceptions are sometimes more accurate than self-perceptions, as well as when self-reports can be most trusted. Also discussed are next steps in the study of self- and other-knowledge, including practical, methodological, and interdisciplinary considerations and extensions. In sum, this chapter illustrates the importance of taking multiple perspectives in order to accurately understand a person.


2018 ◽  
Vol 36 (6) ◽  
pp. 671-708 ◽  
Author(s):  
Sara Hagá ◽  
Kristina R. Olson ◽  
Leonel Garcia-Marques
Keyword(s):  

2020 ◽  
Vol 1 (1) ◽  
pp. 38-46
Author(s):  
Mesirawati Waruwu ◽  
Yonatan Alex Arifianto ◽  
Aji Suseno

The limitless development of social media, its meaning and function have begun to shift, no longer as a means of establishing relationships, communication, but at the stage of losing the role of ethics and morals, even disputes have occurred triggered by debates from communicating in social media. The purpose of this study is to describe the role of Christian ethics education in relation to the impact of social media development in the era of disruption. Using descriptive qualitative methods with literature literature can find solutions for believers in facing moral decadence due to social media abuse by knowing the era of disruption and ethical challenges from the wrong use of social media can affect moral decadence so that Christian ethics education on a biblical basis can bring modern humans. Believers in particular have become bright in social media and their use in accordance with Christian faith in this era of disruption.


2014 ◽  
Vol 9 (1) ◽  
pp. 12-24
Author(s):  
Michael Comerford

The plethora of new data sources, combined with a growing interest in increased access to previously unpublished data, poses a set of ethical challenges regarding individual privacy. This paper sets out one aspect of those challenges: the need to anonymise data in such a form that protects the privacy of individuals while providing sufficient data utility for data users. This issue is discussed using a case study of Scottish Government’s administrative data, in which disclosure risk is examined and data utility is assessed using a potential ‘real-world’ analysis.


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