Genetic Discrimination: Does It Exist, and What Are Its Implications?

Does genetic discrimination exist? Thus far, there have been no cases other than Burlington Northern and maybe a couple of other cases which have been filed by plaintiffs in either federal or state court. Notwithstanding all of the statutes, there haven\u27t been a tremendous amount of charges coming in, people coming to the EEOC (Equal Employment Opportunity Commission), or to respective state agencies and even filing charges. This fact confuses me, because I actually believe that genetic discrimination, as we\u27ve been talking about it, is happening more often in the real world than this charge flow would indicate

Genetic Discrimination: Does It Exist, and What Are Its Implications?

Does genetic discrimination exist? Thus far, there have been no cases other than Burlington Northern and maybe a couple of other cases which have been filed by plaintiffs in either federal or state court. Notwithstanding all of the statutes, there haven\u27t been a tremendous amount of charges coming in, people coming to the EEOC (Equal Employment Opportunity Commission), or to respective state agencies and even filing charges. This fact confuses me, because I actually believe that genetic discrimination, as we\u27ve been talking about it, is happening more often in the real world than this charge flow would indicate

Using Sparse Semantic Embeddings Learned from Multimodal Text and Image Data to Model Human Conceptual Knowledge

Distributional models provide a convenient way to model semantics using dense embedding spaces derived from unsupervised learning algorithms. However, the dimensions of dense embedding spaces are not designed to resemble human semantic knowledge. Moreover, embeddings are often built from a single source of information (typically text data), even though neurocognitive research suggests that semantics is deeply linked to both language and perception. In this paper, we combine multimodal information from both text and image-based representations derived from state-of-the-art distributional models to produce sparse, interpretable vectors using Joint Non-Negative Sparse Embedding. Through in-depth analyses comparing these sparse models to human-derived behavioural and neuroimaging data, we demonstrate their ability to predict interpretable linguistic descriptions of human ground-truth semantic knowledge.Comment: Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018), pages 260-270. Brussels, Belgium, October 31 - November 1, 2018. Association for Computational Linguistic

Blindsight: How We See Disabilities in Tort Litigation

Tort litigation operates with a distorted perspective of disability. It suffers from blindsight; it does not see people with disabilities the way they see themselves. Disability advocates emphasize that most people with disabilities lead happy lives. Deeply rooted biases, however, make it difficult for this perspective to be recognized. Tort litigation’s heavy emphasis on medical testimony and its repeated portrayal of plaintiffs as “less than whole” over-emphasize the physical aspects of disability and unfairly depict people with disabilities as tragic. When legal actors embrace these views, they reinforce harmful stereotypes outside the courthouse doors. Newly disabled plaintiffs are also likely to internalize this distorted perspective, as they are repeatedly exposed to it in the course of the litigation. This Article recommends several ways that tort litigation can present plaintiffs with disabilities in more empowering ways, while still recognizing the severity of the injuries involved, and without sacrificing the recovery of hedonic damages or otherwise reducing the plaintiffs’ awards