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Why are there so few women in science? In Breaking into the Lab, Sue Rosser uses the experiences of successful women scientists and engineers to answer the question of why elite institutions have so few women scientists and engineers tenured on their faculties. Women are highly qualified, motivated students, and yet they have drastically higher rates of attrition, and they are shying away from the fields with the greatest demand for workers and the biggest economic payoffs, such as engineering, computer sciences, and the physical sciences. Rosser shows that these continuing trends are not only disappointing, they are urgent: the U.S. can no longer afford to lose the talents of the women scientists and engineers, because it is quickly losing its lead in science and technology. Ultimately, these biases and barriers may lock women out of the new scientific frontiers of innovation and technology transfer, resulting in loss of useful inventions and products to society.

Sex discrimination in science --- Women scientists --- Science

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Conscious and unconscious bias, societal pressures, and discomfort with women’s ambition are issues that women are confronted with in any male-dominated setting, and tech is no exception. Statistically, women are a disproportionately small percentage of the technology industry. How did we get here, what is changing, and what can future generations of women in STEM expect? In Crushing the IT Gender Bias, author Kellyn Pot’Vin-Gorman applies her two decades of experience in tech to these meaningful questions, plus many more. As a mentor and sponsor of women in the database and development communities, Pot’Vin-Gorman uses experience, visualizations of hard data, and industry interviews to describe the many challenges that women face in STEM. She then shows you how to inoculate against them. Small, positive changes like these are similar to a vaccine: they build individual immunity and thus create herd immunity to protect the most vulnerable. This shift is accomplished through increased representation of—and direct exposure to—successful role models in the industry. You’ll get practical advice related to hiring practices, salary negotiations, and barriers to collaboration. After witnessing multiple female peers depart the tech world, Pot’Vin-Gorman has written Crushing the IT Gender Bias to make her voice heard and to start this necessary conversation productively so that women can thrive. Additionally, this book is for male professionals who desire to grow in their understanding and eliminate bias in their environments. Do not be content with mere survival. Read this book, practice the techniques, and, most importantly, learn how to pay it forward. By arming yourself with knowledge and facing bias head-on, you can be the meaningful change that you want to see in the tech industry.

Women in computer science. --- Women in information science. --- Sex discrimination in science.

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Women in science. --- Sex discrimination against women. --- Sex discrimination against women --- Sex discrimination in science. --- Psychological aspects. --- Science --- Discrimination against women --- Subordination of women --- Women, Discrimination against --- Feminism --- Sex discrimination --- Women's rights --- Male domination (Social structure) --- Minorities in science

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Sex and Gender Bias in Technology and Artificial Intelligence: Biomedicine and Healthcare Applications details the integration of sex and gender as critical factors in innovative technologies (artificial intelligence, digital medicine, natural language processing, robotics) for biomedicine and healthcare applications. By systematically reviewing existing scientific literature, a multidisciplinary group of international experts analyze diverse aspects of the complex relationship between sex and gender, health and technology, providing a perspective overview of the pressing need of an ethically-informed science. The reader is guided through the latest implementations and insights in technological areas of accelerated growth, putting forward the neglected and overlooked aspects of sex and gender in biomedical research and healthcare solutions that leverage artificial intelligence, biosensors, and personalized medicine approaches to predict and prevent disease outcomes. The reader comes away with a critical understanding of this fundamental issue for the sake of better future technologies and more effective clinical approaches.

Sex discrimination in science. --- Sex discrimination in medicine. --- Artificial intelligence --- Sex Factors. --- Sexism --- Biomedical Research --- Research Design --- Data Adjustment --- Data Reporting --- Design, Experimental --- Designs, Experimental --- Error Sources --- Experimental Designs --- Matched Groups --- Methodology, Research --- Problem Formulation --- Research Methodology --- Research Proposal --- Research Strategy --- Research Technics --- Research Techniques --- Scoring Methods --- Experimental Design --- Adjustment, Data --- Adjustments, Data --- Data Adjustments --- Design, Research --- Designs, Research --- Error Source --- Formulation, Problem --- Formulations, Problem --- Group, Matched --- Groups, Matched --- Matched Group --- Method, Scoring --- Methods, Scoring --- Problem Formulations --- Proposal, Research --- Proposals, Research --- Reporting, Data --- Research Designs --- Research Proposals --- Research Strategies --- Research Technic --- Research Technique --- Scoring Method --- Source, Error --- Sources, Error --- Strategies, Research --- Strategy, Research --- Technic, Research --- Technics, Research --- Technique, Research --- Techniques, Research --- Research --- Clinical Trials Data Monitoring Committees --- Experimental Medicine --- Investigational Medicine --- Investigative Medicine --- Research, Biomedical --- Research, Medical --- Medical Research --- Medicine, Experimental --- Medicine, Investigational --- Medicine, Investigative --- Animals, Laboratory --- Gender Issues --- Sex Discrimination --- Gender Bias --- Gender Discrimination --- Sex Bias --- Sexual Discrimination --- Bias, Gender --- Bias, Sex --- Discrimination, Gender --- Discrimination, Sex --- Discrimination, Sexual --- Factor, Sex --- Factors, Sex --- Sex Factor --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Medicine --- Sexism in medicine --- Science --- Moral and ethical aspects. --- methods --- Sexism. --- Sex bias --- Attitude (Psychology) --- Prejudices --- Sex (Psychology) --- Social perception --- Sex role --- Sex factors in disease. --- Biology --- Sex Factors --- Artificial Intelligence --- Technology Assessment, Biomedical --- Research.