Gretchen Krueger's poignant narrative explores how doctors, families, and the public interpreted the experience of childhood cancer from the 1930s through the 1970s. Pairing the transformation of childhood cancer from killer to curable disease with the personal experiences of young patients and their families, Krueger illuminates the twin realities of hope and suffering. In this social history, each decade follows a family whose experience touches on key themes: possible causes, means and timing of detection, the search for curative treatment, the merit of alternative treatments, the decisions to pursue or halt therapy, the side effects of treatment, death and dying—and cure. Recounting the complex and sometimes contentious interactions among the families of children with cancer, medical researchers, physicians, advocacy organizations, the media, and policy makers, Krueger reveals that personal odyssey and clinical challenge are the simultaneous realities of childhood cancer. This engaging study will be of interest to historians, medical practitioners and researchers, and people whose lives have been altered by cancer.
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With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.