Fifty percent of women with Medicaid who desire postpartum sterilization are unable to receive one, and almost half of these women become pregnant in the subsequent year. Using normative, qualitative, and quantitative observational analyses, Dr. Arora investigates this disparity between women with private versus Medicaid insurance that exists, in part, due to federal policy aimed at reducing coercion and enhancing informed consent. Balancing ethical goals and clinical evidence, she advocates for policy change that is evidence-based and just.
‘Moral distress’ is a term originally coined to refer to the suffering, frustration, and outrage of nurses who found themselves compromising their own integrity under conditions of institutional constraint and duress. It is now recognized as a growing reality for clinicians across clinical disciplines and roles. While the “epidemic” of moral distress poses serious challenges both to clinician well-being and to the quality of clinical care, moral distress is also a call of conscience that signals genuine investment in moral standards and commitments. It thus has great potential, if properly worked with and directed, to motivate and inform moral reform. This talk will explore both key challenges and important forms of positive potential held by moral distress, highlighting the need for responsive environments, in which claims of moral distress are heard and given ‘uptake,’ not as a mere expressions of individual suffering, but as significant and hopeful forms of moral protest.
Can agriculture save the planet before it destroys it?
Jack A. Bobo
Can the Researcher-Participant Relationship Ground Ancillary-Care Obligations?
Henry Richardson, JD, MPP, PhD
Lecture Title TBA
Lisa S. Parker, PhD
Technical co-lead of the Ethical Artificial Intelligence Team
Gebru is the Technical co-lead of the Ethical Artificial Intelligence Team at Google. She was previously a postdoc at Microsoft Research, New York City in the FATE (Fairness Transparency Accountability and Ethics in AI) group, where she studied algorithmic bias and the ethical implications underlying any data mining project (see this New York Times article for an example of her work). She received a PhD from the Stanford Artificial Intelligence Laboratory, studying computer vision under Fei-Fei Li. Her thesis pertained to data mining large scale publicly available images to gain sociological insight, and working on computer vision problems that arise as a result.