#  AI and Morality 

 



    ![shira zilberstein](/sites/g/files/omnuum1481/files/styles/hwp_5_4__480x385/public/sociology/files/zilberstein.jpeg?itok=Qw8ekW34) 

 



 

####  calendar\_today Date and Time 

 **September 30, 2025** 

 12:00PM - 01:30PM EDT 

####  pin\_drop Location 

  

 [33 Kirkland Street  
4th floor  
Cambridge, MA 02138  
United States



 ](<https://www.google.com/maps?q=US MA Cambridge 02138 33 Kirkland Street 4th floor>) 



 

 [ Zoom link arrow\_circle\_right ](https://urldefense.proofpoint.com/v2/url?u=https-3A__harvard.zoom.us_j_96372272419-3Fpwd-3DT3LzabIAnHO8rhOIPhPerh2buZlymQ.1&d=DwMFaQ&c=WO-RGvefibhHBZq3fL85hQ&r=ucLwQxgL7PLTLJ-rzsKeXzy6ba8DpcocKLbnEdjDx_U&m=xj5o1v15ysj1htTEb016lf--xpRQ6JSUsUKZnPaaf5IO2gNWTgi63qU5_KXuNrl_&s=I5G06KCpmL-TxL_TATYgOQzdCCgkcOfp5CmtCI-gfdM&e=) 

 



 

[Culture and Social Analysis Workshop ](https://cultureworkshop.sociology.fas.harvard.edu/) presentation by

Shira Zilberstein (PhD Candidate at Harvard Department of Sociology)

THIS EVENT IS REMOTE ONLY - ZOOM LINK BELOW

**ABSTRACT**

**The Making of Ethical Ai: Developing Machine Learning Solutions for Healthcare**

Artificial intelligence (AI) is often framed as a transformative solution to society’s most pressing challenges, especially in healthcare. This project analyzes how AI maintains legitimacy as a healthcare solution despite persistent uncertainty, limited effectiveness, and high-profile failures. Drawing on over 16 months of ethnographic fieldwork as well as additional interviews and extensive media and policy analysis, I show how AI practitioners sustain their work by moralizing it as a contribution to the common good. I theorize moralization processes as a mechanism through which scientific and technical projects gain legitimacy by aligning with shared ideals of equity, care, and wellbeing, obscuring limitations while reinforcing professional authority and sustaining investment in uncertain innovation.

As part of this broader project, I present a comparative study of three U.S. research labs developing socially responsive AI for medicine and public health. Despite ambitious aims, the work in each lab is continually shaped by institutional norms, funding pressures, and organizational constraints that redirect projects toward incremental, limited, or stalled outcomes. The case shows how scientific knowledge, organizational forms, and moral commitments are co-produced, as visions of responsible innovation become entangled with the structural demands of applied science. I extend scholarship on AI’s impacts by foregrounding the meso-level dynamics that explain the marketization of science and medicine and reveal how organizational processes shape the possibilities of technoscientific solutions to social problems.

Shira Zilberstein is a PhD candidate in sociology at Harvard University. Her research analyzes the cultural and organizational foundations of knowledge production and technological innovation aimed at addressing social problems. Her dissertation focuses on the development of AI models for healthcare, showing how research communities grapple with the limits of technological solutions while sustaining enduring commitments to AI as a mode of social intervention. In related work, she has studied knowledge production and collective action across digital platforms, legal arenas, urban art scenes, and shifting conceptions of agency.



 

 



 

 

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