[Commlist] Post Doc Researcher with the Fairness, Accountability, Transparency, and Ethics (FATE) research group at Microsoft Research

The Fairness, Accountability, Transparency, and Ethics (FATE) research 
group at Microsoft Research New York City is looking for a Post Doc 
Researcher to start July 2024: 
https://jobs.careers.microsoft.com/global/en/job/1667778/Post-Doc-Researcher%E2%80%93-FATE-%E2%80%93-Microsoft-Research 
<https://jobs.careers.microsoft.com/global/en/job/1667778/Post-Doc-Researcher%E2%80%93-FATE-%E2%80%93-Microsoft-Research> We 
will begin to review applications for the position on January 3, 2024.

This two-year position is an ideal opportunity for an emerging scholar 
whose work focuses on the social implications of machine learning and AI.

As a Postdoctoral Researcher, you will define your own research agenda, 
driving forward an effective program of basic, fundamental, and applied 
research. You will also have the opportunity to collaborate with members 
of the research group, including Solon Barocas, Alexandra Chouldechova, 
Kate Crawford, Hal Daumé, Miro Dudík, Hanna Wallach, and Jennifer 
Wortman Vaughan, as well as others in the New York City lab and other 
Microsoft Research labs.

Microsoft Research offers an exhilarating and supportive environment for 
cutting-edge, multidisciplinary research, both theoretical and applied, 
with access to an extraordinary diversity of data sources, an open 
publications policy, and close links to top academic institutions around 
the world. Additionally, the position offers unique opportunities to 
engage with the broader responsible AI (RAI) ecosystem within Microsoft, 
including product teams, AI policy teams, and RAI practitioners.

We seek applicants with a demonstrated interest in FATE-related topics 
and a desire to work in a highly interdisciplinary environment that 
includes researchers from computer science, statistics, the social 
sciences, the humanities, and other fields. Successful candidates will 
also have an established research track record, evidenced by notable 
journal or conference publications and broader contributions to the 
research community.

We will consider candidates with a background in a technical field such 
as computer science (especially AI, machine learning, NLP, and computer 
vision), statistics, economics, and decision sciences as well as 
candidates with a socio-technical orientation, such as those in 
information science, sociology, anthropology, science and technology 
studies, media studies, law, and related fields.

We are especially interested in candidates who would like to pursue 
research aligned with one or more of the following themes:

• Computational, statistical, and sociotechnical approaches to fairness 
assessment: Data collection, experimental design, sample-efficient 
statistical methods, measurement, and visualization for fairness 
assessment; mixed-methods approaches, including participatory methods, 
for measuring fairness-related harms caused by AI and human-AI systems.

• Human-centered AI transparency: Explanation, evaluation, and 
uncertainty communication approaches to improve stakeholder 
understanding of AI models or systems; transparency approaches for 
improving human control, autonomy, oversight, and mitigation of AI 
harms; transparency in human-AI collaboration.

• Institutional, organizational, and economic challenges of AI 
development, deployment, and use: Challenges of translating real-world 
problems into machine learning tasks and integrating AI with existing 
institutional processes; incentives for and resistance to contributing 
training data; impacts of generative AI on the cultural industries; 
environmental impacts of generative AI systems.

• AI law and policy; AI for policymaking and regulation: How existing 
laws and policies apply to AI, and where new regulations might be 
necessary; AI as a tool for effective policymaking, regulation, and 
enforcement.

• Responsible AI in practice: Turning RAI principles into policies and 
practices; translating RAI research into practice; navigating 
organizational dynamics, competing incentives, and decision-making under 
uncertainty.

Candidates must have completed their PhD, including submission of their 
dissertation, prior to the start of the position (i.e., dissertation 
submitted and degree preferably conferred by July 2024). We encourage 
candidates with tenure-track job offers from other institutions to 
apply, provided they are able to defer their start date by at least one 
year to accept our position.

To be assured of full consideration, all application materials, 
including reference letters, need to be received by January 3, 2024. 
Applications received after that date may be considered until the 
position is filled.

This role is not to exceed two years.

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