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Convening on Machine Learning Ethics in International Development

By MIT D-Lab | MIT CITE
Wed, Aug 21. 9:00AM - 5:00PM
Ashesi University, Berekuso
past 12 months
Convening on Machine Learning Ethics in International Development

As machine learning becomes ubiquitous in its use, NGOs, research institutions, and governments are applying these methods to address issues in international development in sectors ranging from finance to healthcare. The ethical issues surrounding the use and application of these tools is an area of growing concern in development. Improper implementation of these algorithms can reinforce gender biases, marginalize certain groups of the population, or introduce inequities into existing infrastructure.
This  convening presents challenges in implementing machine learning (ML) solutions and presents a framework for ensuring that ML is used in a fair, unbiased, and ethical way when used in international development. 
Transportation from Accra will be provided.
Talks on Ethical, Responsible Use of Machine Learning
August 21 from 9am to 5pm, dinner and networking session following
Keynote Address by Nyalleng Moorosi, from Google AI and the Council for Scientific and Industrial Research (CSIR).
Additional topics presented by faculty, staff, and researchers from MIT, Ashesi, Boston College, USAID, and others. The topics for these talks include:
Why Fairness in ML is Important 
Challenges in Implementing ML
Case studies of machine learning being used in international development 
Ethical Use Framework to ensure ML is fair, unbiased, and ethical 
Guided workshop (limited capacity - by application only)
August 22 from 9am to 5pm
Participants are asked to bring laptops.
Guided workshop to help individuals and organizations looking at implementing ML in their work address important challenges. The workshop will be run by Mike Teodorescu, Assistant Professor at Boston College, Rich Fletcher, Lead Research Scientist at MIT, and Amit Gandhi, Doctoral Candidate at MIT. The speakers will provide case studies and techniques for implementing fairness into algorithms, and help participants identify potential sources of unfairness and bias in their data.
Space for August 22 workshop is limited and available on a competitive basis. If you or your organization are interested, please fill out this form.

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