Notes from Jan 25 meeting

DG: Akash Asthana (Unlicensed) Namita Singh (Unlicensed) Ashu Sikri (Unlicensed)

SEWA: Nithya and Salonie

A couple highlights relating this to recent DFN convos: 

Salonie and Nitya from Sewa Bharat which is the national level federation for SEWA and it aims to create a support system for cooperatives, bring in innovation, help women collectives setup producer companies. 

For reference, Sewa has 150 member orgs and 1.9mn women members. Its a trade union for informal workers that has been around for 50 years. Many of the SEWA collectives are focused on agricultural production. 

Focus for the CIFAR project is how to support women's cooperatives; where are gaps and entryways where digital tech can play a supportive role in self-governance. Working closely with with two women's agricultural cooperatives 

  1. South gujarat, 1000 farmers (viable)

  2. Ahmedabad, reviving a 200 farmer cooperative that is currently defunct

Work on establishing women's cooperatives in other geographies as well: 

 

Karnbhumi

 

We covered a couple key questions: 

  1. What makes a women's agriculture focused collective successful? 

2. Where does it make sense to federate / centralize vs. decentralize?

One thing I was trying to get at with this question is where does it make sense for data to stay at the Producer group / local level and where does it make sense to aggregate and pool together? 

Centralize: 

Decentralize

Next Steps


Andrew Hicks (Unlicensed) scheduling an intro call with the team for Jan 17

"A data infrastructure platform will enable women agricultural workers to use machine learning algorithms to tap into rich data insights, such as tracking supply and demand for produce, predicting production costs, comparing market models and predicting competitive pricing."

https://cifar.ca/ai/ai-society/cifar-solution-networks/data-communities-for-inclusion/

Over the next three years, CIFAR’s first Solution Network will develop an open-access data infrastructure platform that will help women agricultural workers in India gain fair access to a competitive marketplace.

The international, interdisciplinary Network with members from India, Canada, Finland, Switzerland, and the U.K, will work with grassroots organizations to identify and remove technological barriers faced by women workers, including known barriers such as digital literacy and access to mobile phones. A data infrastructure platform will enable women agricultural workers to use machine learning algorithms to tap into rich data insights, such as tracking supply and demand for produce, predicting production costs, comparing market models and predicting competitive pricing. These insights will promote equitable economic opportunities for women agricultural workers in India.


Intro call on January 19:

DG: alesha (Unlicensed) Ashu Sikri (Unlicensed) Andrew Hicks (Unlicensed)

CIFAR: