Why Community and Collaboration is the Key for Building Ethical AI

A new way of building AI solutions through forming communities of organizations, experts, and enthusiasts that collaborate to learn and build trusted and socially-beneficial AI solutions.

Rudradeb Mitra
Mar 27 · 7 min read

The problem with AI ethics

Photo by Pablo García Saldaña on Unsplash

AI Ethics is a complicated and broad field and has been one of the most debated topics in the AI space for a long while.

While many initiatives have been formed (such as OpenAI or a recent joint program by Harvard and MIT) many more problems remain to be solved.

Building ethical AI solutions means to answer the “hard questions” — Is it moral? Is it safe? What value does it bring?

And only by answering these questions properly, socially-beneficial AI can be realized.

A recent study by the Nuffield Foundation explored the topic of AI ethics deeper and concluded that despite a shared set of concepts and concerns is evolving, some main gaps remain to be filled; most significantly a lack of clarity or consensus around the meaning of central ethical concepts as well as insufficient attention towards tensions between ideals and values.

The question is: How can we get closer to solving these problems?

A community as a mean to unite people and values

There is no power for change greater than a community that discovers what it cares about.

Margaret J. Wheatley

A community is formed first and foremost because its members share common values, interests, and goals.

With regards to AI that means to bring together the right people that associate with a problem and are willing to solve it together. Considering the tremendous impact that most of today’s AI solutions have on people and society, it would be detrimental to built solutions in isolation from the people and social circumstances that make them necessary in the first place.

Therefore, we need to stop focusing on individuals or teams but start shifting our attention towards communities of people solving a problem.

50 students, two experts, and a common mission

In the last six months, I have been working with a project community of more than 50 students and AI enthusiasts. All members shared the following mission:

Through collaborative work and shared learning, we will reach our goals faster while boosting our knowledge and building a solution that creates a positive impact in the Clean Tech Sector.

Using Machine Learning for Low-Resolution Satellite Images

The technical goal was to build a sophisticated Machine Learning model to increase the adoption of rooftop solar panels in India. The project’s results are up until now from utmost quality (find details here and here).

The students are coming from all over India, have never met each other but are united through the power of community and collaboration.

Some of the students — From the top — Jitendra, Abhigyan, Raghav, Devendra, Rasika, Iresh, Jerin Paul, and Shivani.

Introducing Collaborative Artificial Intelligence

Collaborative AI means to merge the concepts of community and collaboration by involving organizations, experts, and students to build solutions that are ethical, trusted, and value-creating; and as a result beneficial for society.

Any real-world problem could be best solved if a group of people comes together to put in their dedicated efforts. When it comes to the collective efforts of dedicated individuals, success is bound to occur!

Iresh Mishra, a 4th-year student of Shri Mata Vaishno Devi University, India.

1. Empowering students to acquire real-world AI skills

Collaborative AI means to provide students with the opportunity to work on real-world projects. With today’s technological advancements, online courses, and available tools, talent is everywhere and can be accessed easily.

In the Solar Machine Learning project, tasks are announced in the community and students take up those tasks according to their skill-set.

The advantages for students:

  1. Work on real-world data
  2. Improved communication skills through frequent demos of their work
  3. Steeper learning curve through shared learning
  4. Mentorship by leading AI experts

What students have to say:

Abhigyan Das, who is helping to gather the data says “I think such a community model should be followed by more organizations because we as students can gain not only first-hand experience about work but can also learn a lot of things which are not available in any course.

I feel working like this meets both the ends, of the working community to get the best of enthusiastic people and of the members who are just beginning to contribute in this field, to add to their learning curve by being able to work towards delivering their own product, have an understanding of the impact of their little ideas. This is only possible when there is mutual trust and respect for each other. ”, adds Smriti Bahugana.

In addition, in the last 18 months, I had the opportunity to speak at over 70 events in 23 countries and in every place, I met awesome and talented people — students, working professionals, and entrepreneurs. I can unequivocally say that talent is not limited to certain areas but is available globally — be it in a small town like Novi Sad, Serbia or Odessa, Ukraine or in big cities like Ho Chi Minh, Vietnam or somewhere else on our diverse planet.

2. Enabling organizations to build trusted and value-creating products

For organizations, Collaborative AI means to harness crowd wisdom, diversity, and inclusion united in one project community.

How organizations benefit:

  1. More trust
  2. Faster results
  3. Access to leading AI experts and domain knowledge
  4. More and better data

Especially the trust generated by community-driven development can significantly help to make people more willing to share their data. Something which is receding in products built by large corporations.

3. Getting access to data

Having access to a larger amount of high-quality data through forming project communities stems from two aspects.

First, leveraging crowd wisdom by having more people involved results in innovative approaches to gather and work with data.

Additionally, a large project community compared to a small team of people generates and prepares high-quality data faster and more efficiently.

One of the students, Rasika Joshi, says “I could focus more on building Neural Network and do training over required formatted data set just because I was working with fellow students and they provided me with the data in a given time frame.”

4. Democratizing AI through the power of global talent

There are hundreds of thousands of Junior Machine Learning engineers and data scientists, who find it extremely hard to work on real-world projects. By connecting organizations and impactful problems with the right people, we have the power to make this technology accessible to a broader audience and all contribute to the democratization of AI.

Building an Equal Opportunity World

AI has the potential to become one of the greatest technologies of today’s and tomorrow’s time, and it is in our hands to make this a reality.

“Imagine a world where no matter where you are born or live if you are talented you get equal access to work and opportunities as anyone else living in any other part of the world.” — The future of Work: Decentralized, Transparent, and Trust Based[1]

This is our vision at Omdena of how the future of work and education should be and we are willing to contribute our part.

We believe community and collaboration are two of the main ingredients to realizing this future and we welcome organizations, experts, and enthusiasts to join our community and collaborate to learn and build high-impact and trustworthy solutions.

If you want to receive updates on our AI Challenges, get expert interviews, and practical tips to boost your AI skills, subscribe to our monthly newsletter.

We are also on Facebook, LinkedIn, and Twitter.

About the authors

Author: Rudradeb Mitra is an AI expert with more than 10 years of experience. He enjoys writing, dancing, and building products for human adoption.

Co-Author: Michael Burkhardt’s background is in Innovation Management. He is a Global Citizen (40+ countries) and writes about AI ethics and meditation for entrepreneurs.


[1] The future of Work: Decentralized, Transparent, and Trust-Based, Rudradeb Mitra, April 20, 2018, https://www.linkedin.com/pulse/future-work-decentralized-transparent-trust-based-rudradeb-mitra/


A global platform where AI engineers and enthusiasts from diverse backgrounds collaborate with mission-driven organizations to solve grand social problems.

Rudradeb Mitra

Written by

Building AI for Good through Global Collaboration | Author | 7 startups | 10 yrs in AI, 16 years in Products Dev | Invited Speaker 27 countries



A global platform where AI engineers and enthusiasts from diverse backgrounds collaborate with mission-driven organizations to solve grand social problems.

Welcome to a place where words matter. On Medium, smart voices and original ideas take center stage - with no ads in sight. Watch
Follow all the topics you care about, and we’ll deliver the best stories for you to your homepage and inbox. Explore
Get unlimited access to the best stories on Medium — and support writers while you’re at it. Just $5/month. Upgrade