Ethical AI Building Blocks: The Interdependence of Emotional & Artificial Intelligence

Ethical AI Building Blocks: The Interdependence of Emotional & Artificial Intelligence

By Jake Carey-Rand


One of my favorite quotes at the moment is from Max Tegmark, MIT professor and author of ‘Life 3.0: Being Human in the Age of Artificial Intelligence’. Tegmark talks about avoiding “this silly, carbon-chauvinism idea that you can only be smart if you’re made of meat” in reference to a more inclusive definition of intelligence to include artificial as well as biological intelligence. I’d like to double down on the requirement for an even more inclusive definition of intelligence – or rather, a more inclusive approach to artificial intelligence (AI). An approach where the emphasis is on diversity and collaboration, for meat lovers, vegans, and robots alike.

Outside the tech biosphere, reservations are often expressed about AI. These moral questions can run even deeper for some of us within the AI sector. Fear that AI will put humans out of a job or learn to wage war against humanity is bounced around the social interwebs at will. But ask a machine learning engineer how the AI she’s been developing actually does what it does, and most often you are met by a bit of a shrug of the shoulders beyond a certain point in the process. The truth is, advanced AI is still a bit of a mystery to us mere humans – even the really smart machine learning humans.

Armed with this context, I won’t argue there aren’t potential downsides. AI is built by people. People decide what data goes into the model. People build models. People train the models and ultimately people decide how to productionize the models and integrate them into a broader workflow or business.

Because all of this is (for the moment) directed by people, it means we have choices. Up to a point – we have a choice about how we create AI, what its tasks are, and ultimately the path we direct it to take. The implications of these choices are crystal clear now more than ever. The power of AI to create a better, healthier and arguably more equitable world is tangible and occurring at a very rapid pace. But so is the dark alternative – people have a choice to create models which spread Fear, Uncertainty, and Doubt to hack an election or to steal money.

AI is a tool like any other… well, almost.


Beyond The Tech

The pursuit of ‘AI nirvana’ is thought by some to be a pipedream cluttered with wasted money and resources along the path to mediocre success. Others share a view that AI at-scale is something reserved only for the FAANG companies (plus Microsoft, Uber, etc.). Without diving into the technicalities of data science and machine learning too deeply, the reality is that organizations are still struggling to capture the value of their data with any corresponding models they build. In fact, 87% of data science projects fail to deliver anything of value in production to the business. Challenges I hear time and again from customers, friends and colleagues include:

  • Competing or out of sync business silos
  • Lack of cohesion around a data strategy
  • Data in various formats and locations
  • Lack of clear objectives within the context of broader business transformation


The Importance of Soft Skills and Collaboration

Critically, some of the most important characteristics of data science success relate to soft skill development – those which make us uniquely human. Yes, we need great programmers, data wranglers, architects, and analysts for everything from data archeology to model training. But it is just as important (I would argue now more important) to curate emotional intelligence if you want to succeed with artificial intelligence. The success of an organization is now judged more heavily based on its ability to build and maintain Cultural Empathy, Critical Thinking, Problem Solving, and Agile Initiatives. Importantly, these skills also lead to a more natural ability to link data science investment directly to organizational (and social) value.

In other words, instilling a culture of diversity, inclusion, and collaboration is integral to AI and ultimately business success. As an organizational psychologist and professor, Tomas Chamorro-Premuzic said in a 2017 Harvard Business Review article, “No matter how diverse the workforce is, and regardless of what type of diversity we examine, diversity will not enhance creativity unless there is a culture of sharing knowledge.” Collaboration is key.


Remove Bias and Enhance Creativity

Out of all the soft skills, the need for an unbiased and collaborative approach to AI is probably the most important thing we can do to more positively impact AI development. Omdena has quickly become the world leader in Collaborative AI, demonstrating rapid success in solving some of the world’s toughest problems. Experts discuss AI bias at length, but remember that humans create AI. We are not perfect and we certainly are not all-knowing. Imagine if all AI were produced by programmers in Silicon Valley. Even they would agree, a model to predict landslides based on drought patterns from satellite imagery in Southeast Asia, would be better done in collaboration with those local to the problem who also understand farming and economics relevant to the region. Likewise, a model built to analyze mortgage default risk based on social sentiment analysis and financial data mining needs to be built by a diverse, collaborative team. As recent history is teaching us, decisions made by the few, expand to elevate systemic division and privilege.

Jack Ma, the world’s wealthiest teacher, said in an address to Hong Kong graduates, ‘Everything we taught our kids over the past 200 years, machines will do better in the future. Educators should teach what machines are not capable of, such as creativity and independent thinking.’

My hope is that schools are adapting to this change, along with all the other changes they must now manage. But for most corporate teams, they have some catching up to do to ensure AI adoption is not only successful but considered a success for all. Let’s start by encouraging a broad, diverse, and collaborative approach to AI. As Tegmark says, “Let’s Build AI that Empowers Us”.


Jake Carey-Rand is a technology executive with nearly 20 years of experience across AI, big data, Internet delivery, and web security. Jake recently joined Omdena as an advisor, to help scale the AI social enterprise.

Omdena is the company “Building Real-World AI Solutions, Collaboratively.” I’ve been watching the impact Omdena and its community of 1,200+ data scientists, from more than 82 countries (we call them Changemakers) have been doing over the last 12 months. Their ability to solve absolutely critical issues around the world has been inspiring. It has also led to some questions about how these Changemakers have been able to do what so many organizations fail to do time and time again – create real-world AI solutions in such a short amount of time. This has inspired us to explore how we could scale this engine of AIForGood even faster. The Omdena platform can be leveraged by enterprises who, especially during these challenging times, have to accelerate, adapt, and transform their approach to “business as usual” through a more collaborative approach to AI.

| Webinar | Can Artificial Intelligence Help to Build A Better Future?

| Webinar | Can Artificial Intelligence Help to Build A Better Future?

A vibrant and insightful discussion on how to define an impactful AI use case (framework below), how AI technology can help during COVID19, why we might be losing our voice during lockdowns, and how to overcome this. Most importantly we discussed how we as citizens and communities can take action to build a better future. Make sure to read the key points below and check out the entire recording at the end of the blog post.


Master Inventor Neil Sahota takes the stage

Our fireside guest Neil Sahota is a sought-after global expert who has worked with Global Fortune 500 companies, world government leaders, and startups around the world. He has been on panels with world-known business leaders like Gary Vaynerchuk and he creates value as a United Nations AI Expert, Master Inventor, and best-selling author.

What constitutes an impactful AI use case?

Focus on solving a problem and creating value, then ROI will follow.

In the first part of the webinar, we addressed the question of what value creation means in the context of AI.

Creating tangible results requires experimentation and breaking with old and conventional thinking habits that are deeply rooted in many organizations. AI is a disruptive technology and with all disruptive technologies, there is an exploration phase and maturity phase. It is the decision of the company to be part of it or miss the accelerating AI train.

Everyone is talking about automation while the real value lies in building innovative solutions where technology is paired with human creativity.


AI Innovation at Omdena

Copyright: Neil Sahota


The magic formula for impactful AI use cases

AI is not the solution to all problems. Building products does not start with thinking about AI but finding a meaningful problem that once solved adds value for the customer or user. Many organizations fall into the trap of adding AI to their strategy without first defining the problem in detail.

According to Neil defining a use case successfully comes with two key ingredients. The right mindset of entrepreneurial thinking, stepping away from “why it won’t work” to “how to make something work”. Next, we need to dive into what are the real problems and root causes to derive a clear problem statement. Only after doing this, we can explore further through knowledge of the space, Rules/regulations, adoption challenges to identify if there is an opportunity or not. Next, the technical implementation starts to build an actual solution. If you are interested, you can find real-world use cases here.

Neil Sahota Omdena


Use cases of AI for COVID19

We addressed the following use cases in the webinar:

  • AI-driven monitoring tools for social distancing
  • Cracking COVID-19 genetic signatures through Machine Learning
  • Looking at the effects of policies for vulnerable populations

What about domestic violence during a lockdown?

One observation during our AI project about how policies and lockdowns affect vulnerable populations was a spike in domestic violence. Below Rudradeb Mitra touches on the example of Singapore. If you are interested in other countries, check out this article.

AI Covid19

Domestic Violence Singapore


Chances for organizations during COVID19

As Omdena Founder Rudradeb Mitra points out the current crisis shows who is affected the most or in other words who is most vulnerable. This can also be applied to customers, users, and even employees where organizations can use AI technology like sentiment analysis to understand which subgroups are most affected by a regulation or policy.


How can we build a better future

Lastly, we discussed how each one of us can make a difference by using his or her voice. There are no experts of the future and the only way to predict the future is to create it. What can each one of us do to create a better future is a question we all need to ask ourselves!

Here is the entire outline of the webinar:

  • Min 3:00: Introduction by Neil Sahota: How to build an impactful AI use case
  • Min 13:15: Opportunities for organizations, increase of domestic violence
  • Min 23:05: Fireside chat: Use cases, how to overcome AI bias, how to build solutions as communities rather than relying on governments and large organizations, etc.

To be updated on our next webinar sign up here.

About Omdena

Omdena is an innovation platform for building AI solutions to real-world problems through the power of bottom-up collaboration.



AI Insights: Why Bottom-Up Collaboration in Teams Works Best to Build Innovative AI Solutions

AI Insights: Why Bottom-Up Collaboration in Teams Works Best to Build Innovative AI Solutions

Killing the myth of the lone genius, why command-and-control work systems are failing to build AI solutions that are adopted.


You may not have heard of top-down management or bottom-up management, but you are definitely familiar with the former, as it’s a traditional management style. Essentially, the boss or leader makes all of the decisions, and the employees or team members carry them out.

According to Stanford research, top-down approaches often result in a lack of participation, input from the wrong people, and not enough creative conflict.

Now, before we get into the why let us understand the differences between both approaches.


Top-down all bad? No, but…

The advantage of top-down management is that decisions can be made and implemented very quickly. This is particularly important when time is limited. The other benefit of top-down project planning is that it helps align the project goals with the organization’s strategic goals as upper management is giving the directions.

But here comes the problem! 

Artificial Intelligence applied in and by organizations to solve real-world problems is a new field and there is no one size fits all model that can be replicated across companies and sectors.

It is just common sense that organizations do not have a strategy in place and solving real-world problems with AI requires innovative minds, lots of creativity, and cross-functional collaboration.

More than 50 percent of AI solutions fail due to a misalignment of stakeholders, no metrics, and no contextual AI strategy.

And there are more reasons why top-down does not work:

  • AI teams are by nature multi-disciplinary consisting of data scientists, Machine learning engineers, data engineers, and domain experts. All of which have different tasks to focus on but need to work together to integrate each other’s work and build a functional solution.
  • AI systems built in the lab are often biased and lack real-world approval. Bias comes from a lack of diversity and top-down means a single person or only a few make decisions. Not a good idea if your system is going to impact thousands or even millions of diverse users. Amazon’s sexist algorithm is an example of how it should not be done.
  • Running AI projects requires solving multiple problems in various domains such as accessing data, preparing the data, fine-tune parameters, testing different data models, and communicating results in a story-driven and value-oriented way. No single person has all the knowledge to cover all tasks needed. 


The alternative? Bottom-up collaboration


Photo by You X Ventures on Unsplash


The advantage of bottom-up planning is that the team members have a voice in the project planning and decisions are made collaboratively. This improves team communication and team building and also empowers the team members.

Identifying tasks first also leads to a more detailed project plan, with a potentially more accurate schedule, and a better understanding of the problem to be solved.


Benefits with regards to AI projects


Bottom-up development sets the ground for innovation by combining diversity of thought with a safe environment where information exchange happens easily and openly. Especially in AI projects, bottom-up teams require people from different backgrounds and experience levels. This high degree of diversity not only prevents biased solutions but also enables breakthroughs via fast iteration cycles and constant perspective sharing, which connects to the next point.

Less risk of bias

More perspectives, higher gender diversity, and cross-cultural teams result in a more inclusive approach to preparing datasets and building solutions.

More and better data

Another potential benefit of bottom-up driven teams is to they find creative ways to access new data sources and augment existing ones. In AI the team with the best data wins.

Higher user adoption

Many AI solutions and products fail in the real world due to a lack of customer trust and no contextual applicability. A bottom-up approach takes the development process out of the lab and into the real world by embedding diverse perspectives and open dialogue where chances are higher to build inclusive and trustworthy solutions.


How it works in reality: a real-world case study

A company that wants to become AI-powered and build solutions for real-world problems has to overcome several challenges along the way.

In theory, bottom-up collaboration sounds all good but many companies struggle to get access to AI experts and data scientists that can help to translate a problem into a deployable AI solution or prototype.

In addition, many organizations have little or no data, to begin with. And even if data is plentiful, the question remains, how to leverage the raw data to solve problems or gain valuable insights. If an organization made the step to develop an AI system, the next wave of challenges is just around the corner.

In order to help organizations, we started Omdena — an innovation platform where organizations host AI projects that are being solved by global and collaborative teams of up to 50 engineers.


Collaborative AI at Omdena


Omdena runs AI Projects with organizations that want to get started with AI, solve a real-world problem, or build deployable solutions within two months.

In our eight-week projects, a selected team of 40 to 50 engineers and domain experts delivers deployable solutions using real-world data. A team consists of various roles, backgrounds, and involves people from around the world, which mitigates not only bias but also results in fast and agile development.

Decisions are being made within different task groups and are discussed within the team to find the best-fit solution.

Our community has compiled many impactful examples of applying AI technology ranging from use cases in the environment to health, finance, safety, justice, and many more.

If you want to learn more about us you can check out all our projects and real-world case studies here.


About Omdena

Omdena is an innovation platform for building AI solutions to real-world problems through the power of bottom-up collaboration.




5 Reasons Why AI Hackathons Won’t Build Real-World Solutions

5 Reasons Why AI Hackathons Won’t Build Real-World Solutions

To create meaningful innovation and build real solutions, we need to move beyond (AI) hackathons.


By Omdena Founder Rudradeb Mitra 



Hackathons are perceived as a fast track to innovation. Creative minds come together and solve problems. This all sounds good in theory but let us look at the facts.

For example, there have been dozens of hackathons in response to COVID-19, 1000s of people are giving their time to build solutions. I even mentored one of the largest, where over 1000 engineers participated. The organizers put days, if not weeks, of work into it. So I truly commend their efforts and their goodwill. However, without taking away anything from them I question the effectiveness of hackathons.


Why AI hackathons won’t build real-world solutions


Reason 1: Lack of domain expertise

Social problems like COVID-19 cannot be solved only by engineers. To build real-world solutions we need to involve policymakers, domain experts, users — something that teams participating in hackathons often don’t have access to.


Reason 2: Absence of diverse backgrounds leads to bias

Hackathons are often formed by teams who know each other and thus lack fresh ideas. We have seen that systems developed only by engineers end up being biased. A “diversity disaster” has resulted in flawed systems that amplify gender and racial biases according to a survey, published by the AI Now Institute, encompassing more than 150 studies and reports.

The report summary says an overwhelmingly white and male field has reached ‘a moment of reckoning’ over discriminatory systems

In addition, when I was mentoring hackathons, I can say that out of 100+ ideas — we can summarize all of them in 10–15 ideas. Most of them were similar due to similar backgrounds of the participants.


Reason 3: Too little development time

Most hackathons run only for a few days or weeks, which is not enough to build or test solutions replicable in production environments.

In a hackathon often you are motivated to impress judges but in a real-world project, you are building entire solutions. Building a prototype is easy, but there is customer research, product marketing, design, and sales, which will determine if the prototype is actually implemented and creates value.


Reason 4: Competition vs. Collaboration

Why do we need hundreds of teams to compete solving the same problems while we could have several teams collaborate so solve multiple problems?

In one of my previous articles, I argue that competition-based models like Kaggle are not the best approach to build real-world solutions. Among many reasons, a key issue is that people are incentivized to win instead of working together to find the best solution to a problem.

For example, in a recent competition, a Kaggle 1st place winner cheated using a fake dataset to get $10,000 prize money and gain exposure.


The alternative? A collaborative approach

To solve the aforementioned problems and create an environment for building real-world solutions to problems like COVID-19, we founded Omdena.

Omdena is an innovation platform for building AI solutions to real-world problems through the power of bottom-up collaboration.

Omdena’s Covid19 initiative displayed at Times Square NYC

Omdena’s Covid19 initiative displayed at Times Square NYC


Contrary to AI hackathons we embed the following mechanisms to build inclusive and deployable solutions.


1. Fostering global collaboration

Most of today’s challenges are global in nature. It is not that one country or group of people can solve it.

To find solutions we need a model where global communities can come together to solve problems, share their data and build solutions. In the world of Big data, AI and Machine Learning, data is key. It is not a sophisticated algorithm or a better team, but it’s the team with better (and more) data that wins.


2. Following a bottom-up model to enable creativity

I firmly believe the future of innovation is bottom-up, where communities come together to collaborate and solve their problems. Communities will drive the future of AI, not Governments or Corporations. I argue that communities have both intrinsic and extrinsic motivation to solve the problem, which is essential in building solutions.

And the evidence is clear. At Omdena our global community in more than 75 countries develops innovative solutions every month. Up to 50 engineers and domain experts collaborate for two months where ideas are shared openly to find the best-fit solution.

With a bottom up approach, those who are more involved with the specifics of their field are included in the ideation and brainstorming process, with the result being a more harmonized and inclusive development system.


3. Working closely with domain experts

Most real-world problems are not limited to just a data science problem but require domain experts to create value. In our projects, domain experts work in a constant exchange with the AI teams to help the company refine the problem, prepare the data, and build a solution that is applicable in their context.


4. Involving people who face the problem

Something which goes beyond domain expertise is to incorporate people who faced the problem. This brings empathy and can help to reduce bias.

On Omdena’s innovation platform almost 1000 AI engineers from 79 countries have collaborated to build real-world solutions within a time frame of two months. The benefits of Collaborative AI are enabled by the people who are part of the project. We believe rigid top-down management principles or winner-takes-all approaches create the wrong incentives for a world where solidarity and teamwork are more important than ever.


Human first, technology second

In conclusion, I am arguing:

All of us interested to build an inclusive future, need to think more holistically to create an environment beyond gender, race, and cultural backgrounds and focus on how we can collaborate as humans.

Digitization and AI have enormous potential for doing good in all aspects of life and in all sectors of the economy. However, it is the combination of people with technology that truly enables progress and higher productivity. We have to emphasize community and purpose. That is the key to create meaningful innovation and products.


About Omdena

Building AI Solutions Collaboratively 

Omdena is an innovation platform for building AI solutions to real-world problems through the power of bottom-up collaboration.

Learn more about the power of Collaborative AI.

How to Solve Ethical Issues in AI Through Collaboration

How to Solve Ethical Issues in AI Through Collaboration

How a community of technology changemakers pioneered a way of building ethical AI solutions through collaborative and diverse teams.

Article written by Rudradeb Mitra and Michael Burkhardt 

This article describes the founding story of Omdena.


Ethical issues in AI

Photo by Pablo García Saldaña on Unsplash


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

Building ethical AI solutions means to answer the “hard questions” — Is it moral? Is it safe? What value & positive impact 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.

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 move away from individuals or small AI teams but shift towards communities of people solving a problem they deeply care about


50 AI enthusiasts, two experts, and a common mission

Late in 2018, I worked with a startup in India to build a clean tech solution.

The technical goal was to build a sophisticated Machine Learning model to increase the adoption of rooftop solar panels. 

Instead of relying on a small team, I managed to build a project community of more than 50 AI engineers and 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 enthusiasts came from all over India, have never met each other but were 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 enthusiasts to build solutions that are ethical, trusted, and value-creating; and as a result beneficial for society.

In the words of one of the collaborators:

“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.

The advantages of Collaborative AI are as follows.


1. Empowering talent globally to acquire real-world skills

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 were announced in the community and collaborators took up those tasks according to their skill-set.

The advantages for collaborators:

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

Abhigyan Das, who helped 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 ends; the organization gets the best of enthusiastic people and the members add to their learning curve by working towards delivering their own product while making a real impact. This is only possible when there is mutual trust and respect for each other.”, adds Smriti Bahugana.


2. Building trusted and value-creating solutions

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

How organizations benefit:

  • More trust
  • Faster results
  • Access to leading AI experts and domain knowledge
  • 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 fellows and they provided me with the data in a given time frame.”


4. Democratizing AI and solving pressing ethical issues

There are tens of thousands of AI 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.

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 to solve not only ethical issues in AI but also some of the most pressing problems in the world.



About Omdena

Omdena is a global platform where a community of changemakers builds AI solutions to real-world problems via collaboration.

Learn more about us and Collaborative AI.

8 Ways Omdena Helps Organizations Build Real-World AI Solutions

8 Ways Omdena Helps Organizations Build Real-World AI Solutions

An organization that wants to become AI-powered and build solutions for real-world problems has to overcome several challenges along the way. 

One of the most common obstacles is to get access to AI experts and data scientists that can help to translate a problem into a deployable AI solution or prototype. In addition, many organizations have little or no data, to begin with. And even if data is plentiful, the question remains, how to leverage the raw data to solve problems or gain valuable insights. If an organization made the step to develop an AI system, the next wave of challenges is just around the corner.

Namely, AI systems built in the lab are often biased and lack real-world approval. More than 50 percent of AI solutions fail due to a misalignment of stakeholders, no metrics, and no contextual AI strategy.

Now, how to solve these issues and build AI solutions successfully?

At Omdena, we have worked with more than 800 AI engineers to find ways to overcome these problems. 

Our approach to developing AI technology is based on collaboration where diversity of thought and inclusion is embedded in the process.

Omdena runs Collaborative AI Projects with organizations that want to get started with AI, solve a real-world problem, or build deployable solutions within two months.

We have been collaborating with various organizations, including the United Nations, companies, startups, and NGOs around the world.

In our eight-week challenges, a selected community of 40 to 50 AI engineers delivers innovative and ethical solutions using real-world data. 

One of our recent partners, Wildfire detection company Sintecsys, describes our diverse teams in the following way: 

An Omdena project brings together power, speed, and accuracy, through the dedication and impeccability from the Data Scientists involved and the leadership that emerged from the collaborative process. It not only got us into the AI game but also pointed us in the most suitable direction for our company. 


Problems we solve for you


The following list gives you a headstart on the advantages of Collaborative AI and whether you’d qualify for a challenge with our global community.


1. I don’t have a clearly defined problem for a project

There are plenty of meaningful problems that could be solved with the right AI technology.

In a nonextensive report, McKinsey compiled a library of more than 160 AI use cases.

Even though the AI movement is accelerating, the progress can be still seen as slow up until now. One of the main reasons is that many of the problems need to be translated into an AI-suitable format. 

Running an Omdena challenge means to work with AI and domain experts to transform a vague problem statement to a specific AI use case within a couple of days or weeks. 

For example, in our project with Impacthub Istanbul, the initial statement was to “improve the aftermath management of an earthquake with AI”. Within two weeks, the community narrowed it down to “Predicting the safest route after an earthquake for people at special risk (schools, hospitals)”. 

2. I do not have (quality) data 

According to several studies and our experience working with more than 600 AI engineers, data challenges, are the most common obstacle organizations face. 

Building a cutting-edge AI-based solution comes with the necessity to feed the algorithm not only the right type of data but also high-quality data.

In an Omdena challenge, our collaborators take care of:

  • Data collection 
  • Data understanding 
  • Data preparation 

A special bonus to ensure high quality is our community-enabled peer-to-peer review process where several people check each other’s code throughout the entire project. 

Our challenge to build a chatbot for Post-Traumatic-Stress-Disorder (PTSD) treatment was even kicked off with no data set at all. Within a week the community found creative ways to access various data sets

3. I do not have the technical staff for an AI project

No AI team, no AI solution?

Obviously, the lack of AI talent is the cornerstone of all obstacles associated with AI adoption. 

At Omdena you’ll get access to a selected community of AI practitioners that are most suitable to tackle your problem. Our AI challenges bring together AI experts, data scientists, machine learning and data engineers, as well as domain experts. 

Working with a fully staffed and diverse community comes with additional advantages, which bring us to the next point. 

4. I want to know how to build my own AI team

A fatal mistake many organizations make is to just hire AI experts or even beginners and expect magic to happen. 

Without asking the right questions, building the AI team will end in an expensive disaster. 

Here are a few common questions that need to be answered properly before making hiring decisions. 

  • What specific capabilities do I need in my organizational context?
  • What are the team composition and level of experience required?
  • How to attract and communicate with data scientists and machine learning engineers? 
  • What are my current data and infrastructure capabilities? 

According to an O-Reily study “hiring the required roles” is the third-biggest problem companies face. 

When building your team, you want to be well-prepared to attract the sought-after data scientists to your enterprise, startup, or NGO. 

An eight-weeks deep dive into a full-scale project has significantly helped our previous partners to derive their own AI strategy.

Wildfire detection company Sintecsys, for example, worked with 42 Omdena collaborators in conjunction with their internal team to scale their business model. The developed machine learning models are currently implemented to avoid wildfires in the Amazon rain forest. 

5. I want to build a fully deployable AI solution

In most of our challenges organizations request an implementable cutting-edge solution, within a short time frame.

The reason why we can move faster than in traditional development approaches are our Collaborative AI processes:

Once a challenge is kicked-off tasks and responsibilities are quickly allocated to the right people 

In addition, we leverage available code, best-in practice tools, and processes to simultaneously test and develop multiple machine learning models. 

And all of this happens in a diverse and inclusive setting where perspectives are shared most effectively. 

For globally known NGO Safecity India, our community built an algorithm to predict safe routes for women at risk of sexual harassment. In Safecity’s words: 

Omdena is one of the world’s finest sets of data scientists building solutions for Good. In only two months, we accomplished what we tried for two years reaching out to some of the biggest corporations. 

6. I want to prototype and validate my hypotheses

Even if your goal is not to build a fully deployable solution, yet, running a challenge will deliver valuable insights through data exploration and rapid prototyping. 

Global AI expert Andrew NG points out that in order to derive an AI strategy successfully, pilot projects are the best way to “get the flywheel turning” as soon as possible. 

A project should generate a quick win such as insights on where to focus your capabilities. 

In a recent challenge, we have helped the United Nations World Food Programme (WFP) to leverage open-source data in Nepal to fight hunger (link). As a follow-up challenge, we are working with the WFP in Istanbul to improve the food supply management in case of natural disasters.

7. I want to develop an ethical & trustworthy solution

According to findings by a New York University research center, the lack of diversity in the artificial intelligence field has reached “a moment of reckoning”. A “diversity disaster” has contributed to flawed systems that perpetuate gender and racial biases found a survey, published by the AI Now Institute, of more than 150 studies and reports.

One of our key mission points is to ensure diversity and inclusion in our challenges. 

We are proud to say that 35 percent of our AI community collaborators are female and see diversity of thought as a necessary condition to develop trustworthy products for real-world product adoption.

In the end, AI for Good solutions cannot be built in isolation of the people and social circumstances that make them necessary in the first place. 

8. My organization would benefit from global exposure

Finally, we work with our partners to not only build cutting-edge AI solutions for tough problems but also provide a global platform where your organization will be showcased to leading partners in the AI space. 

Partnering with Omdena includes joint promotional efforts pre-project when we announce the call for applications and invite the AI community, during a project, as well as after in form of social media coverage, case studies, and webinars. 

We at Omdena build long-lasting partnerships and are excited to help you solve your problems. 

“Why compete if we can collaborate” 


For further questions, check out our FAQs or get to know the Omdena team in our LiveChat.

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