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During the last three months of building our AI for Good initiative at Appsilon, we have learned and observed a lot about what appears to be a nascent, global movement. In this blog post I would like to share with you the insights we gathered including the opportunities, challenges and practical actions you can take to help the international community tap the potential of data science, machine learning and artificial intelligence in the fight for our planet.

Right from the beginning we had the intuition that there exists great demand for data science services amongst international organizations, academia and governmental actors working in various areas of sustainability. This has been confirmed through conversations with practitioners, requests for support we received through our contact channels and overall interest in the materials and ideas we shared with the community.

Similarly, there exists a large pool of data science talent, people who are eager to contribute their skills to projects that matter. In these last few months a number of events have ingrained the issues of climate change and the environment deeper into the public discourse. Major international publications and media outlets comment on the developments in this area, as well as threats arising from unsustainable use of the planet’s resources. The tech world is increasingly responsive to these messages and there clearly exists a demand for interesting projects.

The core idea behind the global AI for Good initiative is to bring these two diverse communities together. To link the most impactful projects with the best talent, so that together we can solve the problems humanity can no longer dismiss as far-out in the future. It is time to take responsibility for our planet and the well-being of future generations.

Using satellite data to identify patches of plastic pollution in the oceans. From BBC

The questions that arise on both sides of the movement include:

  • What is “good”? – the climate change and sustainability effort is becoming a multi-trillion dollar industry. It can be difficult for the tech community to identify projects that make sense. It is not necessarily the case that the best publicised and most funded projects that appeal to common sense are the ones we should direct our limited resources towards. The market can create perverse incentives, especially in the sustainability area, where rewards cannot be easily quantified and the outcomes are uncertain and often realise over the long-term.
  • What are the data science tools I can use to support my work? How can I apply them? – scientists and practitioners in the various areas of sustainability often face resource constraints, which limit their attention to the core activities of their work. They do not have the time to gain an understanding of the advantages greater utilization of new technologies could bring to their work. Even if they have such an understanding, they may not have the funding to procure data science services.
  • Where can I find interesting projects? / Where can I find data science support? – whilst there is some overlap, the two communities naturally operate in their separate “bubbles” and sometimes find it difficult to communicate. This can be referred to as a sort of a market failure, where demand and supply of projects and skills exist but are not be matched.

How can we go about resolving these issues? The short answer is that all actors in the tech and sustainability communities have to take an active role in growing the AI for Good movement. Together we can build and cross this bridge. The next three sections provide some practical ideas on how to go about this.

What can I do as a data science specialist?

The tech community has the most to gain by learning about the potential pathways towards a sustainable future from practitioners in these areas. Rather than jumping to conclusions or deciding what is best – it is sometimes best to just listen. At Appsilon we advocate what we call an “exploratory approach” where we take a step back and invite ideas from the community. We admit that we do not have the expertise to decide what is “good”. Of course, we allow discretion in this process, sometimes an internal idea can facilitate further research and result in an interesting project.

Network

We have found tremendous value in networking with individuals in the sustainability area. 

As a first step we would encourage you to follow interesting individuals and organizations on twitter and other social media. Going forward you may consider participating in domain-specific events and conferences. You can read about our experiences with the biodiversity community in one of our previous blog posts.

We also found great value in connecting with other data science specialists. My conversation with Kris Sankaran, one of the authors of the seminal “Tackling climate change with Machine Learning” paper we covered in our first AI for Good blog post, has been very inspiring. We agreed to share contacts and insights. It also felt good to feel as a part of something bigger and transnational.

Listen

Engage with online materials and learn about the various endeavours in the sustainability area. I suggest devoting a couple of hours every week to learn about these issues, so that you have sufficient knowledge to engage with the community when you get the chance. Subscribe to our newsletter or check out the links in the last section of this blog post if you need inspiration.

When engaging with the practitioners, make sure you allow them to fully explain their work and challenges without overwhelming them with your solutions. I know you may have great ideas, but stepping in too early may leave some interesting areas uncovered and have you working on a project that is tailoring the problem to your solution rather than the other way around.

Educate and offer support

With this caveat, I strongly encourage all data scientists to participate in the education effort, which aims at raising awareness within the sustainability community about the potential data science can bring to enhancing their efforts.

I believe that the smallest actions matter – talk about the AI for Good movement with your colleagues and share interesting projects through your social media. If you or your company have the resources consider participating in events and conferences devoted to the various aspects of sustainability efforts. You can also get creative. Appsilon, for instance, is planning a webinar for the biodiversity and related communities about the applications of data science.

Whilst all these actions will help build the movement, the direct contribution of your time would of course be most welcome by the resource-constrained organizations. Therefore, consider donating some of your time to this cause and pitch your organization internally to devote some of their resources towards that end.

What can I do as a practitioner in the sustainability area?

First of all, rest assured that there are vast resources offered by individuals and companies in the tech industry willing to contribute their time to your project.

Explore

There already exists a large body of knowledge about the applications of data science, machine learning and artificial intelligence. So far, these tools have primarily been finding application in commercial endeavours, but these can easily serve as an inspiration for your line of work. A simple Google search can show you the breadth of opportunities and I strongly encourage you to reach out to data scientists in case you require further explanation. You can start with reading through our blog and subscribing to our newsletter. We also provide a number of links to interesting resources in the last section of this blog post.

Google has teamed with AES to use drones and an AI model to perform regular inspections of wind turbines and flag potential damage

Request support

Once you have an idea for a potential application of DS/ML/AI to your project reach out to the data science community. You can find interested specialists through various communities, which are currently emerging (see links in the last section of this blog post) or by researching companies offering such services. Some companies, like Appsilon, offer their services pro bono.

Make sure that you clearly define your problem in as accessible terms as possible when requesting support. You may find the concepts in your field self-explanatory, but they can be entirely novel to a non-specialist. I believe I can speak for the entire data science community when I say that we are eager to develop a deep understanding of the intricacies of the fields we venture into with our skills and solutions. However, we have to be realistic about the level of involvement we can have outside of our main area of expertise.

Therefore, a well structured request would state the nature of your project, provide a description of the available data and the expected outcome including a use case. As a side note, you may think about the potential for publicising the results of the project to help the AI for Good movement gain traction and to offer additional value to the company or individual supporting your project.

What can we all do?

The impact of the AI for Good movement in bringing together projects that will secure the future of our planet and the data science talent rests upon growing the movement globally. All actions towards that end, from talking about it within our networks to engaging in large-scale international projects, will help make this a reality. There is a snowball effect in this process. Aside from direct interaction between individuals and organizations, new communities are evolving which focus on facilitating the exchange on a larger scale.

Therefore, I strongly encourage everyone interested in building the AI for Good movement, procuring data science services and finding projects to register and participate in the following forums:

climate change ai logo

A discussion board and online community for actors on both sides of the AI for Good initiative – NGOs, government actors, academia in the sustainability area and DS/ML/AI practitioners. Once registered you will find some of the most renowned data scientists there. You should also have a look at the broader Climate Change AI initiative.

plana.earth

There’s no plan B for our planet – Plan A uses data to predict where and how climate change will hit the hardest. They provide a crowdfunding facility for climate change projects and a community platform for stakeholders to build momentum for collective climate action. They share the insights through their Climate Academy and help companies to become sustainable, understand their carbon footprint and adapt to climate change. Their service is an excellent resource for anyone wishing to learn more about our planet and to get involved in the movement.

Mattermore.io is an upcoming service aimed at bringing concerned people and organizations together to introduce technology into climate change solutions. Once they are up and running they will help individuals to collaboratively define the most pressing problems, discover interesting projects to work on and organizations to follow or join. In the meantime, you can join their waitlist and think of the contribution you could make in 9 areas Mattermore will focus on.

ai commons logo

AI Commons is a community in the AI4G area, their mission: “AI Commons connects problem owners with the community of solvers to collectively create solutions with AI.” They already have the support of various for-profit businesses and NGOs. The initiative was founded by some of the greatest minds in the field of AI including Yoshua Bengio.

I believe that as we go forward the different efforts of these and other organisations and individuals in this area will converge towards a common goal – building a bridge across the domains of tech, environment and society to secure a sustainable future for our planet.

Thanks for reading. Follow me on Twitter at @tbaraslupski

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