Whether you’re just getting started or are already building your career, you can help Data Science and machine learning evolve in a way that benefits people and communities. If you are interested, here are a few ideas that could guide your next steps.
1. Learn more
A key element in getting involved and helping the field move in the right direction is to stay up-to-date with the latest developments in data science and machine learning. We’ve created this resource page to support your learning journey.
"Doing good" is a noble goal, but it’s sometimes hard to figure out how to start – some actions, for example, might come with downsides we’re initially unaware of. As a first step, we recommend some preliminary research to get a sense of the factors you should consider. For example, you can read Doing Good Better, a book introducing the concept of Effective Altruism, or watch this short video.
Next, you may want to zoom in specifically on the issues and potential risks that arise as machine learning and AI become more powerful. It’s a complex topic, and to help you navigate it we’ve created an audio course based on Season 2 of the TDS Podcast, where we focused on emerging problems in data science and neighboring fields. Our curated columns on these topics are another helpful resource you can browse at your own pace – they cover a wide range of real-world use cases (and solutions).
Beyond TDS, there are many other online spaces where you can find insightful articles, podcasts, and videos on similar topics. Our favorites include the Open Philanthropy Project, the Future of Life Institute, Center for Human Compatible AI, the TED playlist on new tech and new ethics, and this conversation on 80000 Hours.
2. Discuss, contribute, and connect
Reading and educating yourself are great places to start, but joining the conversation is even better. You can reach out directly to people working on these topics and offer your support. Twitter and LinkedIn are good places to find other members of the community who share your interests, and TDS is one, too.
Browse our curated columns on these topics, and if any article or project resonates with you, reaching out to authors in the comments section is a great way to make an initial connection. Many authors also welcome getting in touch via their Twitter and LinkedIn accounts.
If you’re interested in contributing your own insights on any of these topics, you can submit an article to our team. If we choose it for publication, we’ll help highlight it so that it’s visible for a longer time on TDS, promote it on our social media accounts, and share it with readers via our newsletter.
3. Help via your career
If you are looking for a career that can make a direct positive impact, we encourage you to have a look at 80000hours.org. To borrow their words:
"You have about 80,000 working hours in your career. That means your choice of the career is one of the biggest decisions you’ll ever make, so it’s really worth figuring out how to use that time for good."
80,000 Hours has published a guide to help you think through the ways you can make a positive contribution. They also list problem profiles on issues you might consider working on, a job board for mission-driven roles, and a podcast for interesting conversations about important ideas.
4. Volunteer on projects with a mission that speaks to you
There are many valuable projects and communities where your contributions could go a long way. Volunteering is also a great way to practice your skills and meet like-minded people who are passionate about leveraging tech and data for good. Here are a few links to consider:
- DataKind – A program that connects data scientists with social-change organizations where their skill sets can make a difference.
- DrivenData – Offering ongoing challenges where data scientists compete to create the "best statistical model for difficult predictive problems that make a difference."
- Solve for Good – A platform for organizations working on social issues to request volunteer help with data-intensive tasks.
- Online Volunteers – A UN-backed initiative that allows experts with research, tech, and other backgrounds to support organizations that tackle COVID-19-related problems.
- Catchafire – A community where professionals find opportunities to donate their time and skills to causes that inspire them.
You can also create your own project: from COVID-19 and pollution to local community help, there are so many ways to harness your knowledge and data expertise for a good cause. Check out the work we’ve featured on our Data for Change column if you need some real-world inspiration.
5. Donate to nonprofit organizations that move the field in the right direction
If you’d like to support organizations whose work makes AI safer and beneficial for all, we recommend checking out the Open Philanthropy Project, Ought, MIRI, GovAI, The Future of Life Institute, and the Center for Human Compatible AI.
If you’re not sure which one to pick, you can always donate instead to a fund that supports multiple organizations aligned around a similar mission. The Long-Term Future Fund, for example, supports numerous programs and research initiatives; you can also read this review to explore the AI-focused nonprofit landscape in greater detail.
Our Columns
Data for Change
The risks of AI and algorithmic bias are well known, but data science can also be – and often is – a force for good. From environmental research and police reform to public-health initiatives, data has informed and empowered people to demand better policy and to work towards greater equity for marginalized communities. Visit our Data for Change column to discover a wide range of articles that discuss data’s potential to improve people’s lives.
Model Interpretability
Machine learning algorithms affect us every day. We use these algorithms to make decisions, solve problems and make our lives easier. But the algorithms that drive applications don’t have fixed rules and regulations. Businesses, government agencies, and schools that rely on these algorithms don’t necessarily understand the details of how a model works or what the results really mean. We have a right to understand how a model arrives at its decisions and why people should (or shouldn’t) trust it. If we are going to use these algorithms to make legal, medical, and financial decisions for us, we need to make sure that we understand how a model was created and why it arrived at its results.
Fairness and Bias
Artificial intelligence and machine learning models are only as fair as the data they’re trained on. Algorithms don’t think for themselves and the predictions they make are based on the choices their creators have made. Without an awareness of these issues, bias is often built into models at every level. We need to learn how to avoid prejudice and build fairness into our models at every step.
Data Privacy
Data is being collected on all of us nearly all the time. It’s getting harder to maintain any semblance of privacy. We are continually providing information to companies to help them improve their business operations. Whether we’re offering our taste in music for better recommendations, our location for route advice, or information about our health and identity, we need to be aware of the choices we’re making and where our data is going. We need to ask how our data is protected and how we can maintain our privacy at every level.
AI Alignment and Safety
As we advance the capabilities of artificial intelligence, how do we program the subtleties and complexities of human decision making? Will the goals of AI be the same as our own? It’s unlikely that artificial intelligence will be aligned with human values by default. As nice as it would be to program a set of laws about not harming human beings, there are compelling reasons to believe that this won’t be possible, and that the alignment problem will in fact be very challenging to solve. AI is improving rapidly: the clock is ticking, and it is up to us to guide AI to make the right choices in every situation and to continue to do so as it becomes smarter. Discussing these issues now is critical for the future of AI.
We created this Medium publication because we believe that data-related knowledge is vital to making the world a better place. Not only does it enable us to understand ourselves and the world around us, but it also helps us make better decisions. In almost every field, data science can help us get a more precise understanding of the matter at hand and, therefore, better inform decision-makers of the potential consequences of their actions.
We have realized over time that important problems are emerging from the progress in our field. Problems related to privacy, interpretability, and alignment, for example. That’s why we decided to reach out to our community and research what the potential issues facing our field are and how we can help resolve them.
By continuing this work, we hope to stay closer to these critical questions and challenges. This helps us to ensure that our community is on the path to making the world a better place.
