Doing good data science
Data scientists, data engineers, AI and ML developers, and other data professionals need to live ethical values, not just talk about them.
Lady Justice (source: Pxhere.com)
The hard thing about being an ethical data scientist isn’t understanding ethics. It’s the junction between ethical ideas and practice. It’s doing good data science.
There has been a lot of healthy discussion about data ethics lately. We want to be clear: that discussion is good, and necessary. But it’s also not the biggest problem we face. We already have good standards for data ethics. The ACM’s code of ethics, which dates back to 1993, is clear, concise, and surprisingly forward-thinking; 25 years later, it’s a great start for anyone thinking about ethics. The American Statistical Association has a good set of ethical guidelines for working with data. So, we’re not working in a vacuum.
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And, while there are always exceptions, we believe that most people want to be fair. Data scientists and software developers don’t want to harm the people using their products. There are exceptions, of course; we call them criminals and con artists. Defining “fairness” is difficult, and perhaps impossible, given the many crosscutting layers of “fairness” that we might be concerned with. But we don’t have to solve that problem in advance, and it’s not going to be solved in a simple statement of ethical principles, anyway.
The problem we face is different: how do we put ethical principles into practice? We’re not talking about an abstract commitment to being fair. Ethical principles are worse than useless if we don’t allow them to change our practice, if they don’t have any effect on what we do day-to-day. For data scientists, whether you’re doing classical data analysis or leading-edge AI, that’s a big challenge. We need to understand how to build the software systems that implement fairness. That’s what we mean by doing good data science.
Any code of data ethics will tell you that you shouldn’t collect data from experimental subjects without informed consent. But that code won’t tell you how to implement “informed consent.” Informed consent is easy when you’re interviewing a few dozen people in person for a psychology experiment. Informed consent means something different when someone clicks on an item in an online catalog (hello, Amazon), and ads for that item start following them around ad infinitum. Do you use a pop-up to ask for permission to use their choice in targeted advertising? How many customers would you lose? Informed consent means something yet again when you’re asking someone to fill out a profile for a social site, and you might (or might not) use that data for any number of experimental purposes. Do you pop up a consent form in impenetrable legalese that basically says “we will use your data, but we don’t know for what”? Do you phrase this agreement as an opt-out, and hide it somewhere on the site where nobody will find it?
That’s the sort of question we need to answer. And we need to find ways to share best practices. After the ethical principle, we have to think about the implementation of the ethical principle. That isn’t easy; it encompasses everything from user experience design to data management. How do we design the user experience so that our concern for fairness and ethics doesn’t make an application unuseable? Bad as it might be to show users a pop-up with thousands of words of legalese, laboriously guiding users through careful and lengthy explanations isn’t likely to meet with approval, either. How do we manage any sensitive data that we acquire? It’s easy to say that applications shouldn’t collect data about race, gender, disabilities, or other protected classes. But if you don’t gather that data, you will have trouble testing whether your applications are fair to minorities. Machine learning has proven to be very good at figuring its own proxies for race and other classes. Your application wouldn’t be the first system that was unfair despite the best intentions of its developers. Do you keep the data you need to test for fairness in a separate database, with separate access controls?
To put ethical principles into practice, we need space to be ethical. We need the ability to have conversations about what ethics means, what it will cost, and what solutions to implement. As technologists, we frequently share best practices at conferences, write blog posts, and develop open source technologies—but we rarely discuss problems such as how to obtain informed consent.
There are several facets to this space that we need to think about.
First, we need corporate cultures in which discussions about fairness, about the proper use of data, and about the harm that can be done by inappropriate use of data can be considered. In turn, this means that we can’t rush products out the door without thinking about how they’re used. We can’t allow “internet time” to mean ignoring the consequences. Indeed, computer security has shown us the consequences of ignoring the consequences: many companies that have never taken the time to implement good security practices and safeguards are now paying with damage to their reputations and their finances. We need to do the same when thinking about issues like fairness, accountability, and unintended consequences.
We particularly need to think about the unintended consequences of our use of data. It will never be possible to predict all the unintended consequences; we’re only human, and our ability to foresee the future is limited. But plenty of unintended consequences could easily have been foreseen: for example, Facebook’s “Year in Review” that reminded people of deaths and other painful events. Moving fast and breaking things is unacceptable if we don’t think about the things we are likely to break. And we need the space to do that thinking: space in project schedules, and space to tell management that a product needs to be rethought.
We also need space to stop the production line when something goes wrong. This idea goes back to Toyota’s Kanban: any assembly line worker can stop the line if they see something going wrong. The line doesn’t restart until the problem is fixed. Workers don’t have have to fear consequences from management for stopping the line; they are trusted, and expected to behave responsibly. What would it mean if we could do this with product features? If anyone at Facebook could have said “wait, we’re getting complaints about Year in Review” and pulled it out of production until someone could investigate what was happening?
It’s easy to imagine the screams from management. But it’s not hard to imagine a Toyota-style “stop button” working. After all, Facebook is the poster child for continuous deployment, and they’ve often talked about how new employees push changes to production on their first day. Why not let employees pull features out of production? Where are the tools for instantaneous undeployment? They certainly exist; continuous deployment doesn’t make sense if you can’t roll back changes that didn’t work. Yes, Facebook is a big, complicated company, with a big complicated product. So is Toyota. It worked for them.
The issue lurking behind all of these concerns is, of course, corporate culture. Corporate environments can be hostile to anything other than short-term profitability. That’s a consequence of poor court decisions and economic doctrine, particularly in the U.S. But that inevitably leads us to the biggest issue: how to move the needle on corporate culture. Susan Etlinger has suggested that, in a time when public distrust and disenchantment is running high, ethics is a good investment. Upper-level management is only starting to see this; changes to corporate culture won’t happen quickly.
Users want to engage with companies and organizations they can trust not to take unfair advantage of them. Users want to deal with companies that will treat them and their data responsibly, not just as potential profit or engagement to be maximized. Those companies will be the ones that create space for ethics within their organizations. We, the data scientists, data engineers, AI and ML developers, and other data professionals, have to demand change. We can’t leave it to people that “do” ethics. We can’t expect management to hire trained ethicists and assign them to our teams. We need to live ethical values, not just talk about them. We need to think carefully about the consequences of our work. We must create space for ethics within our organizations. Cultural change may take time, but it will happen—if we are that change. That’s what it means to do good data science.