To give women academics and others well-deserved—and overdue—time in the spotlight, TechCrunch is launching a series of interviews focusing on notable women who have contributed to the AI revolution. We’ll be publishing several pieces throughout the year as the AI boom continues, highlighting essential work that often goes unrecognized. Read more profiles here.
Sandra Wachter is Professor and Senior Researcher in Data Ethics, Artificial Intelligence, Robotics, Algorithms and Regulation at the Oxford Internet Institute. He is also a former fellow of the Alan Turing Institute, the UK’s national institute for data science and artificial intelligence.
While at the Turing Institute, Watcher assessed the ethical and legal aspects of data science, highlighting instances where opaque algorithms have become racist and sexist. It also looked at ways to control artificial intelligence to counter misinformation and promote fairness.
Q&A
Briefly, how did you get started with AI? What drew you to the space?
I can’t remember a time in my life when I didn’t believe that innovation and technology have incredible potential to make people’s lives better. However, I also know that technology can have devastating effects on people’s lives. And so, I’ve always been driven – mostly by my strong sense of justice – to find a way to guarantee that perfect middle ground. Enabling innovation while protecting human rights.
I have always felt that law has a very important role to play. Law can be the middle ground that protects people but allows innovation. Law as a discipline came very naturally to me. I like challenges, I like to understand how a system works, see how I can game it, find loopholes and then close them.
AI is an incredibly transformative force. It applies to finance, employment, criminal justice, immigration, health and the arts. This can be good and bad. And whether that’s good or bad is a matter of planning and politics. Of course I was drawn to it because I felt that legislation can make a real contribution to ensuring that innovation benefits as many people as possible.
What work are you most proud of (in AI)?
I think the project I’m most proud of right now is a project co-authored by Brent Mittelstadt (philosopher), Chris Russell (computer scientist), and myself as a lawyer.
Our latest work on prejudice and justice, “The unfairness of fair machine learning”, revealed the harmful impact of the imposition of many measures of “group justice” in practice. In particular, justice is achieved by “equalizing” or making everyone worse off, rather than helping disadvantaged groups. This approach is highly problematic under EU and UK non-discrimination law, as well as ethically problematic. In a piece in Wired We discussed how harmful leveling can be in practice — in health care, for example, enforcing group justice could mean missing more cancer cases than absolutely necessary, while making a system less accurate overall.
For us this was scary and something that is important for people in technology, politics and really every person to know. In fact, we have worked with UK and EU regulators and shared our alarming results with them. I sincerely hope that this will give policymakers the necessary leverage to implement new policies that prevent AI from causing such serious harm.
How do you address the challenges of the male-dominated tech industry and, by extension, the male-dominated AI industry
The interesting thing is that I never saw technology as something that “belonged” to men. It wasn’t until I started school that society told me that technology has no place for people like me. I still remember when I was 10 years old the curriculum dictated that the girls should be knitting and sewing while the boys were building birdhouses. I also wanted to build a birdhouse and asked to be transferred to the boys’ class, but my teachers told me “girls don’t do that”. I even went to the school principal trying to overturn the decision but unfortunately failed at that time.
It’s very hard to fight against a stereotype that says you shouldn’t be part of this community. I wish I could say that things like this don’t happen anymore, but that’s unfortunately not true.
However, I was incredibly fortunate to work with allies like Brent Mittelstadt and Chris Russell. I was privileged to have incredible mentors as my Ph.D. supervisor and I have a growing network of like-minded people of all genders who are doing their best to steer the path forward to improve the situation for everyone interested in technology.
What advice would you give to women looking to enter the AI field?
Above all try to find like-minded people and allies. Finding your people and supporting each other is vital. My most impressive work always comes from conversing with open-minded people from other backgrounds and industries to solve common problems we face. Accepted wisdom alone can’t solve new problems, so women and other groups that have historically faced barriers to entry in AI and other tech fields have the tools to truly innovate and deliver something new.
What are some of the most pressing issues facing artificial intelligence as it evolves?
I believe there is a wide range of issues that need serious legal and political consideration. To name a few, AI is plagued by biased data that leads to biased and unfair results. AI is inherently opaque and hard to understand, yet it is tasked with deciding who gets a loan, who gets the job, who should go to jail, and who is allowed to go to university.
Generative AI has relevant issues, but it also contributes to misinformation, is full of delusions, violates data protection and intellectual property rights, puts people’s jobs at risk, and contributes more to climate change than the airline industry.
We have no time to waste. we should have addressed these issues yesterday.
What are some issues AI users should be aware of?
I think there is a tendency to believe a certain narrative along the lines of “AI is here and here to stay, get on board or be left behind”. I think it’s important to think about who is pushing this narrative and who is profiting from it. It is important to remember where the real power lies. The power does not belong to those who innovate, but to those who buy and implement AI.
So consumers and businesses should ask themselves, “Is this technology really helping me, and how?” Electric toothbrushes now have “AI” built into them. Who is this for? Who needs this? What is improving here?
In other words, ask yourself what is broken and what needs fixing, and whether AI can actually fix it.
This type of thinking will shift market power and innovation will hopefully head in a direction that focuses on utility for a community rather than just profit.
What’s the best way to build responsible AI?
Having laws in place requiring responsible AI. Here again, a very unhelpful and untrue narrative tends to prevail: that regulation stifles innovation. This is not real. Regulation is suffocating harmful innovation. Good laws promote and nurture moral innovation. This is why we have safe cars, planes, trains and bridges. Society does not lose if regulation prevents it
creating artificial intelligence that violates human rights.
Traffic and safety regulations for cars are also said to “stifle innovation” and “limit range”. These laws prevent people from driving without a license, prevent cars from entering the market without seat belts and airbags, and penalize people who do not drive within the speed limit. Imagine what the safety record of the auto industry would be like if we didn’t have laws to regulate vehicles and drivers. Artificial intelligence is currently at a similar inflection point, and heavy industry lobbying and political pressure mean it’s still unclear which way it will go.
How can investors best push for responsible AI?
I wrote a paper a few years ago entitled “How fair AI can make us richer.” I strongly believe that artificial intelligence that respects human rights and is unbiased, explainable and sustainable is not only the legally, ethically and morally right thing to do, but it can also be profitable.
I really hope that investors will understand that if they push for responsible research and innovation, they will also get better products. Bad data, bad algorithms and bad design choices lead to worse products. Even if I can’t convince you that you should do the moral thing because it’s the right thing, I hope you can see that the moral thing is also more profitable. Ethics should be seen as an investment and not as an obstacle to be overcome.
