Bias in AI: A problem recognized but still unresolved

There are those who praise the technology as the solution to some of humankind’s gravest problems, and those who demonize AI as the world’s greatest existential threat. Of course, these are two ends of the spectrum, and AI, surely, presents exciting opportunities for the future, as well as challenging problems to be overcome.

One of the issues that’s attracted much media attention in recent years has been the prospect of bias in AI. It’s a topic I wrote about in TechCrunch (Tyrant in the Code) more than two years ago. The debate is raging on.

At the time, Google had come under fire when research showed that when a user searched online for “hands,” the image results were almost all white; but when searching for “black hands,” the images were far more derogatory depictions, including a white hand reaching out to offer help to a black one, or black hands working in the earth. It was a shocking discovery that led to claims that, rather than heal divisions in society, AI technology would perpetuate them.

As I asserted two years ago, it’s little wonder that such instances might occur. In 2017, at least, the vast majority of people designing AI algorithms in the U.S. were white males. And while there’s no implication that those people are prejudiced against minorities, it would make sense that they pass on their natural, unconscious bias in the AI they create.

And it’s not just Google algorithms at risk from biased AI. As the technology becomes increasingly ubiquitous across every industry, it will become more and more important to eliminate any bias in the technology.

Understanding the problem

AI was indeed important and integral in many industries and applications two years ago, but its importance has, predictably, increased since then. AI systems are now used to help recruiters identify viable candidates, loan underwriters when deciding whether to lend money to customers and even judges when deliberating whether a convicted criminal will re-offend.

Of course, data can certainly help humans make more informed decisions using AI and data, but if that AI technology is biased, the result will be as well. If we continue to entrust the future of AI technology to a non-diverse group, then the most vulnerable members of society could be at a disadvantage in finding work, securing loans and being fairly tried by the justice system, plus much more.

Fortunately, the issue around bias in AI has come to the fore in recent years, and more and more influential figures, organizations and political bodies are taking a serious look at how to deal with the problem.

The AI Now Institute is one such organization researching the social implications of AI technology. Launched in 2017 by research scientists Kate Crawford and Meredith Whittaker, AI Now focuses on the effect AI will have on human rights and labor, as well as how to safely integrate AI and how to avoid bias in the technology.

In May last year, the European Union put in place the General Data Protection Regulation (GDPR) — a set of rules that gives EU citizens more control over how their data is used online. And while it won’t do anything to directly challenge bias in AI technology, it will force European organizations (or any organization with European customers) to be more transparent in their use of algorithms. This will put extra pressure on companies to ensure they’re confident in the origins of the AI they’re using.

And while the U.S. doesn’t yet have a similar set of regulations around data use and AI, in December 2017, New York’s city council and mayor passed a bill calling for more transparency in AI, prompted by reports the technology was causing racial bias in criminal sentencing.

Despite research groups and government bodies taking an interest in the potentially damaging role biased AI could play in society, the responsibility largely falls to the businesses creating the technology, and whether they’re prepared to tackle the problem at its core. Fortunately, some of the largest tech companies, including those that have been accused of overlooking the problem of AI bias in the past, are taking steps to tackle the problem.

Microsoft, for instance, is now hiring artists, philosophers and creative writers to train AI bots in the dos and don’ts of nuanced language, such as to not use inappropriate slang or inadvertently make racist or sexist remarks. IBM is attempting to mitigate bias in its AI machines by applying independent bias ratings to determine the fairness of its AI systems. And in June last year, Google CEO Sundar Pichai published a set of AI principles that aims to ensure the company’s work or research doesn’t create or reinforce bias in its algorithms.

Demographics working in AI

Tackling bias in AI does indeed require individuals, organizations and government bodies to take a serious look at the roots of the problem. But those roots are often the people creating the AI services in the first place. As I posited in “Tyrant in the Code” two years ago, any left-handed person who’s struggled with right-handed scissors, ledgers and can-openers will know that inventions often favor their creators. The same goes for AI systems.

New data from the Bureau of Labor Statistics shows that the professionals who write AI programs are still largely white males. And a study conducted last August by Wired and Element AI found that only 12% of leading machine learning researchers are women.

This isn’t a problem completely overlooked by the technology companies creating AI systems. Intel, for instance, is taking active steps in improving gender diversity in the company’s technical roles. Recent data indicates that women make up 24% of the technical roles at Intel — far higher than the industry average. And Google is funding AI4ALL, an AI summer camp aimed at the next generation of AI leaders, to expand its outreach to young women and minorities underrepresented in the technology sector.

However, the statistics show there is still a long way to go if AI is going to reach the levels of diversity required to stamp out bias in the technology. Despite the efforts of some companies and individuals, technology companies are still overwhelmingly white and male.

Solving the problem of bias in AI

Of course, improving diversity within the major AI companies would go a long way toward solving the problem of bias in the technology. Business leaders responsible for distributing the AI systems that impact society will need to offer public transparency so that bias can be monitored, incorporate ethical standards into the technology and have a better understanding of who the algorithm is supposed to be targeting.

But without regulations from government bodies, these types of solutions could come about too slowly, if at all. And while the European Union has put in place GDPR that in many ways tempers bias in AI, there are no strong signs that the U.S. will follow suit any time soon.

Government, with the help of private researchers and think tanks, is moving quickly in the direction and trying to grapple with how to regulate algorithms. Moreover, some companies like Facebook are also claiming regulation could be beneficial. Nevertheless, high regulatory requirements for user-generated content platforms could help companies like Facebook by making it nearly impossible to compete for new startups entering the market.

The question is, what is the ideal level of government intervention that won’t hinder innovation?

Entrepreneurs often claim that regulation is the enemy of innovation, and with such a potentially game-changing, relatively nascent technology, any roadblocks should be avoided at all cost. However, AI is a revolution that will continue whether it’s wanted or not. It will go on to change the lives of billions of people, and so it clearly needs to be heading in an ethical, unbiased direction.

Governments and business leaders alike have some serious questions to ponder, and not much time to do it. AI is a technology that’s developing fast, and it won’t wait for indecisiveness. If the innovation is allowed to go on unchecked, with few ethical guidelines and a non-diverse group of creators, the results may lead to a deepening of divisions in the U.S. and worldwide.