Last week, a member of my family died in a car accident. Jasper was on his way home and was hit by a taxi. He fought for his life, but died the next day. Jasper was only 16 years old. I was at Davos and at one point I had to step out of the conference to cry. Five years ago, another family member died after she was hit by a truck when crossing the road.
It's hard to see a tragedy like this juxtaposed against a conference filled with people talking about improving the state of the world. These personal losses make me want to fast-forward to a time in the future where self-driving cars are normal, and life-saving innovations don't have as much regulatory red tape to cut through before they can have an impact. It's frustrating that we may have the right technology in sight today, but aren't making it available, especially when people's lives are at stake.
Imagine two busses full of people crashing, killing everyone on board, every single day. That is how many people die on America's roads every day. In fact, more people are killed by cars than guns, but I don't see anyone calling for a ban on automobiles. Car accidents (and traffic jams) are almost always the result of human error. It is estimated that self-driving cars could reduce deaths on the road by 90%. That is almost 30,000 lives saved each year in the US alone. The life-saving estimates for driverless cars are on par with the efficacy of modern vaccines. I hope my children, now ages 6 and 8, will never need a driver's license and can grow up in a world with driverless cars.
The self-driving car isn't as far off as you might think but is still being held back by government regulators. Delayed technology isn't limited to self-driving cars. Life-saving innovations in healthcare are often held back by regulatory requirements. The challenge of climate change could be addressed faster if the regulatory uncertainty around solar and wind power permits and policies were reduced. The self-serving interest of lobbying groups focused on maintaining the status quo for industries like Big Oil make it harder for alternative energies to gain momentum.
Regulators need to frame their jobs differently; they need to ask how they can facilitate and enable emerging disruptive innovations, rather than maintain existing systems. Their job should focus more on removing any barriers that prevent disruptions from having a faster impact. If they do this job well, some established institutions will fail. In some cases, economic sacrifices by the incumbents should be of lesser concern than advancing social health and safety for the benefit of society. I'm less concerned about technology destroying jobs, and more concerned about our children not being able to benefit from available technical advances that improve their lives. We should realize that opportunities for long-term economic growth come with short-term disruption or temporary pain.
Losing family members in fatal accidents makes one think about what could have been done. I'm often asked how one can create a "Silicon Valley" model elsewhere in the world. I may have an answer. If you want to out-"Silicon Valley" Silicon Valley, create a region with a regulatory environment that supports prompt, responsible innovation to drive the adoption and iteration of new technologies. A region where people can responsibly launch self-driving cars, fast-track healthcare and address climate change. A region where long-term advantages are valued more than short-term disadvantages. Such a region would attract capital and entrepreneurs, and would be much better for our children.
Volkswagen's recent emissions scandal highlighted the power that algorithms wield over our everyday lives. As technology advances and more everyday objects are driven almost entirely by software, it's become clear that we need a better way to catch cheating software and keep people safe.
A solution could be to model regulation of the software industry after the US Food and Drug Administration's oversight of the food and drug industry. The parallels are closer than you might think.
The case for tighter regulation
When Volkswagen was exposed for programming its emissions-control software to fool environmental regulators, many people called for more transparency and oversight over the technology.
One option discussed by the software community was to open-source the code behind these testing algorithms. This would be a welcome step forward, as it would let people audit the source code and see how the code is changed over time. But this step alone would not solve the problem of cheating software. After all, there is no guarantee that Volkswagen would actually use the unmodified open-sourced code.
Open-sourcing code would also fail to address other potential dangers. Politico reported earlier this year that Google's algorithms could influence the outcomes of presidential elections, since some candidates could be featured more prominently in its search results.
Research by the American Institute for Behavioral Research and Technology has also shown that Google search results could shift voting preferences by 20% or more (up to 80% in certain demographic groups). This could potentially flip the margins of voting elections worldwide. But since Google's private algorithm is a core part of its competitive advantage, open-sourcing it is not likely to be an option.
The same problem applies to the algorithms used in DNA testing, breathalyzer tests and facial recognition software. Many defense attorneys have requested access to the source code for these tools to verify the algorithms' accuracy. But in many cases, these requests are denied, since the companies that produce the proprietary criminal justice algorithms fear a threat to their businesses' bottom line. Yet clearly we need some way to ensure the accuracy of software that could put people behind bars.
What we can learn from the FDA
So how exactly could software take a regulatory page from the FDA in the United States? Before the 20th century, the government made several attempts to regulate food and medicine, but abuse within the system was still rampant. Food contamination caused widespread illness and death, particularly within the meatpacking industry.
Meanwhile, the rise of new medicines and vaccines promised to eradicate diseases, including smallpox. But for every innovation, there seemed to be an equal amount of extortion by companies making false medical claims or failing to disclose ingredients. The reporting of journalists like Upton Sinclair made it abundantly clear by the early 1900s that the government needed to intervene to protect people and establish quality standards.
In 1906, President Theodore Roosevelt signed the Food and Drug Act into law, which prevented false advertising claims, set sanitation standards, and served as a watchdog for companies that could cause harm to consumers' welfare. These first rules and regulations served as a foundation for our modern-day FDA, which is critical to ensuring that products are safe for consumers.
The FDA could be a good baseline model for software regulation in the US and countries around the world, which have parallel FDA organizations including the European Medicines Agency, Health Canada, and the China Food and Drug Administration.
Just as the FDA ensures that major pharmaceutical companies aren't lying about the claims they make for drugs, there should be a similar regulator for software to ensure that car companies are not cheating customers and destroying the environment in the process. And just as companies need to disclose food ingredients to prevent people from ingesting poison, companies like Google should be required to provide some level of guarantee that they won't intentionally manipulate search results that could shape public opinion.
It's still relatively early days when it comes to discovering the true impact of algorithms in consumers' lives. But we should establish standards to prevent abuse sooner rather than later. With technology already affecting society on a large scale, we need to address emerging ethical issues head-on.
(I originally wrote this blog post as a guest article for Quartz.)
The Industrial Revolution, started in the middle of the 18th century, transformed the world. It marks the start of a major turning point in history that would influence almost every aspect of daily life. The Industrial Revolution meant the shift from handmade to machine-made products and increased productivity and capacity. Technological change also enabled the growth of capitalism. Factory owners and others who controlled the means of production rapidly became very rich and working conditions in the factories were often less than satisfactory. It wasn't until the 20th century, 150 years after its beginning, that the Industrial Revolution ended creating a much higher standard of living than had ever been known in the pre-industrial world. Consumers benefited from falling prices for clothing and household goods. The impact on natural resources, public health, energy, medicine, housing and sanitation meant that chronic hunger, famines and malnutrition started to disappear and the life expectancy started to increase dramatically.
An undesired side-effect of the Industrial Revolution is that instead of utilizing artisans to produce hand-made items, machines started to take the place of the artisans. Before the industrial revolution, custom-made goods and services were the norm. The one-on-one relationships that guilds had with their customers sadly got lost in an era of mass-production. But what is exciting me about the world today is that we're on the verge of being able to bring back one-on-one relationships with our customers, while maintaining increased productivity and capacity.
As the Big Reverse of the Web plays out and information and services are starting to come to us, we'll see the rise of a new trend I call "B2One". We're starting to hear a lot of buzz around personalization, as evidenced by companies like The New York Times making delivery of personalized content a core part of their business strategy. Another recent example is Facebook testing shopping concepts, letting users browse a personal feed of clothing and other items based on their "likes". I'd imagine these types of feeds could get smarter and smarter, refining themselves over time as a user browses or buys. Or just yesterday, Facebook launched Notify, an iOS app that pushes you personalized notifications from up to 70 sites.
These recent examples are early signs of how we're evolving from B2C to B2One (or from B2B2C to B2B2One), a world where all companies have a one-on-one relationship with their customers and personalized experiences will become the norm. Advances in technology allow us to get back what we lost hundreds of years ago in the Industrial Revolution, which in turn enables the world to innovate on business models. The B2One paradigm will be a very dramatic shift that disrupts existing business models (advertising, search engines, online and offline retailers) and every single industry.
For example, an athletic apparel company such as Nike could work sensor technology into its shoes, telling you once you've run a certain number of miles and worn them out. Nike would have enough of a one-on-one relationship with you to push an alert to your smartphone or smartwatch with a "buy" button for new shoes, before you even knew you needed them. This interaction is a win-win for both you and Nike; you don't need to re-enter your sizing and information into a website, and Nike gets a sale directly from you disrupting both the traditional and online retail supply chain (basically, this is bad news for intermediaries like Amazon, Zappos, clothing malls, Google, etc).
I believe strongly in the need for data-driven personalization to create smarter, pro-active digital experiences that bring back one-on-one relationships between producers and consumers. We have to dramatically improve delivering these personal one-on-one interactions. It means we have to get better at understanding the user's journey, the user's context, matching the right information/service to the user and making technology disappear in the background.
Algorithms are shaping what we see and think -- even what our futures hold. The order of Google's search results, the people Twitter recommends us to follow, or the way Facebook filters our newsfeed can impact our perception of the world and drive our actions. But think about it: we have very little insight into how these algorithms work or what data is used. Given that algorithms guide much of our lives, how do we know that they don't have a bias, withhold information, or have bugs with negative consequences on individuals or society? This is a problem that we aren't talking about enough, and that we have to address in the next decade.
Open Sourcing software quality
In the past several weeks, Volkswagen's emissions crisis has raised new concerns around "cheating algorithms" and the overall need to validate the trustworthiness of companies. One of the many suggestions to solve this problem was to open-source the software around emissions and automobile safety testing (Dave Bollier's post about the dangers of proprietary software is particularly good). While open-sourcing alone will not fix software's accountability problems, it's certainly a good start.
As self-driving cars emerge, checks and balances on software quality will become even more important. Companies like Google and Tesla are the benchmarks of this next wave of automotive innovation, but all it will take is one safety incident to intensify the pressure on software versus human-driven cars. The idea of "autonomous things" has ignited a huge discussion around regulating artificially intelligent algorithms. Elon Musk went as far as stating that artificial intelligence is our biggest existential threat and donated millions to make artificial intelligence safer.
While making important algorithms available as Open Source does not guarantee security, it can only make the software more secure, not less. As Eric S. Raymond famously stated "given enough eyeballs, all bugs are shallow". When more people look at code, mistakes are corrected faster, and software gets stronger and more secure.
Less "Secret Sauce" please
Automobiles aside, there is possibly a larger scale, hidden controversy brewing on the web. Proprietary algorithms and data are big revenue generators for companies like Facebook and Google, whose services are used by billions of internet users around the world. With that type of reach, there is big potential for manipulation -- whether intentional or not.
There are many examples as to why. Recently Politico reported on Google's ability to influence presidential elections. Google can build bias into the results returned by its search engine, simply by tweaking its algorithm. As a result, certain candidates can display more prominently than others in search results. Research has shown that Google can shift voting preferences by 20 percent or more (up to 80 percent in certain groups), and potentially flip the margins of voting elections worldwide. The scary part is that none of these voters know what is happening.
And, when Facebook's 2014 "emotional contagion" mood manipulation study was exposed, people were outraged at the thought of being tested at the mercy of a secret algorithm. Researchers manipulated the news feeds of 689,003 users to see if more negative-appearing news led to an increase in negative posts (it did). Although the experiment was found to comply with the terms of service of Facebook's user agreement, there was a tremendous outcry around the ethics of manipulating people's moods with an algorithm.
In theory, providing greater transparency into algorithms using an Open Source approach could avoid a crisis. However, in practice, it's not very likely this shift will happen, since these companies profit from the use of these algorithms. A middle ground might be allowing regulatory organizations to periodically check the effects of these algorithms to determine whether they're causing society harm. It's not crazy to imagine that governments will require organizations to give others access to key parts of their data and algorithms.
Ethical early days
The explosion of software and data can either have horribly negative effects, or transformative positive effects. The key to the ethical use of algorithms is providing consumers, academics, governments and other organizations access to data and source code so they can study how and why their data is used, and why it matters. This could mean that despite the huge success and impact of Open Source and Open Data, we're still in the early days. There are few things about which I'm more convinced.
It's hard to ignore the strong force of digital distributors on the open web. In a previous post, I focused on three different scenarios for the future of the open web. In two of the three scenarios, the digital distributors are the primary way for people to discover news, giving them an extraordinary amount of control.
When a small number of intermediary distributors get between producers and consumers, history shows us we have to worry -- and brace ourselves for big industry-wide changes. A similar supply chain disruption has happened in the food industry, where distributors (supermarkets) changed the balance of power and control for producers (farmers). There are a few key lessons we can take from the food industry's history and apply them to the world of the web.
Historical lessons from the food industry
When food producers and farmers learned that selling directly to consumers had limited reach, trading posts emerged and began selling farmers' products more than their own. As early as the 14th century, these trading posts turned into general stores, and in the 18th century, supermarkets and large-scale grocery stores continued that trend in retailing. The adoption of supermarkets was customer-driven; customers benefit from being able to buy all their food and household products in a single store. Today, it is certainly hard to imagine going to a dozen different speciality stores to buy everything for your day-to-day needs.
But as a result, very few farmers sell straight to consumers, and a relatively small amount of supermarkets stand between thousands of suppliers and millions of consumers. The supermarkets have most of the power; they control how products are displayed, and which ones gain prominent placement on shelves. They have been accused of squeezing prices to farmers, forcing many out of business. Supermarkets can also sell products at lower prices than traditional corner shops, leading to smaller grocery stores closing.
In the web's case, digital distributors are the grocery stores and farmers are content producers. Just like supermarket consumers, web users are flocking to the convenience and relevance of digital distributors.
Control of experience
Web developers and designers devote a tremendous amount of time and attention to creating beautiful experiences on their websites. One of my favorites was the Sochi Olympics interactive stories that The New York Times created.
That type of experience is currently lost on digital distributors where everything looks the same. Much like a food distributor's products are branded on the shelves of a supermarket to stand out, publishers need clear ways to distinguish their brands on the “shelves” of various digital distribution platforms.
While standards like RSS and Atom have been extended to include more functionality (e.g. Flipboard feeds), there is still a long way to go to support rich, interactive experiences within digital distributors. As an industry, we have to develop and deploy richer standards to "transport" our brand identity and user experience from the producer to digital distributor. I suspect that when Apple unveils its Apple News Format, it will come with more advanced features, forcing others like Flipboard to follow suit.
Control of access / competition
In China, WeChat is a digital distributor of different services with more than 0.5 billion active users. Recently, WeChat blocked the use of Uber. The blocking comes shortly after Tencent, WeChat's parent company, invested in rival domestic car services provider Didi Kuaidi. The shutdown of Uber is an example of how digital distributors can use their market power to favor their own businesses and undermine competitors. In the food industry, supermarkets have realized that it is more profitable to exclude independent brands in favor of launching their own launching grocery brands.
Control of curation
Digital distributors have an enormous amount of power through their ability to manipulate curation algorithms. This type of control is not only bad for the content producers, but could be bad for society as a whole. A recent study found that the effects of search engine manipulation could pose a threat to democracy. In fact, Google rankings may have been a deciding factor in the 2014 elections in India, one of the largest elections in the world. To quote the study's author: "search rankings could boost the proportion of people favoring any candidate by more than 20 percent -- more than 60 percent in some demographic groups". By manipulating its search results, Google could decide the U.S. presidential election. Given that Google keeps its curation algorithms secret, we don't know if we are being manipulated or not.
Control of monetization
Finally, a last major fear is control of monetization. Like supermarkets, digital distributors have a lot of control over pricing. Individual web publishers must maintain their high quality standards to keep consumers demanding their work. Then, there is some degree of leverage to work into the business model negotiation with digital distributors. For example, perhaps The New York Times could offer a limited-run exclusive within a distribution platform like Facebook's Instant Articles or Flipboard.
I'm also interested to see what shapes up with Apple's content blocking in iOS9. I believe that as an unexpected consequence, content blocking will place even more power and control over monetization into the hands of digital distributors, as publishers become less capable of monetizing their own sites.