The Data Revolution: 6 Societal Problems and Solutions
Recently, I was invited to speak at South Summit’s Future World Session: The Data Revolution panel and to share my thoughts on global opportunities and challenges linked to, you guessed it, big data. As part of my preparation, I brushed-up on my data knowledge, as it relates to business, government, and society; and, set out to reflect on the following challenges: a decrease of competition and rise of monopolies of data; collusive pricing based on data; difficulty in governing data; data, as it relates to, privacy and social inequality; and finally artificial intelligence (AI). As I went through each topic, I noted six problems along with potential solutions.
Below, I share my initial thoughts in a problem-solution format in no particular order. Please share your feedback at the end of the post.
Problem: Privacy
The privacy of consumers is at stake. There are many consumers who do not read the opt-ins and are unaware that they are giving away their personal data. This lack of privacy terms understanding is a problem.
Solution: Personal Data Bank
Raising awareness is step one. From there a potential solution is to, as proposed by the World Economic Forum, create data banks. That is, every person can have an account, with her or his data and track it. This way, the people can be given back the control of their privacy and their data.
Problem: Monopolies of Data and Decreased Competition
Amazon knows our preferences by the purchases we make and ratings we give. Google knows us better than we do by the directions we get on Google Maps, the emails we send via Gmail, the calendar invites we send/receive, and the searches/purchases we make off Google Search. Facebook knows us by the personal information we’ve filled out in our profiles and the photos and messages we’ve shared. With all that data, Facebook, Amazon, and Google hold a monopoly of data and through that an overwhelming amount of power in the market.
This is a problem because monopolies lead to decreased competition which, in turn, leaves new entrants and consumers powerless. Plus, monopolies are illegal. What makes the situation worse is that due to lagging regulations, governance bodies do not recognize data monopolies as infringement on antitrust laws.
Solution: Data Monopoly Regulation
A potential solution to obstruct data monopolies is to have merger and acquisition regulation around data assets. That means that governance bodies responsible to oversee mergers and acquisitions, not only take into account market share percentages but also consider the dynamics of data assets.
Problem: AI Creates Products and Services that Lack Fungibility
As The Economist puts it: AI “services include translation, visual recognition and assessing someone’s personality by sifting through their writings — all of which can be sold to other firms to use in their own products.” Big data turns AI algorithms into products and services that are infungible — not easily comparable, trackable or measurable.
Solution: Govern New Asset Classes
The solution to infungible products and services is to acknowledge that we’re working with a new asset classes. Governance bodies need to work to classify and define what encompasses the new asset class. Then, it needs to regulate them to ensure consumer protection and the promotion of healthy competition in the market.
Problem: Tacit Collusion via Algorithms
Data can fuel algorithms that set prices. That seems harmless until you consider that companies are using the tech as a tool of collusion. Collusive companies are starting to manipulate markets by using algorithms to quickly react to competitor prices and draw in customers before they can react. Read about a real data collusion case here.
Solution: Anti-Price Collusion Algorithms and Data Sharing
One way of addressing algorithmic tacit collusion is to have governance bodies patrol collusion inducing algorithms with anti-price collusion algorithms and intelligent bots. Yet another way of addressing algorithmic driven collusion, is to do as the Swiss and Germans do: demand that data be shared publicly. If all have access to the same data, then everyone can compete “fairly.” Read more about Switzerland’s and Germany’s data sharing initiatives here.
Problem: Social Inequality
Jaron Lanier, author of “Who Owns the Future?,” points out that some will benefit far more than others in the data revolution. He says “data refineries will make all the money.” The problem here is that individuals will be promised “free services” by a select few companies who then will turn around and reap immense financial benefits. The leaders of the few data giants make a ton of money, on the backs of their “customers,” and these same “customers” get no financial compensation.
Solution: Get People to Understand the Power of their Data
Again, raising awareness is step one. People need to understand that the personal data is being mined by algorithms and turned into products and services; while they get no compensation. People need to understand the value of their data and demand compensation.
Problem: The Data Revolution and AI Can be Good and Bad
It’s true that the data revolution and faster/cheaper tech is enabling AI. And AI can be good and bad. In this post, we’ve spent a lot of time pointing out challenges but there are benefits that come from data and AI. Read this post to discover three ways AI is good for society.
Nevertheless, there is no denying that AI can be bad. Perhaps, AI won’t bring about an apocalypse as Elon Musk predicts, but it will lead to job automation, job loss and societal shifts that put many at a disadvantage. It’s a matter of time.
Solution: People to Shape the Data Revolution and Raise AI
The solution to the data revolution is that AI can be shaped and raised by businesses, government and society. Business can drive positive outcomes by choosing how they execute on data/AI business models and join organizations like the Partnership on Artificial Intelligence. Investors can do the same by choosing their company investments wisely. Governments can keep up with technology and regulations — like Germany and Switzerland. Society can choose to shape AI by learning more about big data/AI and then going to advocate for the kind of world they want to live in — be it through activism, non-profits (such as OpenAI), or their line of work.
In conclusion, it’s important to note that the data revolution is a responsibility that we all hold, as individuals, members of a workplace, and society. Note that there is an urgency to make sure that data and AI be used for good and all harm is minimized. Tech will continue to evolve the way we live, work, and play. What are you going to do about it? I suggest you do your part in shaping the data revolution and raising AI.
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Join me at South Summit’s Future World Session on The Data Revolution October 4–6, in Madrid! I invite you to join in on identifying and addressing the opportunities/challenges linked to the data revolution.
To my IE peers: Make sure to RSVP to South Summit here and take advantage of the free Learner’s tickets, 30% discount for Disruptor, Business and Premium tickets and volunteering opportunities.
