DAOs could hold the answer to better data governance guidelines
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Automation, hybrid work models, the human cloud, the metaverse, and a more flexible and collaborative working environment are just a few trends shaping the office of the 21st century. Another such trend that doesn’t get the attention it deserves for its innovativeness: decentralized autonomous organizations, or DAOs.
DAOs are companies built entirely out of coded rules and decisions. As the term would suggest, being autonomous means the entity runs almost entirely without human intervention, yet it can serve as an extension of a traditional limited liability company. In other words, people, often aligned in a common interest and with no single leader, collectively own the company and work together, often across borders, within the project and its platform. All of the administrative responsibilities for a DAO lie in the capable hands of blockchain technology.
If, for example, the DAO reaches a particular objective, smart contracts codified in blockchain technology applications would self-execute another directive. Essentially, these smart contracts define the rules — set by DAO members and available for review through the DAO’s blockchain — for automating the company’s operational process.
The future of these organizations looks so bright that businesses have started popping up to fuel their growth with supportive infrastructure. One example is Utopia Labs, a company in which I invest. Formed in late 2021, the organization is building an operating system to make DAOs even more efficient. AI-focused tech leaders today have a lot to learn from this momentum. Transparency, accountability, and efficiency in smart contracts can offer insights into shaping and instituting data governance guidelines for AI-driven systems.
The need for data governance of AI-driven systems
AI-driven systems have advanced well beyond the automation of mundane, repetitive tasks with little-to-no supervision or instruction. Now, companies are using data-driven AI models to predict behaviors accurately and shorten innovation cycles, helping bring new products and services to market faster without sacrificing quality. AI allows companies to monitor systems more effectively for patterns and anomalies with uninterrupted attention space. Should a system detect an irregularity, a company can take prompt action and reduce any potential risk to operations.
More importantly, many companies are becoming more data-centric in their business models. This is where data governance policies play a critical role because data serves as the strategic centerpiece for generating new business opportunities. Some companies simply repackage and sell data, others use the information to provide direction and guide improvements, and others offer AI-driven data catalog solutions to inventory and make sense of data from disparate sources. No matter where a company falls, data is a commodity.
As such, data governance is an absolute necessity. And the unique structure of DAOs can be informative in guiding how to govern AI-driven systems. Community decisions, not the motivations of central decision-making authorities, incentivize DAOs. Similarly, the whole customer-company relationship could be retooled to provide data governance.
DAOs: Building better data governance policies
By communicating with customers and involving them when using personal data to craft services, contributors understand how the information flows through every node and process. Collaboration makes the relationship closer and more iterative. It also incentivizes the data collection process and enables three of the most important guidelines:
1. Consumer-led product development
If AI-driven systems continue to inform product development using the current tracking-based data model, consumers are limited to choosing products that result from the monitoring and interpretation of their data and behaviors. Conversely, DAO product decisions are user-driven from the start, enabling users to intuit their own needs in a product and then inform design decisions specifically for those biases.
2. Continuous iteration
DAOs are constantly iterated. Contributors zoom in and out of projects’ orbits, lending capabilities at a dizzying pace compared to traditional employment lengths of service. This speeds the innovation cycle and continuously fine-tunes existing products or services with new capabilities as they emerge.
In a DAO, contributors vote on the direction of projects, creating a feedback loop that doesn’t currently exist in AI-driven systems. Rather than simply honing complexity into simplicity, by asking humans whether they accept the internal decisions, DAOs bend the arc of data models toward community centricity.
DAO guidelines aren’t without pitfalls, however. Though distributed, DAOs can still be subject to biases, risks, and manipulations. Take the Maker platform, for example. It uses a DAO framework of member voting to guide protocol development. Anyone can invest in voting power with MKR tokens, a decentralized exchange. However, those with the most MKR tokens invested do hold more influence, as their votes are weighted more heavily. So there is the potential for “authority” in decision-making. While the community is still small, bad actors could destroy the still-nascent governance structures.
We’re on the first chapter of a story that weaves a winding path through pitfalls and downsides on its way to paradigm change. Both DAOs and AI systems will need to be audited and regulated in ways that enable their successful journey along the way.
Dan Conner is the general partner at Ascend Venture Capital.
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