We’re excited to bring back Transform 2022 in person on July 19 and virtually from July 20-28. Join leaders in AI and data for in-depth discussions and exciting networking opportunities. Register today!
Automation, hybrid work models, the human cloud, the metaverse, and a more flexible and collaborative work environment are just a few trends shaping the 21st century office. Another trend that isn’t getting the attention it deserves for its innovativeness: Decentralized Autonomous Organizations, or DAOs.
DAOs are businesses built entirely from coded rules and decisions. As the term suggests, being self-contained means that the entity operates almost entirely without human intervention, but it can serve as an extension of a traditional limited liability company. In other words, people, often aligned in a common interest and without a single leader, collectively own the company and work together, often across borders, within the project and its platform. All the administrative responsibilities of a DAO are in the capable hands of blockchain technology.
If, for example, the DAO accomplishes a particular goal, the smart contracts codified in blockchain technology applications would themselves execute another directive. Essentially, these smart contracts define the rules – set by DAO members and available for review via the DAO blockchain – to automate the business’ operational process.
The future of these organizations looks so bright that companies have started popping up to fuel their growth with supporting infrastructure. An 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. The transparency, accountability, and efficiency of smart contracts can offer insights into developing and implementing data governance guidelines for AI-enabled systems.
The need for data governance of AI-driven systems
AI-driven systems have moved well beyond automating mundane, repetitive tasks with little or no supervision or instruction. Now companies are using data-driven AI models to accurately predict behavior and shorten innovation cycles, bringing new products and services to market faster without sacrificing quality. AI allows companies to more effectively monitor systems for patterns and anomalies with uninterrupted attention span. If a system detects an irregularity, a company can take quick 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 vital role, as data serves as a strategic centerpiece to generate new business opportunities. Some companies simply repackage and sell data, others use the insights to direct and guide improvements, and others offer AI-powered data catalog solutions to catalog and make sense of data from from disparate sources. Regardless of a company’s situation, data is a commodity.
As such, data governance is an absolute necessity. And the unique structure of DAOs can be instructive in guiding the governance of AI-driven systems. Community decisions, not the motivations of central decision-making authorities, drive DAOs. Similarly, the entire client-company relationship could be reorganized to ensure data governance.
DAO: Developing Better Data Governance Policies
By communicating with and involving customers when using personal data to create services, Contributors understand how information flows through each node and process. Collaboration makes the relationship closer and more iterative. It also encourages the data collection process and enables three of the most important guidelines:
1. Consumer-driven product development
If AI-based systems continue to inform product development using the current tracking-based data model, consumers are limited to choosing products that result from monitoring and interpreting their data and of their behaviors. Conversely, DAO product decisions are user-driven from the start, allowing 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 are zooming in and out of project orbits, lending capacity at a dizzying rate compared to traditional service lives. This accelerates the innovation cycle and continuously refines existing products or services with new features as they emerge.
In a DAO, contributors vote on the direction of projects, creating a feedback loop that currently does not exist in AI-driven systems. Rather than just turning complexity into simplicity, asking humans if they accept internal decisions, DAOs bend the arc of data models toward community centrality.
However, DAO guidelines are not without pitfalls. Although distributed, DAOs can still be subject to bias, risk, and manipulation. Take the Maker Platform, for example. It uses a member-voting DAO framework to guide protocol development. Anyone can invest in voting power with MKR tokens, a decentralized exchange. However, those who have invested the most MKR tokens have more influence because their votes are more weighted. There is therefore a potential for “authority” in decision-making. While the community is still small, bad actors could destroy still-nascent governance structures.
We are in the first chapter of a story that weaves a winding path through pitfalls and setbacks on its way to a paradigm shift. DAOs and AI systems will need to be audited and regulated to enable their successful journey along the way.
Dan Conner is the general partner of Rise of venture capital.
Welcome to the VentureBeat community!
DataDecisionMakers is where experts, including data technicians, can share data insights and innovations.
If you want to learn more about cutting-edge insights and up-to-date information, best practices, and the future of data and data technology, join us at DataDecisionMakers.
You might even consider writing your own article!
Learn more about DataDecisionMakers