By Kevin Frazier<\/p>\n
Something I think just about all of us agree on: We want better\u00a0AI<\/a>. Regardless of your current perspective on AI, it\u2019s undeniable that, like any other tool, it can unleash human flourishing. There\u2019s progress to be made with AI that we should all applaud and aim to make happen as soon as possible.<\/p>\n There are kids in rural communities who stand to benefit from AI tutors. There are visually impaired individuals who can more easily navigate the world with AI wearables. There are folks struggling with mental health issues who lack access to therapists who are in need of guidance during trying moments.<\/p>\n A key barrier to leveraging AI \u201cfor good\u201d is our imagination \u2014 because in many domains, we\u2019ve become accustomed to an unacceptable status quo. That\u2019s the real comparison. The alternative to AI isn\u2019t well-functioning systems that are efficiently and effectively operating for everyone.<\/p>\n Yet there\u2019s a justifiable sense that AI is falling short of its potential. An understandable response is to oppose further AI development efforts. Perhaps the thinking goes, \u201cWell, if it\u2019s this bad after this much money has been spent, then what\u2019s the point of burning even more resources on AI?\u201d<\/p>\n But if any one of those prior examples had you nodding along \u2014 thinking, \u201cIt\u2019d sure be nice to improve education, make daily life more inclusive, or help folks through difficult mental moments\u201d \u2014 then you\u2019re already part of the coalition to make AI better. Let\u2019s make that our shared agenda.<\/p>\n That agenda calls for a few concrete actions. For starters, there\u2019s real value in using AI with an eye toward evaluating its ability to solve problems. We\u2019re not going to uncover AI\u2019s most productive use cases unless folks from a range of backgrounds test its application to new and complex problems.<\/p>\n If I were a gambling man, I\u2019d wager that the main uses of AI five years from now will be wildly different from those today. That process can be expedited by empowering more people to thoughtfully and loudly experiment with AI. Loudly means that when AI goes well or goes wrong, users share that outcome. And when sharing that outcome, be specific. Vague frustration doesn\u2019t move anyone. Name the exact barriers, regulations, and assumptions standing in the way of a more prosperous and just world.<\/p>\n Specific complaints are only useful, though, if they enter a broader conversation \u2014 and that requires honesty about how we\u2019re actually using these tools. As Ethan Mollick and others have observed, there are \u201csecret cyborgs\u201d out there who are hesitant to share the fact that they\u2019re using AI. This is a net negative behavior. It hinders the open dialogue necessary for an evidence-driven approach to collectively deciding when and how to use AI. Folks should not be ashamed of using AI but rather celebrated for testing how a new tool can solve old problems.<\/p>\n Disclosure is the floor. The ceiling is something more ambitious: deciding, together, what good AI use actually looks like. Here\u2019s what nobody in the AI debate is saying: We don\u2019t need the government to set AI norms, and we shouldn\u2019t trust the labs to do it either. We need each other \u2014 and we need infrastructure to make that possible.<\/p>\n Think of a searchable, community-built platform \u2014 an AI Policy Commons \u2014 where anyone can propose an AI usage policy for a specific context: a parent submits a prompt designed to help her kid learn without outsourcing thinking; a faith community posts guidelines for using AI in pastoral care; a teachers\u2019 union publishes a vetted set of classroom norms. Others find those policies, test them, and report back.<\/p>\n Over time, reputational signals emerge \u2014 you can filter by what your church endorses, what a leading education organization has field-tested, or what other parents in similar situations have rated most effective. No state-based mandate. No excessive control by a private actor. Just distributed experimentation producing something neither a legislature nor a lab could generate: norms with real-world legitimacy. Civil society has always been where culture gets made. The AI Policy Commons is how we do that for this moment.<\/p>\n None of this works, however, without better inputs. Cultural norms shape how we use AI; data determines what AI can do. We will not have better AI until we have better data. Americans should be able to access and direct their data \u2014 donating health data to AI researchers working on new cures, allowing a child\u2019s educational data to go to a startup working on tools for kids with similar learning challenges. Data should not be regarded as a liability to be minimized. It\u2019s the raw material of the AI we actually want. We can and should examine how to bring about that future.<\/p>\n If you\u2019re not satisfied with today\u2019s AI, do something about it. The people who will shape what this technology becomes are the ones willing to use it, critique it, and demand the conditions that make it better.<\/p>\n Kevin Frazier<\/a>\u00a0is a Senior Fellow at the Abundance Institute, directs the AI Innovation and Law Program at the University of Texas School of Law.<\/p>\n To send a letter to the editor about this article, submit online<\/a> or check out our guidelines<\/a> for how to submit by email or mail.<\/p>\n","protected":false},"excerpt":{"rendered":"By Kevin Frazier Something I think just about all of us agree on: We want better\u00a0AI. 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