Colorado can lead on AI fairness without a regulatory straitjacket

Colorado Gov. Jared Polis has called lawmakers back to Denver for a rare special session, partly to revisit Colorado’s first-in-the-nation artificial intelligence (AI) law. The special session kicked off on August 21st. While the 2024 statute aimed to curb algorithmic bias in hiring, lending, and other high-stakes areas, Polis now warns its broad mandates could create costly compliance hurdles and discourage innovation.

Instead of scrapping the goal of AI algorithmic fairness, Colorado has an opportunity to lead the country in developing anti-discrimination tools and testing what works without erecting barriers that lock out smaller players or slow emerging markets. The state can do this through partnerships with industry, universities, and nonprofits to first study whether AI discrimination is actually occurring and then pilot new technologies or algorithms that could reduce it.

As background, in 2024, the Colorado Legislature passed Senate Bill 24-205. It became the nation’s first state to enact comprehensive AI regulation tailored to high-stakes automated decisions in areas like employment, housing, education, and healthcare. The law mandates bias risk assessments, transparency disclosures, and consumer recourse mechanisms for systems that significantly influence life-changing outcomes.

Unfortunately, there could be extraordinary compliance costs. Companies may simply avoid developing or using services in the state rather than figure out how to comply with a complicated and potentially costly law. Polis, though supportive of the law’s intent, warned in his signing statement that a patchwork of state laws could stifle innovation and create a “challenging regulatory environment.”

In an X post on Aug. 19, the governor reiterated his concerns:

“In Colorado, we can promote innovation while also protecting consumers. …There is clear motivation in the legislature to take action now to protect consumers and promote innovation, all without creating new costs for the state or unworkable burdens for Colorado businesses and local governments.”

Moreover, a new White House’s AI Action Plan now adds a risk factor Colorado didn’t have to consider when SB 24-205 was passed: losing federal contracts. The AI Action Plan states that “the Federal government should not allow AI-related Federal funding to be directed toward states with burdensome AI regulations that waste these funds.”  For instance, the National Science Foundation has a $100M grant to research institutes in various states, such as the University of Texas at Austin.

While agencies are only beginning to implement this guidance and no interpretations have been issued, it is possible that a future agency could deem Colorado’s anti-discrimination mandates overly burdensome and threaten federal grants or contracts—introducing a funding risk that was not on the table when the legislature first enacted the law.

There are evidence-based, market-oriented steps Colorado lawmakers could take in place of the existing law.

Before discussing novel approaches to artificial intelligence, it is worth noting that Reason Foundation has questioned the need for additional anti-discrimination laws to address these types of AI issues. There are, for instance, already laws against racial discrimination in lending for housing. However, as policymakers convene, the majority may insist that the state go above and beyond existing laws and create additional actions related to artificial intelligence technologies.

One option Colorado could explore is the potential launch of a task force bringing together industry leaders, local universities, and nonprofits to study how discrimination in housing, education, and employment may be occurring when AI tools are used. Right now, policymakers and the public don’t have a clear picture of the scope of the problem: which applications are driving biased outcomes, whether the bias stems from generalized models or from specific software deployments, and in what contexts it most affects individuals. Without this baseline understanding, it’s impossible to design targeted, effective interventions.

Task force findings should be published in a public report and delivered to the legislature, giving both lawmakers and stakeholders an evidence base for future debates. By focusing first on identifying the degree and sources of bias, Colorado can replace guesswork with data, ensuring any eventual rules are grounded in measurable harm rather than hypothetical risks. This approach would also signal to the broader market that the state is committed to problem-solving, not preemptive overregulation.

Following the creation of a task force, the state could develop solutions to reduce AI discrimination. Once the task force has mapped where and how AI-driven discrimination occurs, its next goal should be to experiment with ways to mitigate it. Because AI models and applications are evolving at a pace of months, not years, there is no static playbook for reducing bias.

The task force should work with model developers, deployers, and academic experts to create algorithms, prompt strategies, or operational guidelines aimed at identifying and reducing discriminatory outcomes in real-world contexts. While the government could offer grants, many academics are already working on this problem. For instance, last year, a University of Colorado Boulder faculty member published research on biases in AI mental health tools.

These efforts should be paired with clear, measurable benchmarks. AI company Anthropic has evaluated benchmarks, such as the Bias Benchmark for QA, to ensure that its models do not perpetuate stereotypes (such as a model assuming that the CEO of a company is male). By testing models and applications against metrics, Colorado researchers could not only assess the effectiveness of mitigation techniques but also create a repeatable standard for others to adopt. If successful, Colorado’s benchmarks could become a national model for innovation in AI fairness without the weight of one-size-fits-all mandates.

The final step is to ensure that all findings from these voluntary efforts are made public and inform future legislation. Regular reports should not only document progress on bias reduction but also flag where interventions are ineffective or counterproductive. This process will help lawmakers avoid locking in policies that can’t adapt to evolving technology and will keep the public informed about the trade-offs involved. By making transparency a norm, Colorado can encourage a culture of trust between industry, regulators, and citizens.

This is especially helpful for smaller technology companies, which cannot afford entire teams dedicated to developing new AI methods that avoid discrimination. Public research and open-source tools, including public benchmarks, make it easier for smaller companies to comply with new rules. Both the supporters of Colorado’s AI law and Polis raise valid concerns over compliance costs. The state need not surrender its role in addressing AI-driven discrimination, nor should it ignore the risks of imposing rules that make Colorado less attractive to innovators. By adopting an exploratory, science-driven approach that works in partnership with the private sector, Colorado can preserve its leadership in addressing legitimate fairness issues while keeping its economy open and competitive.

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Source: https://reason.org/commentary/colorado-can-lead-on-ai-fairness-without-a-regulatory-straitjacket/