Imagine you live in a neighborhood that has long been under-resourced — "redlined" back in the days when such overt discrimination was both legal and encouraged by the federal government, and which has never fully recovered. And suppose your local government provides funding to support neighborhoods with everything from transit upgrades to rehabilitating dilapidated homes. You might expect your troubled neighborhood to be first in line for funding.

In more than two dozen U.S. cities, you could well be wrong. And it would be even more frustrating if you discovered that your neighborhood had been deprioritized not by a human official you could hold accountable but by an algorithm — an automated decision-making system that decided your community was a bad investment.

Yes, that has really happened.

Cities across the United States have begun using urban planning algorithms to classify neighborhoods by market strength and investment value, and then create tailored development plans for each — plans that determine which neighborhoods receive funding for services or infrastructure upgrades. But at least one widely used algorithm encourages users to prioritize investments and public subsidies in stronger, more prosperous markets before investing in weaker, distressed areas.

That is seen as a way to maximize return on investment for public dollars, but it can channel vitally needed funding away from the communities that need it most, typically those that had been subjected to both overt and covert discrimination. In Detroit, for example, city officials used a planning algorithm known as Market Value Analysis (MVA) to justify the reduction and disconnection of water and sewage utilities, plus withholding of federal, state and local redevelopment dollars, in the city's "weak markets," which happened to be its Blackest and poorest neighborhoods. In Indianapolis, MVA recommendations made small-business support, home repair and rehabilitation, homebuyer assistance, and foreclosure-prevention programs unavailable to the city's most distressed neighborhoods.

This illustrates a fundamental pitfall of algorithms, as well as the risks that they can be misused or produce unintended consequences. While the MVA was created to help revitalize distressed neighborhoods, it uses variables like average home prices, vacancy rates, foreclosures and homeownership to determine neighborhood "value," but those data points are neither ahistorical nor objective. Instead, they reflect a history of systemic bias. Redlining accounts for 30 percent of the gap in homeownership and 40 percent of the gap in home values for Black Americans between 1950 and 1980. Even today, maps of economically disadvantaged or under-resourced areas still bear a startling resemblance to the Federal Housing Administration's redlining maps from the 1930s. Algorithms can perpetuate or amplify long-standing human biases.

One major source of algorithmic bias can be found in the "training data" used to teach such a system to recognize patterns in bits of information. For example, if a Black or Latino neighborhood is overpoliced, leading to skewed arrest rates, a predictive-policing algorithm could "learn" that Blacks and Latinos are more likely to be criminals, when in fact they're just more likely to be arrested.

Often, the victims of algorithmic redlining don't know what happened to them, because information on algorithms and their use is generally not publicly available. California's Legislature is considering a step to begin to remedy this problem: If passed, AB 13 will bring transparency to the use of algorithms by state agencies and programs. For example, it would require a prospective contractor to submit an "automated decision system impact assessment" to evaluate the privacy and security risks to personal information as well as risks that could result in inaccurate, unfair, biased or discriminatory decisions impacting individuals.

That's an essential start, but America can do better. We can go from algorithmic redlining to algorithmic greenlining — using the powerful tools of artificial intelligence to promote equity and help close the nation's yawning racial wealth gap.

In the words of Cathy O'Neil, author of Weapons of Math Destruction, "Big Data processes codify the past. They do not invent the future. Doing that requires moral imagination, and that's something only humans can provide. We have to explicitly embed better values into our algorithms, creating Big Data models that follow our ethical lead."

California has modeled a first step with a tool known as CalEnviroScreen. A law that we at the Greenlining Institute helped pass, SB 535, prioritized funds from the state's cap-and-trade program for communities with the greatest economic and environmental challenges, and directed the state to create a scientific tool to decide which communities to prioritize. CalEnviroScreen, developed with extensive community consultation, examines multiple indicators such as unemployment rates and exposure to pollution. Based on this data, the algorithm outputs a CalEnviroScreen score that quantifies the environmental and socioeconomic burdens within a community and determines its eligibility for targeted investments.

CalEnviroScreen is a simple example of what's possible if we consciously put equity metrics into algorithms used to make complex decisions. Imagine how much further human creativity could take this idea if we try. Algorithmic greenlining can happen — if we have the will to do it.

Vinhcent Le is the technology equity legal counsel at the Greenlining Institute | | @VinhcentLe

Governing's opinion columns reflect the views of their authors and not necessarily those of Governing's editors or management.

Reader Response:

Vinhcent Le's characterization of the Reinvestment Fund's Market Value Analysis (MVA) offers an interesting perspective, but is inaccurate in some details.

The MVA is a data-based, field-validated, stakeholder-informed, geographically granular examination of the conditions in a community. Creating an MVA is a public process. Every MVA has a community stakeholder group that meets throughout the project to discuss the data, analysis, results and implications. These groups are demographically diverse and typically include representatives from local government, community-based organizations, real estate professionals and others.

The MVA is not an algorithmic black box that users have no sightline into; the variables that comprise the analysis are fully disclosed, as well as how those variables are statistically combined. Unlike credit scores or automated valuation models that the general public, impacted by those algorithms, cannot examine, the MVA centers transparency.

The MVA was created in 2001 for Philadelphia as part of a $300 million investment effort known as the Neighborhood Transformation Initiative (NTI) led by then-Mayor John Street. Among NTI's accomplishments: thousands of vacant lots cleaned, greened and fenced with post-and-rail fencing; abandoned homes demolished or rehabbed; below-market home-improvement loans for people with troubled credit; and access to city contracts for Black demolition contractors who were formerly unequal participants. Hardly MVA-based redlining. Similarly, Detroit's MVA helped guide resources to the city's most challenged neighborhoods. In a city that was 83 percent Black at the time, this did not mean preferencing a white neighborhood over a Black neighborhood. The Indianapolis neighborhood investment strategy used the MVA to provide a "toolkit for every neighborhood," matching all communities with an appropriate set of programs.

The MVA has also been instrumental in drawing attention to the "middle neighborhoods" in many cities — communities oftentimes neglected by local programs and investment. Yet, these are neighborhoods that are the living, breathing examples of racially integrated areas where people struggle to build a good life for themselves and their families, and could benefit from greater governmental attention.

Lastly, in most cities where MVAs are done, we analyze mortgage capital flows to different markets. Through these analyses, we have been able to identify, for example, that in St Louis 8 percent of home sales in distressed markets had Home Mortgage Disclosure Act-recorded mortgages (compared to over 95 percent in St Louis' strongest markets). The MVA can surface credit inequities; it does not cause them.

Communities across the country are using their MVAs to support land banking and equitable development strategies, fair housing assessments, Low-Income Housing Tax Credit preferences and a variety of other constructive purposes. We have had the pleasure and honor of working with government officials who are genuinely looking to make better use of the resources at their disposal to improve neighborhoods in their cities. The MVA is one valuable tool that they use in those efforts.

Ira Goldstein, President, Policy Solutions, Reinvestment Fund