Amid pitched discussions of whether artificial intelligence–powered technologies will one day transform art, journalism, and work, a more immediate impact of AI is drawing less attention: The technology has already helped create barriers for people seeking to access our nation’s social safety net.

Across the country, state and local governments have turned to algorithmic tools to automate decisions about who gets critical assistance. In principle, the move makes sense: At their best, these technologies can help long-understaffed and underfunded agencies quickly process huge amounts of data and respond with greater speed to the needs of constituents.

But careless—or intentionally tightfisted and punitive—design and implementation of these new high-tech tools have often prevented benefits from reaching the people they’re intended to help.

In 2016, during an attempt to automate eligibility processes, Indiana denied one million public assistance applications in three years—a 54 percent increase. Those who lost benefits included a six-year-old with cerebral palsy and a woman who missed an appointment with her case worker because she was in the hospital with terminal cancer. That same year, an Arkansas assessment algorithm cut thousands of Medicaid-funded home health care hours from people with disabilities. And in Michigan, a new automated system for detecting fraud in unemployment insurance claims identified fivefold more fraud compared to the older system—causing some 40,000 people to be wrongly accused of unemployment insurance fraud.