With the introduction of Chatbot/GPT to the public, artificial intelligence, like every other technology introduced since the beginning of the 20th century, is making some people nervous.

• When Henry Ford introduced the Model T to America his "new-fangled" gadget frightened both the cows in the field and the farmers who put them there. But the urban population embraced the automobile (horses in big cities were a messy inconvenience) and after only ten years there wasn’t a horse to be found above the Bowery.
• The Ban the Bomb movement exploded across the country only a few short months after we dropped a hydrogen bomb on both Hiroshima and Nagasaki. We were not ready for nuclear technology but once it appeared it refused to disappear.
• Computer technology, the internet, and cell phones have all dramatically altered the way we live our lives. With some bumps along the road our culture is still adapting to this rapid advance in information technology. Artificial intelligence is, apparently, just one more step along that road.

Artificial Intelligence

In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text can learn to generate lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.

AI in Social Work

Given the increasing number of studies employing the relatively new tools of data-driven technology, the social work profession is aggressively researching its potential benefits.

• One key goal is the integration of new technology into social work and the building of capacity to use powerful digital resources to identify and implement solutions to pressing social problems.
• Social work researchers are already using AI and natural language processing to detect bias in social media and to develop culturally sensitive algorithms for violence detection and prevention.
• Other researchers are merging social work and AI to enhance HIV prevention programs for homeless youth, while others are working on predictive risk modeling to help child welfare agencies identify risks for child abuse and maltreatment.
• Growing engagement with such tools as Big Data, algorithms, and AI also is reflected in the use of large administrative data sets from public assistance, employment, and social service records. The data gathered from these sources can help the profession better understand how specific policy elements affect program participants.
• Some social workers are already using natural language processing, a text-mining technique that translates narrative text into structured data with an algorithm to analyze clinical visit notes.

Concerns

However, there’s still a fair amount of concern about these tools, and some social workers are focused on addressing the most disturbing aspects, such as the inherent biases that can be found in Big Data.

• AI and other data science fields lack adequate representation of Black, Indigenous, People of Color (BIPOC), women, and other marginalized groups, undermining their ability to address issues of fairness, equity, and justice.
• Significant concerns exist about the potential for inherent data biases to perpetuate bias in child welfare, banking and lending, law enforcement, and health and wellness. The sources of bias in AI are data sets that underrepresent or misrepresent phenomena related to race and gender, as well as algorithms and research methods that amplify biased data.
• Experts are also calling for greater interdisciplinary work in the development and use of AI, especially by social scientists who study the social foundations in which AI operates and engage with marginalized communities most at risk of harm from AI. BIPOC researchers in AI are critical of racism and sexism in facial analysis models, for example, arguing for greater diversity in the field and transparency in AI research and data sharing standards.

AI is Here to Stay

Social service professionals are right to be concerned about the use of AI in the early stages. But like nuclear technology, AI is here to stay and learning how to best utilize this new tool is the way to go.

• At the Silver School of Social Work at New York University, Victoria Stanhope, PhD, MSW is utilizing an AI approach to examine collaborative documentation, a strategy in which behavioral health clinicians complete visit notes jointly with consumers during the session. Stanhope’s study is using natural language processing, a text-mining technique that translates narrative text into structured data with an algorithm to analyze clinical visit notes. It will contribute to the base of evidence on collaborative documentation and develop an algorithm to analyze person-centered care to inform quality improvement in behavioral health care.
• Michael Lindsey, PhD, MSW, MPH, has established an AI hub to help researchers investigate how AI-driven systems can be used to equitably address poverty and challenges related to race and public health, and to provide thought leadership on the implications.

There are many more examples of early work going on in the application of AI to social work that will hopefully put this powerful information technology to good use soothing everyone’s nerves.