Provides information on all things social work, including direct practice (both clinical and community organizing), research, policy, education... and everything in between.
[Episode 137] In today's Social Work Podcast, I speak with ChatGPT-4o about the role of AI in child welfare and community organizing and how we can use Bronfenbrenner's ecosystems framework and an antiracist perspective. That's right. I'm interviewing OpenAI's LLM, ChatGPT-4o.
On May 13th, 2024, OpenAI released the Omni version of ChatGPT4. This was a big step forward in processing speed. You could input video audio sort of visuals data all sorts of things and it could analyze it almost in real time. I've been playing around with ChatGpt-4, the earlier version, and you know talking to it on the app and there was always a significant delay as it was thinking through things and kind of coming up with a response. But the Omni version, the response is basically in real time, and I thought what better moment than now to interview ChatGPT, because you'll hear a little bit of the thinking, right? It’ll be a slight delay but no more than you would for any live human guests on the social work podcast.
Unlike a human guest, I didn't have to coordinate schedules. All I did was I took my phone, I clicked on the app, I hit the little headset icon which activated the voice input, and I held up my phone to my microphone so that I was recording my voice like I am now, and then ChatGPT just coming right out of my phone into the speaker. So, it's good audio. it's not rich deep audio like I like to have, but you know when you record over Zoom or Skype or any of the other things that I've done in the past again it's not pristine audio either, but it's pretty good
This is the first time I've interviewed a chatbot. I'd love to hear your thoughts about the interview. Please text me or leave a voicemail for me at (215) 948-2456. If you leave a voicemail, please let me know if you're ok with me including your message as part of a follow-up podcast episode.
Hey there podcast listeners, Jonathan here. We have an exciting episode for you today. Today's guest is ChatGPT-4o. In today's episode we're going to be talking about the role of AI in child welfare, community organizing, understanding Bronfenbrenner's ecosystems model and taking an anti-racist perspective. Now, none of this was planned. I came up with the questions on the spot. I'm recording this introduction afterwards so that you have an idea of what's going on.
Now, there's been a lot of conversation about generative AI. There’s been stuff in the movies for decades about the technology overlords taking over the world. About AI being smarter than humans. About AI taking jobs that social workers and other folks can do.
I want to explain just briefly what my understanding of AI is, particularly with ChatGPT. So, it's a large language model. It was computer programs that were trained on billions of words to identify patterns and responses. these billions of words are sometimes called bags of words. where the computer program learns what words are likely to come after other words, right? So ChatGPT, when it is coming up with responses it's literally predicting the words that are likely to come up next. and so, when you see chat GPT work in real time you see it coming up with words kind of in the same way that you would on a typewriter or computer as you're typing things out it's thinking in the same way that we do about what is going to come up next.
On May 13th, 2024, open AI released the Omni version of ChatGPT4. This was a big step forward in processing speed. You could input video audio sort of visuals data all sorts of things and it could analyze it almost in real time. I've been playing around with ChatGpt-4, the earlier version, and you know talking to it on the app and there was always a significant delay as it was thinking through things and kind of coming up with a response. But the Omni version, the response is basically in real time, and I thought what better moment than now to interview ChatGPT, because you'll hear a little bit of the thinking, right? It’ll be a slight delay but no more than you would for any live human guests on the social work podcast.
Unlike a human guest, I didn't have to coordinate schedules. all I did was I took my phone, I clicked on the app, I hit the little headset icon which activated the voice input, and I held up my phone to my microphone so that I was recording my voice like I am now, and then ChatGPT just coming right out of my phone into the speaker. So, it's good audio. it's not rich deep audio like I like to have, but you know when you record over Zoom or Skype or any of the other things that I've done in the past again it's not pristine audio either, but it's pretty good
As with any guests I prepped ChatGPT for the interview. I said:
Jonathan Singer: so I'd like you to be an expert in artificial intelligence and the use of AI in Social Work practice. Is that something that you can talk about? ChatGPT-4o: To discuss the intersection of AI and Social Work practice AI has a lot of potential in this field, from Predictive Analytics for case management to enhancing accessibility and providing support for mental health interventions. Where would you like to start the conversation?
So, I hope that you find this interesting. And if you don't find this interesting, I would also really like to know, what was it like to listen to AI answering questions about AI.
AI in some sense is the ultimate expert on AI, but it's also a machine, right? It's a computer algorithm. it's not something with intent. You can't hang out and have coffee with it afterwards, and so I understand that this experience might be different.
Deborah Lupton in 2017 talked about this concept of sociomaterialism, right, which is our experience interacting with material objects in the world. and increasingly we are interacting with AI in the world. and so, I think paying attention to our experience of that is really important. some of you might be excited about this and some of you might hate this, which totally fine. Like I said, I would really love to get some feedback - your impressions and experience is with this.
The last thing that I'd like to say about this is that, in my experience over the last year and a half of playing with generative AI, I found it to be really useful as a consultant.
So, there are things that I'm really good at and I find that AI is not as good as me doing those things. but there are other things that I'm not so good at and I find that AI is a really useful assistant, in that way. I also find that AI can be helpful as a consultant in the sense that I might ask it some questions and say "hey can I can I chat with you about this?" right? it's not meant to be a Google situation where you're just searching for one answer, but really to go back and forth and to build on the conversation, and so I think that you hear a little bit of that in this conversation as I say "okay well that's an interesting idea but what about this?" and then you get to hear what we think of as the "intelligence" in artificial intelligence as ChatGPT builds on what it said before in a novel way. It might not be novel in the sense that you've never thought about it but novel in the sense that it's not something that was pre-programmed into the computer to respond and so I’m really interested in your feedback about this episode.
This is the very first time that I have interviewed and AI chatbot as a guest on the social work podcast. I don't know if this is the future. the Australian podcaster Mim Fox and I have a book that's coming out in November of 2024 on Podcasting and Social Work Education where we think about a possible future where people are able to have AI come up with dialogues on the spot podcast episodes on the spot about specific information.
It reminds me that in 1993 William Gibson, on Fresh Air, famously said that "the future is here, it's just not evenly distributed." This might be a glimpse into the future. people might listen back to this episode in 10 years and say, "wow that that seems really archaic" and that that would be totally fine. I'm interested in your feedback. I'm interested in your thoughts on this, so why don't you give a call to my Google Voice number at 215-948-2456 and leave me a voicemail about your experience listening to this podcast episode. Or you can go on to Twitter or... sorry, X, or the Facebook page or anywhere where the social work podcast has a presence and leave me your thoughts. And if you leave a voicemail, let me know if I can use your voicemail in a follow-up episode about people's responses and reactions. Again, the numbers 215-948-2456.
And now without further ado, on to episode 137 of the Social Work Podcast: The role of AI in child welfare and community organizing: An interview with ChatGPT.
Interview
Jonathan Singer: Well, thank you so much for being here today on the Social Work Podcast.
The first question that I have for you is, what are some of the possible scenarios for social workers using AI technology for social work practice? And specifically, I'm wondering if you can give me one example with a lot of detail about how a social worker could use AI tools in a social work practice setting.
ChatGPT-4o: Thank you for having me! One detailed scenario where AI technology could be utilized in social work practice is in predictive analytics for child welfare services.
Imagine a social worker named Alex who is responsible for assessing the risk levels of children in potentially unsafe environments. Currently, Alex relies on a combination of interviews, past case records, and personal judgment to make these assessments. However, with the integration of AI, this process could be significantly enhanced.
AI tools can analyze vast amounts of data from various sources, including historical case data, school records, medical reports, and even social media activity, to identify patterns and risk factors that might not be immediately apparent to a human. For instance, an AI system could help Alex by providing a risk assessment score for each child based on this comprehensive data analysis. This score would highlight the likelihood of future incidents of neglect or abuse, allowing Alex to prioritize cases that need immediate attention.
Moreover, the AI system can continuously learn and improve its predictions by analyzing outcomes of past cases. If a particular intervention strategy led to positive outcomes in similar situations, the AI could suggest that strategy to Alex, thus supporting more informed decision-making.
In this scenario, Alex still plays a crucial role, using professional judgment to interpret the AI’s suggestions and make the final decisions. The AI serves as a powerful tool to enhance Alex's ability to identify high-risk cases more accurately and allocate resources more effectively, ultimately leading to better outcomes for vulnerable children.
Jonathan Singer: So that's really interesting because I think one of the criticisms of AI is that it has the potential and actually has been shown to target Black families...
ChatGPT-4o: You're right. One significant criticism of AI is that it can perpetuate or even exacerbate existing biases, particularly against marginalized communities. This happens because AI systems learn from historical data, which may contain biases reflecting systemic inequalities.
For example, if historical data used to train an AI system reflects biases in how certain communities are treated or assessed, the AI might inadvertently reinforce these biases. In child welfare, this could mean disproportionately higher risk scores for children from certain racial or socioeconomic backgrounds, not because they are inherently at higher risk, but because the data reflects historical over-surveillance or differential treatment of these groups.
To mitigate this, it’s crucial to implement several measures:
Bias Audits: Regularly auditing the AI systems to identify and correct biases. This involves scrutinizing the data, the algorithms, and the outcomes to ensure they do not disproportionately impact any particular group.
Diverse Data Sets: Ensuring the training data is as diverse and representative as possible. This helps the AI learn from a wide range of scenarios and reduces the likelihood of perpetuating bias.
Human Oversight: Maintaining strong human oversight where social workers like Alex use AI as a supportive tool rather than a decision-maker. This allows for professional judgment and context to play a crucial role in interpreting AI-generated insights.
Transparency and Accountability: Making AI processes transparent and holding developers accountable for the impacts of their systems. This means being clear about how decisions are made and who is responsible if the AI system leads to adverse outcomes.
By addressing these concerns, the use of AI in social work can be made more equitable and just, supporting social workers in their mission to help all individuals and communities fairly.
Jonathan Singer: Well, I really appreciate you addressing all of those issues around equity and auditing and things like that. And I'm wondering, in a situation where you have social workers who need to audit AI technology, what resources or skills would they need to have in order to be able to audit something that might have been engineered by computer scientists, something that they have no expertise in?
ChatGPT-4o: Great question. Auditing AI technology indeed requires a unique set of skills and resources that may not traditionally fall within the expertise of social workers. However, several strategies can help bridge this gap:
Interdisciplinary Collaboration: Social workers can collaborate with data scientists, ethicists, and IT professionals. This team approach ensures that the audit benefits from diverse expertise. Social workers bring their understanding of social contexts and impacts, while data scientists provide technical insights into how the AI operates.
Training and Education: Providing social workers with training on basic AI principles, data ethics, and auditing techniques can empower them to participate more effectively in the auditing process. This training doesn’t need to make them experts but should cover understanding how algorithms work, recognizing bias, and interpreting data outputs.
Accessible Tools: Developing and using auditing tools designed to be user-friendly for non-experts can help. These tools can automate parts of the auditing process and present findings in ways that are easy to understand and act upon. For example, visualization tools can help highlight potential biases in data or decision-making patterns.
Ethical Frameworks: Using established ethical frameworks and guidelines specifically tailored for AI in social work can guide the auditing process. These frameworks can outline key principles and practices for ensuring fairness, transparency, and accountability.
Continuous Learning and Support: Establishing ongoing learning opportunities and support networks for social workers to stay updated on advancements in AI and auditing practices. Workshops, webinars, and online courses can be valuable resources.
By leveraging these strategies, social workers can effectively audit AI technologies, ensuring they are used ethically and equitably in their practice.
Jonathan Singer: I'm really glad you brought up ethics. Are you familiar with the NASW Code of Ethics?
ChatGPT-4o: Yes, I am familiar with the NASW Code of Ethics. The Code of Ethics outlines the core values and ethical principles that guide social work practice, including service, social justice, dignity and worth of the person, importance of human relationships, integrity, and competence. These principles are crucial when considering the integration of AI in social work to ensure technology is used in a way that upholds these values. How do you see the NASW Code of Ethics intersecting with the use of AI in social work?
Jonathan Singer: That's a great question. I was actually about to ask you the same thing. I mean, I think that one of the issues is that AI is not in the current version of the Code of Ethics. So, what are some ways that you think that the NASW Code of Ethics needs to be updated in order to incorporate some of these ethical challenges that AI might present in the future?
ChatGPT-4o: Updating the NASW Code of Ethics to incorporate ethical challenges related to AI is essential for guiding social workers in this evolving landscape. Here are a few ways the Code could be updated:
Informed Consent and Transparency: Add guidelines specifically addressing the use of AI in practice. Social workers should be transparent with clients about how AI tools are being used in their care, including the limitations and potential biases of these technologies. Clients should have the right to understand and consent to the use of AI in their treatment or assessment processes.
Bias and Fairness: Include explicit directives to prevent and address biases in AI systems. Social workers should advocate for the development and use of AI tools that are fair and equitable, actively working to identify and mitigate any biases that could affect marginalized communities.
Confidentiality and Privacy: Reinforce the importance of maintaining confidentiality and privacy when using AI. This includes ensuring that data used by AI systems is securely stored and that client information is protected from unauthorized access.
Competence and Training: Emphasize the need for ongoing education and training related to AI. Social workers should be competent in understanding and using AI tools, and they should seek continuous professional development to stay updated on the latest advancements and ethical considerations.
Accountability and Oversight: Establish clear accountability measures for the use of AI in social work practice. This could involve setting up regular audits, creating oversight bodies, and ensuring that social workers are held responsible for the ethical use of AI.
Human-Centered Approach: Reaffirm the importance of the human-centered approach in social work. AI should be used to support and enhance the work of social workers, not replace the human connection that is fundamental to effective practice.
By incorporating these updates, the NASW Code of Ethics can provide a robust framework for social workers to navigate the ethical complexities of integrating AI into their practice, ensuring that technology serves to enhance, rather than undermine, their core values and mission.
Jonathan Singer: So I think that the updating of the NASW Code of Ethics is really important, and I like some of the things that you pointed out there. I think it's challenging to hold social workers accountable for AI when social workers are not the ones developing AI. I'm wondering if we can pivot to a different situation, one where we have social workers who are engaged in community organizing around a topic, say, the development of a casino in a neighborhood. How might AI be used in that scenario to support a community that might be opposed to having a casino in their neighborhood?
ChatGPT-4o: That's an intriguing scenario. AI can be a powerful tool in community organizing, including efforts to oppose the development of a casino in a neighborhood. Here's how AI could support such a community initiative:
Data Analysis and Visualization: AI can analyze large datasets to provide insights into the potential impacts of the casino. This includes economic forecasts, crime rate projections, and social consequences. These insights can be presented through visualizations, making complex data more accessible and compelling for community members and stakeholders.
Sentiment Analysis: By analyzing social media posts, community forums, and local news, AI can gauge public opinion on the casino development. Sentiment analysis can help organizers understand the concerns and attitudes of the community, allowing them to tailor their messaging and strategies more effectively.
Predictive Modeling: AI can predict the long-term effects of the casino on the neighborhood, such as changes in property values, local business impacts, and shifts in community demographics. This information can be crucial in making a strong case against the development.
Resource Mobilization: AI can help identify and mobilize resources by analyzing patterns in funding, volunteer engagement, and public support. This could include identifying potential donors, allies, and partners who are likely to support the cause based on past behaviors and interests.
Strategic Planning: AI can assist in strategic planning by simulating different scenarios and outcomes based on various actions the community might take. This can help organizers choose the most effective strategies to oppose the casino development.
Community Engagement: AI-powered chatbots and virtual assistants can facilitate communication and engagement with community members, providing information, answering questions, and collecting feedback. This can enhance outreach efforts and ensure that community voices are heard and considered.
By leveraging AI in these ways, social workers and community organizers can strengthen their efforts, making their campaigns more data-driven, strategic, and responsive to the needs and sentiments of the community.
Jonathan Singer: These are all really interesting examples, and I appreciate you providing the details about the community organizing. And I want to go back to something that you were talking about earlier with the child welfare scenario. And I'm wondering, you know, social workers think about Bronfenbrenner's ecosystems model, micro, meso, macro, exo, chrono, and I'm wondering how that framework can help to understand the use of AI in a child welfare setting where you might have predictive analytics and things like that working out. So how can you use ecosystems as a framework for understanding the role of AI in the child welfare setting that you were talking about?
ChatGPT-4o: Using Bronfenbrenner's ecological systems theory can provide a comprehensive framework for understanding the role of AI in child welfare by examining how AI interacts at different levels of a child's environment. Let's break this down:
Microsystem: This level includes the child's immediate environment, such as family, school, and peers. AI can be used to gather and analyze data from these direct interactions to assess risk and identify protective factors. For example, AI can monitor school attendance, academic performance, and family dynamics to detect early signs of potential issues.
Mesosystem: This involves the interconnections between microsystems, like the relationship between the child's family and school. AI can facilitate better communication and coordination between these entities. For instance, AI systems can ensure that important information about a child's well-being is shared in real-time between teachers, social workers, and healthcare providers, fostering a more holistic approach to the child's care.
Exosystem: This encompasses the broader social systems that indirectly affect the child, such as parents' workplaces, community services, and local policies. AI can analyze data from these sources to understand their impact on the child's environment. For example, predictive analytics can identify community trends or policy changes that might affect child welfare, enabling proactive measures to be taken.
Macrosystem: This level includes the larger cultural and societal context, such as socioeconomic status, cultural norms, and legislation. AI can help social workers understand and address these broader factors by analyzing large-scale data trends and providing insights into systemic issues affecting child welfare. This can guide advocacy efforts and policy development to support vulnerable children and families.
Chronosystem: This refers to the dimension of time, including life transitions and historical events. AI can track and analyze longitudinal data to understand how changes over time impact a child's development and well-being. For example, AI can study the effects of significant life events, such as parental divorce or a change in living situation, on a child's risk profile, allowing for timely interventions.
By applying Bronfenbrenner's ecosystems model, social workers can use AI to gain a deeper, multi-layered understanding of the various factors influencing a child's welfare. This approach ensures that AI is not just used to make predictions in isolation but is integrated into a broader, context-aware strategy that considers the complex, interconnected systems affecting each child.
Jonathan Singer: I appreciate the analysis, and some of those examples were really informative. Now, one of the criticisms of Bronfenbrenner's ecosystems model is that it places this idea of racism as kind of an outer level, but the experience of racism can really be a micro system experience. How would you use anti-racist approaches to understand an ecosystem's perspective in a child welfare setting?
ChatGPT-4o: You're absolutely right. Racism can permeate every level of a child's ecosystem, from the micro to the macro, and it's essential to adopt an anti-racist perspective to fully understand and address its impacts in a child welfare setting. Here’s how we can integrate anti-racist approaches at each level of Bronfenbrenner’s ecosystems model:
Microsystem: Recognize that racism can occur in the child's immediate environments, such as within the family, school, and peer interactions. Anti-racist approaches here involve:
Educating and training families and educators on recognizing and addressing racism.
Implementing culturally responsive practices in schools to create a safe and inclusive environment.
Ensuring that social workers are trained to identify and address racism experienced by children directly in their immediate surroundings.
Mesosystem: At this level, it’s about the relationships between the child's microsystems. Anti-racist approaches include:
Promoting strong, collaborative relationships between families, schools, and community organizations that prioritize anti-racism.
Facilitating open communication channels that allow for discussions about racism and its impacts, ensuring that all parties are working together to support the child.
Developing support networks that include mentorship programs and community groups focused on combating racism.
Exosystem: Here, we consider the broader social systems that influence the child indirectly. Anti-racist approaches can involve:
Analyzing and addressing systemic racism in institutions such as parents' workplaces, healthcare systems, and social services.
Advocating for policies that promote equity and reduce racial disparities in access to resources and opportunities.
Using AI to identify and track patterns of systemic racism, enabling targeted interventions to support affected children and families.
Macrosystem: This level includes cultural and societal norms, values, and laws. Anti-racist approaches involve:
Advocating for broader societal change to address racism, including reforms in education, justice, and housing policies.
Promoting public awareness campaigns to shift societal attitudes and norms towards greater equity and inclusion.
Ensuring that child welfare policies are developed with an anti-racist lens, aiming to dismantle systemic barriers.
Chronosystem: This involves considering the impact of historical and life events over time. Anti-racist approaches here include:
Understanding and addressing the long-term effects of historical and ongoing racism on children and their families.
Incorporating historical context into assessments and interventions to provide a more comprehensive understanding of a child's experiences.
Engaging in continuous reflection and adaptation of practices to remain responsive to the evolving nature of racism and its impacts over time.
By applying anti-racist approaches across all levels of Bronfenbrenner’s ecosystems model, social workers can more effectively address the pervasive and multifaceted nature of racism. This ensures that interventions are not only holistic but also equitable and just, truly supporting the well-being of all children in the child welfare system.
Jonathan Singer: Thank you so much. I really appreciate you sharing all of your thoughts and ideas about the role of AI and social work practice in child welfare, in community organizing, and especially your analysis of Bronfenbrenner's ecosystems model and the role of anti-racism in all of that. I think that was really helpful and insightful for our listeners. I appreciate it.
ChatGPT-4o: You're very welcome! I'm glad I could contribute to the discussion. It's important to explore these topics deeply, as they have significant implications for social work practice and the communities we serve. If there's anything else you'd like to discuss in the future, I'd be happy to help. Thank you for having me on the Social Work Podcast!
Singer, J. B. (Producer/Host). (2024, May 22). #137 - The Role of AI in Child Welfare and Community Organizing: Interview with ChatGPT-4o [Audio Podcast]. Social Work Podcast. Retrieved from https://www.socialworkpodcast.com/2024/05/ChatGPT-4o.html
No comments:
Post a Comment