How unions can use ChatGPT and generative AI for growth

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What are the ways that unions could use generative AI and ChatGPT to grow in membership or lift capacity?

In this article, I want to briefly go over some of the areas that unions could use ChatGPT and other generative AI tools for growth, communications, campaigning and operations.

Read first: What unions need to know about ChatGPT and generative AI

It is up to each union to navigate the ethics of using generative AI. It is becoming increasingly clear that AI and ChatGPT are enormously problematic, for example labour standards, data and privacy, or climate change impact to name just three areas. The entire AI field is unregulated (in the US as well as Australia), undermines labour standards and benefits Silicone Valley billionaires and hedge-fund oligarchs.

Nonetheless, the labour-storage/saving capabilities of generative AI are rapidly developing. Unions rarely have the luxury of choosing which tools to use — most unions use Microsoft Office rather than open-source versions for example.

Noting that generative AI an ChatGPT are rapidly changing, here are some promising areas for unions:

  • Communications: generative AI is relatively effective at creating social media, marketing and other communications content (including text, images and video). For unions with limited comms resources, ChatGPT will allow a comms officer to create a much larger volume of union content for social media, websites, flyers and posters, design assets, emails and more. The nature of generative AI is that it is highly responsive to niche/specific prompts, so the more context about a union’s goals, audience and “style” is included in the prompt, the more useful the output is. (Note: unions who use AI-generated images, should disclose that they have done so through some kind of label or note.)
  • Operations: There are a wide range of administrative areas in union operations that could be assisted by generative AI. For example, ChatGPT excels at producing project-management templates and check-lists. Similarly, when combined with AI transcription of recordings (e.g. of meetings via Zoom/Teams), generative AI can produce accurate summaries of meetings (e.g. member meetings, committee of management meetings, internal planning meetings) in just a few minutes.
  • Summarising from large texts: Generative AI can rapidly scan and summarise large volumes of text. For example, policy workers have used ChatGPT to summarise multiple years of UN climate reports, or answering specific questions about information in those reports. Law firms and finance firms are using this summary feature to scan and summarise corporate financial reports and annual reports. Unions research staff could use generative AI for the same purpose as part of research for organising and industrial campaigns. Obviously, a great deal of care and vigilance should be exercised when relying on this.
  • Assist with collective bargaining. Similarly industrial officers could use the summary and comparison feature of generative AI to assist with collective bargaining, by bringing all of your union’s agreements into a single conveniently accessed place that then allows you to rapidly find and compare clauses. (For details on how other unions have already done this, see this comment by French union official Nicolas Blanc.)
  • Expand trade union education. Generative AI that is trained on years or decades of union education material can assist union educators in creating new course modules and video training scripts. With all the caveats about providing sensitive union information to a generative AI tool, it would definitely be worth investigating tools like “Nolej“.
  • Member servicing: If your union has a call centre or member assistance centre, then generative AI can be used by team members to more rapidly provide comprehensive responses to member questions, especially by email. I absolutely would not recommend having any “automation” of responses by AI, rather AI tools (like should be used by union call centre staff to augment their work, and enable them to respond to members faster. For example, some companies provide customer service staff with generative AI tools that access and summarise internal knowledge-base documentation. This is especially useful not just for call centres, but social media teams who may not have the resources to respond to comments and messages on Facebook or other social platforms.

As noted, most of the current uses for generative AI for unions is about time-saving and productivity improvements.

Member and data privacy

As I note in my explainer for unions on generative AI, these tools use huge databases to work. Dr Christina Colclough, a global expert on unions and worker privacy, provides a sound word of caution for unions considering using ChatGPT and generative AI:

The moment a union uploads what could be sensitive information to these systems, is the moment these commercial companies own that data and can reuse it. So my recommendation is: never upload membership data, conversations, union meetings, databases or spreadsheet files that in any shape or form can be de-anonymised or otherwise linked to a person or persons.

Many companies are wary of their staff using ChatGPT for this reason, and unions should likewise be very cautious.

It is possible to use a self-hosted version of a generative AI tool, or to run a version of ChatGPT called Azure OpenAI, which doesn’t hand over your union’s private information. These two options are quite technical, so is likely only possible for larger unions with experienced IT staff.

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