What do intelligence analysts do?
The people who are really good understand sourcing and how important it is for critical thinking. The education should be focused on helping people recognize and refute bullshit. Step one is the critical thinking necessary to say, “This makes no sense,” or “This is just fluff.” The people who are professionally trained to be really good at understanding the quality and history of a source, and to understand the source’s access to information or lack of, are librarians. We should probably steal shamelessly from librarians. Data journalism, same thing. There are lots of parallel professions where we could be learning more to improve our own performance.
The folks that I’ve seen who crush it, they’re like a dog with a bone. They will not let go. They’ve got a question, they’re going to answer the question if it kills them and everybody else around them. It’s a kamikaze thing. Those people, the tenacious ones who care about sources and have critical thinking skills, or at least tools to help them think critically, seem the highest performers to me. As a rule, they all keep score. It’s part of their process.
That is from Santi Ruiz interviewing Rob Johnston, interesting throughout
“This study aims to investigate the presence of bias in the visual representation of librarians generated by ChatGPT across three different library settings: school, public, and academic. It focuses on analysing biases related to gender, ethnicity, age, attire, hairstyles and library design in the generated images.
The research employed a zero-shot prompting technique to instruct ChatGPT to create visualisations of librarians in the specified settings, either interacting with another librarian or advising a library user. The generated images were then evaluated based on criteria such as positioning, posture, visual cues indicating age and gender and the characteristics of the library environment.
The analysis revealed significant biases in the generated images, with a predominant depiction of librarians as Caucasian. Gender representation overstated the presence of men in all libraries, most notably in academic libraries with only 6% of academic librarians depicted as female. Additionally, there was a noticeable trend towards older librarians in public and academic settings, and the size of library buildings increased from school to academic environments.
These findings highlight the reinforcement of stereotypes and the misrepresentation of authority dynamics, particularly the portrayal of men in positions of power relative to female colleagues. This study contributes to the growing body of research on biases in generative AI outputs, emphasising the potential dangers of relying on such tools for image generation.
It underscores the importance of critically examining AI-generated content to avoid perpetuating discrimination and inequality within the profession of librarianship. The findings serve as a cautionary note regarding the implications of using generative AI for visual representation in professional contexts.”
