AI is everywhere; is it in qualitative research, too? Potentials, Pitfalls, and Open-Source Solutions (Part 2)

In this second part of our exploration into AI’s role in qualitative research (you can find part one here), we’ll focus on the concerns and issus that come with its growing presence as well as some tips for utilizing AI tools like Taguette, Weft QDA, and  AcademiaOS and avoid the pitfalls. 

Concerns and Pitfalls:

One concern is the potential loss of human judgment. Sure, AI can code and analyze data, but it can’t truly understand the subtleties and emotional undertones of human experiences. Qualitative research relies on context and interpretation, and there’s a real risk that AI might oversimplify the nuances (Denecke et al., 2023; Arbelaez Ossa et al., 2024). Also,some researchers fear that by automating tasks like coding, we might be reducing our own engagement with the data (Schmitt, 2024; Zhang et al., 2024).

Then, there’s the issue of bias. AI models are trained on existing datasets, which means they can reflect the biases in those datasets. When applied to qualitative research, these biases can skew the analysis, particularly in fields like sociology or anthropology, where cultural sensitivity is critical (Denecke et al., 2023; Kooli, 2023). The transparency of AI systems is also a challenge. Many AI tools are “black boxes,” meaning researchers don’t fully understand how the algorithms arrive at certain conclusions (Arbelaez Ossa et al., 2024) and this can undermine the trust and integrity of qualitative research.

There’s also a concern about de-skilling. As AI takes over tasks like data coding and pattern recognition, researchers may lose the opportunity to develop essential analytical skills (Christou, 2023; Arbelaez Ossa et al., 2024), and early-career researchers might miss out on learning experiences that deepen their understanding of the field. There is also a more considerable looming risk that AI could standardize research outcomes. Since AI tends to generate results based on patterns, it might reinforce existing trends rather than offering new or challenging perspectives (Arbelaez Ossa et al., 2024) that would be detrimental to the intellectual landscape of qualitative research.

Finally, there’s the problem of data privacy. AI systems process data without specific concern or clarity about use or storage and this makes privacy and consent critical concerns (Denecke et al., 2023). This and probably many more concerns are important to consider when it comes to utilizing AI for qualitative research. 

Some Tips for Balancing the Use of AI

–   AI as a Complementary Tool 

AI works best when it’s used as a complement to human expertise, not a replacement. Tasks like coding and pattern recognition can be automated, but researchers should retain control over interpretation and analysis. AI should assist, not dictate (Schmitt, 2024; Christou, 2023).

–   Mitigating the Risks of Bias and Automation

To counter the risks of bias and over-reliance on automation,  AI-generated outputs should be combined with manual reviews with the idea that while we harness AI’s efficiency we also ensure that the insights remain contextually accurate and ethically sound (Arbelaez Ossa et al., 2024).

–   AI Literacy and Critical Engagement  

AI’s potential in qualitative research will only grow as it continues to evolve and in order to understand the future potentials and pitfalls of its integration, researchers need to learn about it and critically engage with its development. The rise of open-source tools could mean that AI will become increasingly accessible, but it’s up to the research community to ensure these tools are used responsibly.

References:

12 Data analysis tools for qualitative research. (2024, January 4). PhD Guidance. https://www.phdguidance.org/data-analysis-tools-for-qualitative-research/

Arbelaez Ossa, L., Lorenzini, G., Milford, S. R., Shaw, D., Elger, B. S., & Rost, M. (2024). Integrating ethics in AI development: A qualitative study. BMC Medical Ethics, 25(1), 10. https://doi.org/10.1186/s12910-023-01000-0

Bishop, L. (2023). A Computer Wrote this Paper: What ChatGPT Means for Education, Research, and Writing. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4338981

Christou, P. (2023). A Critical Perspective Over Whether and How to Acknowledge the Use of Artificial Intelligence (AI) in Qualitative Studies. The Qualitative Report, 28(7), 1981–1991. https://doi.org/10.46743/2160-3715/2023.6407

Crawford, K. (2021). The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.

Denecke, K., Glauser, R., & Reichenpfader, D. (2023). Assessing the Potential and Risks of AI-Based Tools in Higher Education: Results from an eSurvey and SWOT Analysis. Trends in Higher Education, 2(4), Article 4. https://doi.org/10.3390/higheredu2040039

Hassani, H., & Silva, E. S. (2023). The Role of ChatGPT in Data Science: How AI-Assisted Conversational Interfaces Are Revolutionizing the Field. Big Data and Cognitive Computing, 7(2), 62. https://doi.org/10.3390/bdcc7020062

Schmitt, B. (2024). Transforming qualitative research in phygital settings: The role of generative AI. Qualitative Market Research: An International Journal, 27(3), 523–526. https://doi.org/10.1108/QMR-08-2023-0107

Übellacker, T. (n.d.). AcademiaOS: Automating Grounded Theory Development in Qualitative Research with Large Language Models. Ar5iv. Retrieved October 5, 2024, from https://ar5iv.labs.arxiv.org/html/2403.08844

Zhang, H., Wu, C., Xie, J., Iyu, Y., & Cai, J. (n.d.). Redefining Qualitative Analysis in the AI Era: Utilizing ChatGPT for Efficient Thematic Analysis. Ar5iv. Retrieved October 5, 2024, from https://ar5iv.labs.arxiv.org/html/2309.10771

PUG (Python User’s Group)

The next PUG meeting will be held on Wednesday, Oct. 23 from 2-3pm on Zoom. RSVP to receive the link on the day of the meeting.

This week we will practice a little web scraping with Python by playing around with the reddit API. We’ll work through the process of extracting info from the site step-by-step together. Hope to see you there!

PUG (Python User’s Group)

The next PUG meeting will be held on Wednesday, Oct. 23 from 2-3pm on Zoom.

This week we will practice a little web scraping with Python by playing around with the reddit API. We’ll work through the process of extracting info from the site step-by-step together. RSVP to receive the Zoom link on the day of the meeting.

Hope to see you there!

Image of a Warehouse worker dealing with files of digital data.

6 Tools for Digital Safety in the Age of Surveillance

6 Tools for Digital Safety in the Age of Surveillance

In response to the past year of anti-genocide student protests across NYC, Gov. Kathy Hochul pledged $3 million dollars to NYPD, and to CUNY administration to crack down on free speech and increase surveillance of student and faculty online activity. In this moment of academic repression where faculty are being fired for taking a stance against genocide online, maintaining internet safety is more important than ever. Here’s a guide to some effective tools to keep your private data private. (The asterisks* indicate that the software is open-source, woohoo!)

1. Password Managers

One of the simplest yet most powerful tools for online safety is a password manager. If you don’t practice good password management, the rest of these tools are a little useless.These applications securely store your passwords and generate strong, unique passwords for each of your accounts. Popular options like LastPass and Bitwarden not only simplify the login process, but also reduce the risk of using weak or duplicate passwords (we use Bitwarden at the GCDI). By enabling two-factor authentication (2FA) alongside your password manager, you add an extra layer of security. But the most secure password manager is a notebook that you keep in your desk! 

For help on creating strong passwords, check out this Tagging the Tower Article by a GCDF alum, Sam O’Hana. 

2. Virtual Private Networks (VPNs)

A VPN is essential for anyone who frequently uses public Wi-Fi networks (or university wifi networks!) By encrypting your internet connection, a VPN helps shield your data from prying eyes. Services like NordVPN, ProtonVPN*, ExpressVPN are user-friendly options that protect your browsing activity and mask your IP address, making it harder to intercept your information.

3. Tor Network

Tor network* is designed to enhance online privacy and anonymity. It works by routing your internet connection through a series of volunteer-operated servers, known as nodes, which encrypt your data multiple times. When you access the internet via Tor, your data travels through these nodes before reaching its final destination, making it difficult for anyone to trace your activity back to you. This process not only obscures your IP address but also helps users bypass censorship and access blocked websites. While Tor is often associated with dark web activities, it’s also used by journalists, activists, and individuals seeking enhanced privacy online.

4. Ad Blockers and Privacy Extensions

Online ads can be more than just an annoyance; they can also pose security risks. Ad blockers like uBlock Origin* or Privacy Badger* help block unwanted ads and trackers, preventing your online activities from being monitored. And browser extensions like HTTPS Everywhere* ensure you connect to websites securely, automatically redirecting you to the most secure version of the website whenever available, which can be useful when browsing on the Tor network.

5. Secure Browsers

Using a secure browser can make a significant difference in your online safety. Browsers like Brave* and Firefox* focus on user privacy and incorporate built-in features to block trackers and protect against fingerprinting. These browsers prioritize security, offering a safer browsing experience compared to other mainstream options.

6. Secure Communication and Co-working Platforms

Finally, secure communication is paramount in protecting your privacy. Encrypted messaging apps like Signal* and Telegram* provide end-to-end encryption, ensuring that your conversations remain private and secure from potential eavesdroppers. For video calls, platforms like Jitsi* also offer enhanced security features, allowing you to communicate safely. To collaborate on documents, try using Cryptpad* instead of Google Suite.

CUNY DHI Lightning Talks

Lightning talks are informal, 3-minute / 3-slide presentations that offer a brief insight into your digital humanities project, research, or question. Projects at any stage–whether just beginning, in-development, or completed–are welcome! The audience includes a diverse community of engaged CUNY students and colleagues who are eager to hear about your project and offer feedback.

Check out the CFP at http://cuny.is/dhifall24

Register to attend or present (both in-person and via Zoom) at http://cuny.is/cunygcdi24. 

This event is free and open to all CUNY students, faculty, and staff.

AI is everywhere; is it in qualitative research, too? Potentials, Pitfalls, and Open-Source Solutions (Part 1)

AI is everywhere these days. It’s in your pocket,  driving cars, selecting what show we should binge on, and which search results we should see first. So, what about qualitative research? 

I was curious to know how social science and humanities researchers are utilizing AI and how they are dealing with the more complicated issues that it raises. While AI offers some amazing tools to streamline and enhance the way we approach qualitative research, it also brings up a host of questions about how much we can—or should—trust machines to do the heavy lifting in fields that thrive on human insight and interpretation.As a geographer, I’m concerned about AI’s environmental impacts, often overlooked.

As a geographer, I am also concerned with the environmental impacts of AI development, which are often overlooked in discussions about its technological promise. Kate Crawford, in “Atlas of AI”, sheds light on the massive ecological footprint AI leaves behind—from the extraction of rare earth metals to the immense energy consumption required to train AI models. These processes contribute to resource depletion, environmental degradation, and significant carbon emissions, raising ethical questions about the sustainability of AI systems. The hidden costs behind AI’s seemingly virtual world challenge us to rethink its true environmental and social impact.

So, I thought it could be helpful to review some research on these issues, and it could help me think about the potentials and pitfalls of AI in qualitative research in this series. For the first part I mainly looked for ways that AI is making things faster and more accessible for researchers and in the second part, I review the ethical concerns and challenges that come with it. Also, in keeping with the spirit of our work at the Digital Initiatives, I’ll introduce some of the best open-source tools that make it easier for everyone to harness AI’s power in qualitative analysis.

Potentials:

Efficient Data Processing and Thematic Analysis.

Let’s face it—manually coding qualitative data can be a time-consuming task, and large language models (LLMs) like ChatGPT can seriously speed up this process. AI allows researchers to work more efficiently and focus on interpretation by automating repetitive tasks like coding and thematic analysis. Studies show that AI can handle massive datasets, identify key themes, and streamline analysis in a fraction of the time (Zhang et al., 2024).

Take Taguette as an example. This open-source platform enables researchers to easily tag and code data, export results, and navigate qualitative data. By handling the heavy lifting, AI frees up time for researchers to engage with deeper, more complex aspects of the data. Similarly, platforms like AcademiaOS leverage AI to assist in grounded theory development, supporting human researchers in managing large amounts of information (Übellacker, 2023).

This expansion of AI utilization in research has also helped democratize the field to a certain extent. Historically, advanced tools for qualitative analysis were locked behind expensive paywalls of high-end, proprietary software like NVivo, or Atlas Ti.

Open-Source Platforms for AI-Driven Qualitative Research

–   Taguette

Taguette is an open-source qualitative research tool that allows researchers to code and tag qualitative data efficiently. Its user-friendly interface makes it accessible to both novice and experienced researchers. Since it’s free and runs on local machines, it’s a great solution for those who want to ensure their data is secure.

–   Weft QDA

For researchers on a budget, Weft QDA offers basic qualitative coding functionalities without the cost of commercial software. Its simplicity makes it a great choice for those new to qualitative research, while its open-source nature ensures that users can adapt the tool to their specific needs.

–   AcademiaOS

AcademiaOS is designed to assist researchers with grounded theory development, using AI to automate much of the coding and analysis. While it handles the heavy lifting, it still requires human oversight, ensuring that the theoretical framework remains sound.

 

References:

Crawford, K. (2021). The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.

Übellacker, T. (n.d.). AcademiaOS: Automating Grounded Theory Development in Qualitative Research with Large Language Models. Ar5iv. Retrieved October 5, 2024, from https://ar5iv.labs.arxiv.org/html/2403.08844

Zhang, H., Wu, C., Xie, J., Iyu, Y., & Cai, J. (n.d.). Redefining Qualitative Analysis in the AI Era: Utilizing ChatGPT for Efficient Thematic Analysis. Ar5iv. Retrieved October 5, 2024, from https://ar5iv.labs.arxiv.org/html/2309.10771

 

Mapping User Group

The Mapping working group is a network of CUNY students, faculty, and staff interested in sharing methods and techniques and finding support from others about ways GIS/Mapping can be used to further research and teaching. The working group provides a sustained, supportive environment to learn new skills, share familiar skills, and collaborate with the Digital Fellows and the CUNY digital community.

If you are interested in this group, we invite you also to join our CUNY Academic Commons group.

Python User’s Group

Python User’ Group (or PUG for short) is an open and informal collaborative space for experimentation and exploration with the Python programming language. It is an opportunity for those interested in Python to work together virtually and find support. Whether you are looking for advice or assistance with new or current projects, looking to discuss and learn new skills using Python tools, or to join us to play around with our collection of sample datasets, PUG is your place!

PUG is open to people of all skill levels, disciplines, and backgrounds. Complete beginners to Python will find a place here. Come, and let’s learn together.

Join our CUNY Commons Group for more information!

Python User’s Group

Python User’ Group (or PUG for short) is an open and informal collaborative space for experimentation and exploration with the Python programming language. It is an opportunity for those interested in Python to work together virtually and find support. Whether you are looking for advice or assistance with new or current projects, looking to discuss and learn new skills using Python tools, or to join us to play around with our collection of sample datasets, PUG is your place!

PUG is open to people of all skill levels, disciplines, and backgrounds. Complete beginners to Python will find a place here. Come, and let’s learn together.

Join our CUNY Commons Group for more information!

Teaching with Manifold: A Community Event

Join us for our first Lunchtime Manifold Community Event!
Whether you’re fresh to Manifold or looking to refuel your research and teaching, this event is for you! Manifold Graduate Fellows, Maura McCreight and Cen Liu, will share their experiences teaching with Manifold in the classroom. There will also be plenty of time for conversation and idea-sharing!
This is a fantastic opportunity to learn about the Manifold digital publishing platform, see how we use it at CUNY, and engage with the community.
Bring your lunch and come hang out with us; we will have snacks and drinks!