Methods and Techniques of Social Research (50:920:30/301)

It is important to take a critical and even skeptical approach to the current hype and push for the use of Generative AI. While some maintain that there are useful, productive, and profitable results, these  should be viewed with an informed and critical eye. The decision to use or not use Generative AI should be based on an understanding of the ethical, environmental and societal impact.  Below are some things to know about the less positive aspects of using Generative AI.

--------------------------------

Did you know that Generative AI

• hallucinates? creates fake citations (which librarians spend too much time trying to track down because someone used AI to write their paper...)? makes things up? provides false and misleading information?
• has a major environmental impact with increased demand for electricity and water consumption? 
• violates copyright, steals intellectual property and plagiarizes? 
• is a labor issue- deskilling and undermining expertise and negatively affecting the job market?
• is a privacy concern, harvesting, selling, sharing, and using our data with little transparency? 
• perpetuates existing biases?

Despite their broad potential, generative AI models also have several important limitations. Understanding these limitations is critical for using these technologies ethically and effectively.

Ethical Concerns

• Bias and Fairness: Generative AI models can learn biases present in the training data, producing outputs that reflect, reinforce, or amplify social prejudices and stereotypes.
• Misinformation and Manipulation: AI-generated content can be used to create convincing fake news, deepfakes, and other forms of misinformation, leading to potential manipulation and harm.
• Plagiarism and Copyright: The use of AI-generated content raises significant questions about authorship, intellectual property, and attribution, potentially leading to issues with plagiarism and copyright infringement.
• Attribution and Accountability: Determining responsibility for AI-generated content can be challenging, raising questions about who is accountable for errors, biases, or malicious outputs.
• Inequality: As AI providers move free to fee-based service models, unequal access to these tools could exacerbate existing global inequalities.

Quality and Reliability

• Quality: AI outputs may contain false, misleading, or inaccurate information.
• Consistency: Generative AI models can produce irrelevant or inconsistent results, even in response to the same prompt.
• Superficiality: While AI can generate content, it might lack true creativity, originality, and deep understanding of complex concepts.
• Degeneration: As AI-generated content fills the internet and becomes the source data on which future generations of AI are trained, the quality of AI output may degrade over time leading to "model collapse".

Data Privacy and Security

• Data Exposure: The training of generative AI models requires large datasets, which could contain sensitive or private information that might be inadvertently revealed in generated outputs.
• User Privacy: AI platforms may collect and retain personal data that could be used for purposes other than what was originally intended or disclosed to the user.

Energy Consumption and Environmental Impact

• High Computational Demands: Training and running large-scale generative AI models can require significant computational resources, leading to high energy consumption and environmental impact.

Human Dependency, De-skilling, and Displacement

• Dependency: Overreliance on AI-generated content might lead to reduced human skills and expertise, impacting critical thinking and creativity.
• Loss of Traditional Skills: Traditional skills like research, writing, and content creation might diminish as these tasks are taken over by AI.
• Job Displacement: AI could potentially displace large segments of the workforce by automating tasks once performed by humans.
• Labor Exploitation: AI systems rely on millions of low-paid workers around the world (particularly the Global South) who review, evaluate, and annotate AI outputs for quality assurance.