Using AI Responsibly

Definition:

Hallucinations are when a generative AI produces a clearly false statement or result but presents it as fact. Hallucinations happen because LLMs are trained by massive data sets, including information and data found on the internet. Not all data sets have been fact checked because of the enormity of their size. It is well known that not all the information found on the internet is accurate, but the AI has no way of differentiating fact from fallacy or misinformation. 

Workaround:

Always fact-check the results of any prompt to ensure accuracy. If you discover a hallucination, correct the AI and either upload or reference a reliable source with the correct information. Then, try your prompt again. If you get another hallucination, revise your prompt to include specific and well-defined boundaries and constraints. 

Definition:

Generative AI is great for administrative tasks and narrow prompts. However, it is limited in its scope and currently unable to process broad challenges like developing a strategy, decision making, or navigating ethical dilemmas. It does not currently have the capability to understand context or complex situations. It focuses mainly on causality (cause and effect) and pattern recognition. 

Workaround:

Use it to support your work in these areas to make the administrative part easier. It can also help with brainstorming and idea generation, but you will need to piece it all together. 

It is not acceptable to use AI tools that are not approved by the university. Using AI tools not approved by the university inherently carries risks such as data exposure, ethical concerns, and potential inaccuracies. Users must be aware of these risks and take appropriate measures to mitigate them. Inputting data into un-approved AI tools is analogous to sharing data publicly, which is prohibited by many data use agreements. 

Risks of using non-approved AI tools include:

1. Data Exposure & Privacy Sensitive Data Leakage:
   1. Breach Risk: Inputting Confidential or Highly confidential data (e.g., PHI, HIPAA-regulated info) may result in unintended storage or exposure. 
   2. Data Usage by Providers: Many unapproved AI tools use your inputs to train their models, which may include personal or institutional data. 
   3. Retention & Access: User data may be stored indefinitely and could be accessed without authorization. 
2. Security Threats: 
   1. Breach Risk: Inputting sensitive data increases the risk of exposure through cyberattacks and provider vulnerabilities. 
   2. Phishing & Exploits: AI can be misused to craft convincing phishing messages or support other malicious activities. 
   3. Misuse of Data: Information may be exploited for identity theft, fraud, or targeted manipulation. 
3. Research & Ethical Concerns 
   1. Inaccuracy: AI-generated content may be incorrect, misleading, or fabricated (e.g., fake citations). 
   2. Bias: Outputs may reflect and perpetuate biases from training data, including synthetic datasets. 
   3. Ethical Misuse: AI can be used to generate harmful or unethical content. 
   4. Privacy in Research: Sharing research data with unapproved tools may violate privacy or data use agreements. 
   5. Reproducibility: Reproducibility is a key tenet of research and one of the challenges of AI tools, researchers, in particular, should do due diligence in documenting reproducibility for their research should scientific process questions arise.