Ethical Considerations Flashcards
1
Q
5 ethical challenges in the case study
A
- Data privacy and security
(Protecting user data from misuse) - Bias and fairness
(Avoiding discriminatory responses) - Accountability and Responsibility
(Assigning responsibilities for the advice given) - Transparency
(Explaining the decision making clearly) - Misinformation and Manipulation
(Preventing the spread of false information)
1
Q
How can we protect user data from misuse (ethical consideration of data privacy and security)?
A
- Ensure that training data is anonymized to protect identities
- Secure data handling using strong encryption method to protect data during transmission
- Complication with data protection regulations such as GDPR, HIPAA
2
Q
How can we avoid discriminatory responses?
A
- Diverse training data that is representative of many viewpoints and demographics
- Implement bias detection tools
- Conduct regular audits of the models to ensure they do not show discriminatory behavior
3
Q
How can responsibility be assigned for responses?
A
- Establish clear governance structures that define who is responsible for different aspects of the model’s deployment and usage
- Thorough documentation of the model development process ensures transparency.
- Ensure there is human supervision to intervene with the model if it makes incorrect or harmful decisions
4
Q
How can the decision-making process of the LLM be clearly explained?
A
- Implement techniques that explain to users how it arrives to its conclusions
- Communicate an honest evaluation of the model to users (Limitations, strengths, potential biases, etc.)
- Implement feedback systems so users can report issues and provide feedback
5
Q
How can we prevent the spread of misinformation?
A
- Implement fact-checking mechanisms to validate outputs generated by the model
- Ensure the training dataset uses data from credible and reliable sources
- Continuous monitoring of the model’s outputs to check for misinformation and update to fix inaccuracies if necessary.
