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Answer :
- What is deepfake? How is deepfake related to AI ethics?
Deepfake is a technology that uses artificial intelligence (AI) to create realistic-looking fake videos or audio recordings. It is achieved through machine learning techniques, especially deep learning, which manipulates real content to make it appear authentic. In terms of AI ethics, deepfakes raise significant concerns as they can be used to spread misinformation, create fake news, manipulate public opinion, and infringe privacy rights. Ethical considerations include the misuse of personal data, consent, and potential harm to individuals or society.
- What is AI bias?
AI bias refers to the presence of systematic and unfair discrimination in AI algorithms or models. It occurs when the AI model produces prejudiced outcomes that reflect inaccuracies or stereotypes present in the training data.
- Give some examples of AI bias.
- Facial Recognition: AI systems may misidentify people of certain ethnicities more often than others.
- Hiring Tools: AI algorithms may favor candidates of a certain gender or exclude minority groups due to biased input data.
- Predictive Policing: AI might target certain communities more frequently based on biased crime statistics.
- Discuss the bias in data collection of AI.
Bias in data collection can stem from various sources, such as historical prejudices, sampling errors, or stereotypes in the data. If the data used to train the model is not representative of the whole population, the AI system may learn and reinforce these biases.
- How is training data useful for AI? Why should it be fair?
Training data is crucial for teaching AI models how to make predictions or decisions. Fair training data is essential because it ensures the AI system is unbiased and reliable, thus preventing discriminatory or harmful outcomes.
- How would you assure that the data used for an AI model is fair and unbiased?
Ensuring fairness and lack of bias requires:
- Diverse and representative data sets.
- Regular auditing and testing for bias.
- Implementation of bias-correction techniques.
- Continual updates to incorporate new, unbiased data.
- What are the possible reasons for AI bias?
- Skewed or incomplete datasets.
- Historical and societal biases reflected in data.
- Lack of diversity in AI development teams.
- Inadequate testing and validation procedures.
- Why is diversity of the data collection team important for AI?
A diverse team brings different perspectives, which can help identify and mitigate biases in data collection. It can also ensure a more comprehensive and inclusive approach to model development and validation.
- Why is regular bias testing important for training data?
Regular bias testing ensures that AI systems remain fair and accurate over time. It helps detect any biases that may arise from new data or changes in societal norms, thus maintaining trustworthiness.
- Discuss some principles that will make an AI model trusted.
- Transparency: Clear understanding of how the model works and its decision-making process.
- Accountability: Mechanisms to correct any errors or biases in the AI’s outcomes.
- Inclusiveness: Representation of diverse groups in model development and deployment.
- Fairness: Ensuring equitable treatment of all individuals or groups.
- Discuss how AI may be used in human rights violations.
AI can be misused for surveillance, censorship, discrimination, and infringing privacy rights. It may also support oppressive regimes by enabling invasive monitoring or bias in policing, thereby facilitating human rights abuses.
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