Robotics & Artificial Intelligence
Which of the following is an important ethical consideration in AI that relates to ensuring fairness and accountability?
- The development of Natural Language Processing tools
- Bias, prejudice, and fairness in AI decision-making
- The transparency of AI algorithms
- The effectiveness of AI in healthcare applications
Answer
Bias, prejudice, and fairness in AI decision-making
Reason — Bias, prejudice, and fairness are important ethical issues in AI. Bias and prejudice can lead to unfair or discriminatory decisions, while fairness ensures equal treatment of individuals or groups. Addressing these issues is essential to maintain accountability and responsible use of AI systems, so that AI decisions are just, transparent, and do not harm society.
Related Questions
Ethics in AI refers to the moral implications and principles that guide the use of AI technologies. Issues such as bias, fairness, accountability, and transparency are essential to address when developing AI systems. Ethical considerations ensure AI is used responsibly, minimising harm and maximising benefits for society.
Mention two ethical issues in AI with their significance.
Natural language processing (NLP) algorithms are crucial in AI systems, such as chatbots, that simulate human conversation. These AI systems rely on NLP to understand and respond to human language. The Turing test is an important measure of how effectively these systems interact with humans.
What is the main purpose of using the Turing test in AI development?
- To improve the security of AI systems
- To assess how AI systems handle sensitive data
- To evaluate if AI systems can pass for conversation with humans
- To train AI systems on large data sets
To acquire relevant data for AI projects, methods such as surveys, observations, and web scraping are used. For instance, a company might use online surveys to gather data on customer preferences, which can then be used to enhance AI systems in e-commerce.
What is the role of surveys and web scraping in acquiring data for AI projects? How do these methods contribute to improving AI-based systems?
Data visualisation techniques, such as bar graphs, scatter plots, and heat maps, are used to explore and understand large datasets. These visual representations help in identifying patterns and making decisions based on the data's characteristics.
Why is data visualisation important in AI projects? How does it help in understanding complex data patterns and relationships?