Machine Learning Bias and Challenges

The video explores the complexities of teaching computers to process human language, known as natural language processing (NLP). It outlines the steps involved in NLP, including text input, speech recognition, and text generation. The video also discusses the challenges of translating signed languages and the limitations of current technologies. It highlights the role of machine learning, particularly neural networks, in NLP and addresses various biases that can affect language tools. The importance of ethical considerations in computational linguistics is emphasized.

What is one of the main challenges in teaching computers human skills?

• Computers can easily recognize traffic lights.

• Computers can easily understand emotions.

• Human skills are simple and straightforward.

• Human skills are often automatic and hard to teach.

Which of the following is NOT a step in Natural Language Processing?

• Producing human language output

• Generating random numbers

• Understanding word meanings

• Inputting text

What is a major limitation of current technology for signed languages?

• It can translate signs to speech and vice versa.

• It can only translate from speech to signs.

• It relies solely on handshapes for translation.

• It is fully developed for all signed languages.

How do neural networks differ from traditional rule-based systems?

• They are based on complex human brain models.

• They use predefined rules for language tasks.

• They learn patterns from data independently.

• They require no training data.

What is supervised learning?

• Learning with random data

• Learning with paired data

• Learning with only audio data

• Learning with no data

What is historical bias in machine learning?

• Bias introduced by outdated technology

• Bias from the world reflected in computer output

• Bias from using too much data

• Bias from incorrect data labeling

Which type of bias occurs when some groups are underrepresented in training data?

• Measurement bias

• Representation bias

• Historical bias

• Evaluation bias

What is aggregation bias?

• Ignoring data from minority groups

• Focusing on irrelevant data

• Using data from only one source

• Combining data groups with different characteristics

What is deployment bias?

• Bias from user feedback

• Bias from initial system design

• Bias from system misuse after release

• Bias from incorrect data input

Why is ethical consideration important in computational linguistics?

• Because it is a new field

• Because technology is rarely used

• Because it affects daily interactions

• Because it is unrelated to linguistics