This document discusses AI ethics and the importance of addressing algorithmic bias, privacy violations, misinformation, and the technological divide. It notes that AI systems can harm people if they are not developed with sensitivity to issues like racial bias. Specific challenges discussed include debiasing word embeddings to reduce gender stereotypes and determining how to handle gray areas of ethics. The document also questions whether humans actually prefer AI systems that have been debiased during development.

1. AI ethics 살펴보기
디지털애널리틱스협동과정 4기 유용상
2022.09.20
: 자연어처리를 중심으로

2. What are AI ethics?
AI ethics is a set of guidelines that advise on the
design and outcomes of artificial intelligence.

3. People can be harmed!
IQ Classifier “AI Gaydar” Missing racial sensitivity

4. Intersection of Ethics & NLP
• Algorithmic bias
: social bias in data& NLP models
• Incivility
: Hate-speech, microaggresion
• Privacy violation
: Privacy violation & language-based profiling
• Misinformation
: Fake news, information manipulation
• Technological divide
: Unfair technologies, underperforming for minorities(dialects, developing countries)

5. Debiasing word embeddings
Biased word embeddings
Identify gender direction

6. Debiasing word embeddings
neutralize
Babysitter Engineer
Grandmother
Grandfather
Woman
Man

7. Debiasing word embeddings
Babysitter
Engineer
Grandmother Grandfather
Woman Man
equalize

8. Future work & Discussion
• From reactive approach to proactive approach
: in data, modeling, evaluation and analysis
• How to deal with gray areas of ethics?
• Do humans Prefer Debiased AI?

9. Reference
• AI Ethics
:https://www.ibm.com/cloud/learn/ai-ethics#toc-ai-ethics--D7eeaGQW
• Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 16 - Social & Ethical
Considerations
: https://youtu.be/-Ldg4qFL6bU
• Man is to computer programmer as woman is to homemaker? debiasing word embeddings
:https://proceedings.neurips.cc/paper/2016/hash/a486cd07e4ac3d270571622f4f316ec5-
Abstract.html
• RNN W2L10 : Debiasing word embeddings
:https://youtu.be/fg8ijSPHyx0
• Do Humans Prefer Debiased AI Algorithms? A Case Study in Career Recommendation
:https://dl.acm.org/doi/abs/10.1145/3490099.3511108