ETHICAL AND SOCIAL ISSUES IN NLP

Question 1

Negative uses of NLP

Answer 1

Profiling of users?
Generating harmful tweets/comments?
Propaganda?
Manipulation and framing?

Question 2

Using the right training data

Answer 2

Incomprehensible training data – not fully disclosed, explored or understood
- The entire Web?
- A large Twitter dataset collected by a single keyword?
eg ChatGPT is only based on data in the US

Size doesn’t guarantee diversity
Static data vs changing social views

Question 3

Data bias

Answer 3

AI is trained on biased data
Racially, gendered, ethically
All data is biased
also depending on how the data is annotated

Question 4

Ethical issue with data

Answer 4

Data have significant implications
- Is the data representative? What about bias?
- Is the data appropriate for the task?
- How was the data labeled?
- Copyright? Private data?

Question 5

AI Data Pollution

Answer 5

We could run out of data to train AI
It’s going to get trickier to find good-quality, guaranteed AI-free training data

Question 6

Issues with testing and validation

Answer 6

Instead of just accuracy:

Systematic evaluation needed
- What is the value? Can the model do harm?
- How feasible it is to deploy it?
- Validation should lead to trust and confidence

Question 7

LLM Risks

Answer 7

• Discrimination, hate speech and
  exclusion
• Information hazards (privacy)
• Misinformation
• Malicious Use (Fraud)
• Human-Computer interaction
  harms (stereotypes)
• Environmental and socioeconomic
  harms (hurt creative economies?)

Question 9

Environmental impact

Answer 9

Training a single BERT base model (without hyperparameter tuning) on GPUs was estimated to require as much energy as a trans-American flight

Question 10

Geopolitical impact

Answer 10

Counties battling to create the best AI
Importance of having the leading AI

Question 11

Resource challenges

Answer 11

Computational infrastructure
- Who can afford these? Only the selected ones?
- Training large models is challenging and very costly
- Running costs

Right staff in right places?

Right partnerships?

Question 12

Regulations

Answer 12

We need an agreed regulatory framework

We need agreed ethical and validation framework(s)

Transparency

Question 13

What is pre-mortem instead of post-mortem

Answer 13

Consider known and try to understand unknown risks and limitations of new product/project before it has been even designed