---
title: Ethics
---



coded biases doco
# Ethics
## 1 Case studies
1. [facial recognition in US riots 2021-01-06](content/notes/facial-recognition-in-us-riots-2021-01-06.md)
2. [Anti govt protest china](content/notes/anti-govt-protest-china.md)
3. [How is safe enough for autonomous vehicles](content/notes/how-is-safe-enough-for-autonomous-vehicles.md)

### 1.1 Differences 1 vs 2
Govt vs vigilante

my judgements contain additionl context
	e.g., pro-democratic vs anti
	
world contains vast differences
	how systems of laws work
	extent of civil liberties afforded to individuals

### 1.2 Discussion
When developing a technology you dont know what is could be used for



## 2 Ethical handling of data
- Data moves very quickly due to computerised systems
- privacy act 2020
- its unethical to ignore potential security problems
	- df

## 3 Ethical handling of bias and errors, e.g., in AI
- large datasets oftenb incdlude bias and errors
	- to AI trained on these datasets with also be biased
	- e.g., facial recognition trining overrepresenting white males
- ML algorithgms are often opqaue
	- its not possible to understand how decisions are reached
	- makes asessing suitability of AI for a use case difficult
- explainable AI
- attacks e.g., 'trapdoors' within ML training data 

## 4 False or misleading claims
- pressure to release can lead to false claims
	- are features fully tested
- need to assess risks of bias
- e.g., AWS uptime information
	- rumoured that service status colour is n management decision
	- 

## 5 Your responsibility
- dont stay silent

## 6 Professional reponsibilities
- comp science per se lacks profressional standards
- there are some prefessional bodies which encoede responsibilities
	- ACM coc
	- IEEE coc
	- neither are specific to NZ
- Within NZ must consider treaty obligations