# Lecture - Ethics and Responsible Innovation MT22, I

> Source: https://ollybritton.com/notes/uni/prelims/mt22/ethics-and-responsible-innovation/lectures/1/ · Updated: 2022-11-11 · Tags: uni, lecture

- [Course - Ethics and Responsible Innovation MT22](https://ollybritton.com/notes/uni/prelims/mt22/ethics-and-responsible-innovation/)

### Notes
- Course Structure
	- 4 interactivel lectures
	- 2 practical sessions during Hilary (2 hours)
	- Written assesment (S+/S/S-)
- Types of harms
	- Deliberate harms
	- Accidental harms, i.e. unintentional side effects
- Algorithmic bias - systematic and repeatable erros in a computer system that reate unfair outcomes, such as privileging one arbitrary group of users over others.
	- Allocative harms - when a system provides different groups unequal opportunities, resources or capabilities
		- Occur a lot in ML systems
		- Often caused by dataset sample bias
		- E.g. voice recognition or face recognition disproportionally performing worse on minorities
	- Representational harms - algorithmically curated or created depiction that is dsicriminatory or otherwise harmful
		- E.g. google showing white men when you google CEO
- Algorithmic decision making
	- Support human decision making = decision support
		- CV screening
	- Making decisions in place of humans = automatied decision making
		- Fraud detection
	- Not always a bad thing -- humans have bias too and so automated systems can sometimes do a better job
	- Reasons for concern
		- Sample bias
		- Feature horizon - not seeing everything that might be relevant
		- Falibility of human judgement - human biases might be baked in
		- Inscrutability - inability to easily tell what the model is really learning and using for inference
		- Feedback loops

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