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AI > Artificial Intelligence > Flashcards

Artificial Intelligence Flashcards

1
Q

May 2016 crash

A

First person killed - travelling in auto-pilot mode

- Tesla did not assume responsibility

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2
Q

March 2018

A

First pedestrian killed

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3
Q

Giuseppe Contissa

A

Self-driving cars should be equipped with an ‘ethical knob’

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4
Q

Jan Gogoll

A

Everyone’s cars should have the same ethical settings

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5
Q

Advantage of giving people a degree of choice for moral settings?

A

Can hold them responsible for the outcomes more easily

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6
Q

Utilitarian ethics

A

Maximising overall happiness

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7
Q

Kantian ethics

A

Applying a basic set of principles to serve as universal laws

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8
Q

Virtue ethics

A

Fully realising a basic set of virtues

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9
Q

Contractualistic ethics

A

Formulating guidelines people would be willing to adopt

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10
Q

Gurney’s theory

A

Computer equipped to make utilitarian calculations might take into account that people prefer to drive in cars that save themselves, so therefore if more people use cars –> overall # deaths decrease –> overall maximum happiness

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11
Q

Hevelke and Nida Rumelin - what to do since it’s unfair to hold drivers responsible

A

Unfair to hold drivers responsible

  • Unfair moral luck
  • Should instead hold users collectively responsible for the risks they introduce as a group into society (NB retribution gap)
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12
Q

Can agency be transferred to a car?

A

Mindell: Always supervised by humans to some degree

- Can’t act on beliefs and desires

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13
Q

‘Mixed traffic argument’

A

People have a duty to switch to the safer alternative, or use added safety precautions e.g. speed limiters and alcohol locks

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14
Q

NZ car regulations (2)

A

LTA s 22: Driver must stop and give assistance
Land Transport Rule: Drivers must not exceed speed limits
- Also offences such as ‘operate’

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15
Q

What is AI? (Michael Scherer)

A

Machines that are capable of performing tasks that, if performed by a human, would be said to require intelligence

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16
Q

Four categories of AI

A

Thinking humanely, acting humanely, thinking rationally, acting rationally

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17
Q

Problem with autonomy

A

Reduces human labour

- Forces disruptive changes to the law and legal system

18
Q

Problem with foreseeability

A

Can’t predict the future
Machine-learning
Humans bound by cognitive limitations - can’t see all the options in time constraint so settle for satisfactory option
- Might not be able to hold designers liable if they didn’t predict the actions

19
Q

Research and development

A

Discreetness: can be conducted with limited visible infrastructure
Diffuseness: can work on it from multiple locations
Discreteness: can design without coordination/replicate code
Opacity: inner workers may be secret
+ low cost

20
Q

How to solve discreteness and opacity?

A

Apportion liability and demand publication of the code

21
Q

How to solve diffuseness and discreetness?

A

More difficult.

- Likely to be large visible corporations so not a huge worry but could be a problem in the future

22
Q

Why should a legislature influence policy?

A

Democratic, freedom, resources; but

Lack of expertise and limited time

23
Q

Why should an agency influence policy?

A

Tailor-made to a specific problem, flexible, specialised, ex-ante; but
Legislatures scared to give agencies too much freedom + who is an AI expert?

24
Q

Why should the courts influence policy?

A

Reactive, and focus on the facts; but

Common law moves slowly and they don’t focus on broad social considerations

25
Q

Artificial Intelligence Development Act

A

Creates an agency assigned with the task of certifying safety of AI systems

  • Tort liability for certified
  • Strict liability for non-certified
26
Q

How does the AIDA agency serve as a middle ground?

A

Not as coercive as a regulatory regime, but provides a strong incentive for AI developers to incorporate safety features.

27
Q

Less interventionalist method than AIDA agency?

A
  1. Government entity to conduct safety research - tort rules for people who ignore it
  2. Private insurance
28
Q

Examples of discrimination/bias in machine learning

A

Google’s image recognition algorithm - labelled black people as “Gorillas”

29
Q

Definition of singularity

A

The fear that we may develop an algorithm capable of recursive self-improvement

30
Q

Pros and cons of state regulation for algorithms

A

Pros: competition between states that produce a race to optimal legal rules
Cons: algorithm regulation is a national problem

31
Q

Pros and cons of federal regulation for algorithms

A

Pros: Extensive expertise, comprehensive policy, quick to respond, can look holistically
Cons: tunnel vision, focus on key political issues

32
Q

If there was a federal agency, what features should it have?

A
  1. Ex-ante regulation
  2. Broad mandate to ensure dangerous algorithms aren’t released
  3. Ultimate authority over safety regardless of the type of products
33
Q

Comparison between drugs and algorithms

A

Crisis after misbranded foods and drugs were being sold

- Not many labelling requirements

34
Q

How could AI benefit NZ?

A

Improve productivity
Make sense of large amounts of data
Improve decision making
Increase GDP by $54B

35
Q

Individual rights models

A

Right to privacy, right to an explanation, right to a human decision maker etc

36
Q

Official Information Act

A

S 23(1): Person has the right to be given a written statement for the reasons for the decision or recommendation

37
Q

Why is s 23(1) OIA problematic?

A

Individuals can’t make sense of the algorithm
May not be aware you are subject to an algorithmic decision
Can only get access to your own data

38
Q

Privacy Act, principle 8

A

Accuracy of personal information to be checked before use

39
Q

Case Note case

A

A manual notation has to be added to the record to comply with principle 8 of the Privacy Act

40
Q

PHRaE framework in NZ

A

Checklist for data scientists thinking of using predictive analytical tools
Ensures privacy, human rights and ethics are taken into account

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