CS代考 AI and Ethics Outline – cscodehelp代写

AI and Ethics Outline
 This week we talk about AI and Ethics
 Why is this topic important
 Regulatory and legal constraints
 Ethical dimensions
Fairness (non-bias), transparency, explanation, rectification
 Features of this domain
 Deciding what we ourselves should do in specific situations.
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Why is it important to consider ethics?
 Most technologies have good and evil applications
 As engineers we owe a duty to our society to consider the ethics of our work
– eg, British Computer Society Code of Conduct
 With AI, there are particular aspects we need to consider
– Algorithms may be learnt, so that even the software developers do not
know what they do or how.
– Many machine learning methods are “black boxes”
– Data may be biased
– There may be significant legal consequences to our design decisions.
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Trolley Problems
 Thought experiments in which we are faced with a moral dilemma – Often involve the control of a trolley (ie, a tram)
 For example:
– We are driving a car in the left land and we see a pedestrian in our lane in
front of us
• If we keep driving we will likely kill the pedestrian
– OR, we can swerve to the right lane, where there is a car heading towards us
• If we swerve to the right lane, we will hit the car and this may kill us and the people in the car
– What should we do?
– Would our answer be different if the numbers of people impacted were different?
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MIT Moral Machine Experiment
www.moralmachine.net
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Aside – Norms vs Rules
 As AI engineers, we could hard-code many rules
– Eg, the road rules that say we should always drive on the left
– Hard-coding would mean the self-driving vehicle could NEVER drive on the right
 But sometimes we need the car to use the other lane, even though it is against the law
– eg, Trolley Problems
 So, our usual solution is not to hard-code the rules as unbreakable constraints, but to code them as norms
– Norm: an accepted standard or way of behaving, which most people follow – How should a machine know when it should break a norm?
 Norms and their exceptions are studied extensively in AI – Particularly in AI and Law.
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Decisions often involve ethical trade-offs
 Trade-offs:
Lives lost as a result of one action-option
vs.
Lives lost as a result of another action-option
 Maybe also another trade-off:
Lives lost outside the car as a result of one action-option vs.
Lives lost inside the car as a result of another action-option
 What car would you purchase?
 What car would you design?
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Pressures from regulators
 Our Society is very concerned with various aspects of new technologies, such as AI
 AI is an important current focus of regulators, eg
– European Commission
• New regulations on AI coming in 2021
• GDPR (General Data Protection Regulations)
• MiFID2 (Markets in Financial Instruments Directive 2)
– UK likely to follow EU regulations for several years • UK Government Office for AI
– National regulators for data privacy and human rights, eg,
• UK Information Commissioners Office (ICO)
• Singapore Personal Data Protection Commission
• Australian Human Rights Commission (looking at rights under CCTV).
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Regulatory focus has been on
 Fairness (and elimination of bias)
– Systems should not be biased against particular groups
– People with protected characteristics (age, gender, religion, ethnicity, etc)
 Transparency
– Stakeholders should be able to see what input data is used, what processes or algorithms are used, what output data results, and what the intended and realized purposes are
 Explainability
– Automated decision-making systems should be able to explain their
decisions in a way that humans can understand
 Rectification
– Automated decisions should be able to be reversed
 Human involvement
– Are decisions mediated by humans in the loop
 Governance of AI systems
– Singapore Government Personal Data Protection Commission (PDPC) Model AI Governance Framework (Second Edition), released January 2020.
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Legal Aspects

How can responsibility be attributed?
AI has no separate legal personality and cannot be an inventor for patents
– Thaler v Comptroller-General of Patents, Designs and Trade Marks [2020] EWHC 2412
England: an automated system is not an agent, as “only a person with a mind can be an agent at law”
– Software Solutions Partners Ltd, R (on the application of) v HM Customs & Excise [2007] EWHC 971, at paragraph 67
USA: “a robot cannot be sued”
– United States of America v. Athlone Industries, Inc., 746 F.2d 977, 979 (3d Cir. 1984), U.S. Court of Appeals for the Third Circuit
Germany: machines and software cannot declare intent for purposes of contracting
– Federal Supreme Court, Judgment of 16 October 2012 – X ZR 37/12 Thanks to Norton Rose LP
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Civil liability: Analogies for causation by machines
USA: Cases relating to Auto-pilots in aircraft
– Claims against manufacturers or operators of planes with auto-pilot-enabled equipment
– Many claims have failed for lack of evidence of manufacturing defects or lack of proof of causation
England: law relating to escaping pets/animals
– Animals are, like other chattels, merely agents and instruments of damage, but they are also animate and automotive
– An owner of an animal – not the breeder who sells it to the owner – has legal responsibility for the actions of the animal
Germany, US and England:
– Some authorities suggest that, even though a contract may have been entered into automatically by software on behalf of a party, it might still be binding on that party.
Thanks to Norton Rose LP
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Judicial views of computer decision-making
“A mind of its own?”: Some courts are beginning to draw distinction between deterministic computers and AI:
 Deterministic systems: Systems that may be automated but are not autonomous – knowledge assessed at the time of programming and by reference to the programmer: B2C2 Ltd v Ltd [2019] SGHC(l) 3 (Singapore International Commercial Court)
 Autonomous systems: Would a court look to the opaque subroutines of the algorithm during subsequent system operation to determine knowledge?
 Probabilistic computing: Computing that is neither deterministic nor autonomous, but based on a probability that something is the correct answer. Quantum computing is an example. How would a court deal with probability outcomes?
Thanks to Norton Rose LP
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Explanations for decision trade-offs
 Imagine being a car driver and facing a difficult trade-off:
– Stay in left lane, and likely kill a pedestrian
– Move to right lane, and smash into an oncoming car
 You decide in the moment and end up in court
 You explain your decision, as best you can
– You decided in the spur of the moment
 The court may go down one level of explanation
– They may examine your state of mind at that moment
– Were you drunk? High on drugs?
– Were you angry or stressed?
– Were you insane?
Source: Wikipedia
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A self-driving car in court
 Same situation, but now the car was an autonomous vehicle
 The court will not accept a statement that the car made the decision in the spur of the moment
– Because the s/w developers had time to decide what to do in this situation
 The court will examine several layers down to find who or what was responsible, eg:
– How did the car-control program decide what to do?
– How did the s/w developers decide how to program the control software?
– What ethical principles did the s/w developers adhere to (explicit or implicit)?
– What ethical training had the s/w developers been given?
– What ethical policies had the car manufacturer or the company employing the developers had in place?
– Etc.
Photo credit: Google 2015
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In practice . . .

Typical data-driven machine learning process
 Bias can arise with history data, training data, test data, and new data
 Bias can be inserted by the learning process
 Bias can be inserted by the monitoring & feedback activities.
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Source: IBM

Examples of bias in AI systems
 AI tool for recruitment of software developers at Amazon
– Amazon gave up attempt after 3 years (2014-2017)
– Reported by Reuters 11 October 2018
Amazon scraps secret AI recruiting tool that showed bias against women | Reuters
 Automated Bank Loans in US Bank
– No loans given to people wearing head covering
– Hats may be a proxy for religious beliefs.
Source: Wikipedia
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 

Recall from Lecture 1: Data-driven vs. Model-driven AI
Data-driven approaches vs. model-driven approaches
Machine Learning/ Deep Learning are usually data-driven
• Patterns are found with no explanation as to why or what these mean
In model-driven approaches, the AI system has a model of the application domain
• For example, a causal model connecting causes with effects.
• Since Windows95, every version of Windows OS has a Bayesian Belief Network linking causes with effects in printer operations, to help diagnose the causes of printer problems.
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Identifying bias is difficult in data-driven systems
 We don’t know what factors were used to make the decisions or recommendations
 If the program undergoes evolution or learning, then the developers may not know what code results.
– Are the s/w developers responsible for the code in this case?
 Since we cannot control the output, we focus on what we can control – the production process
– Looking for bias in the input, training and test data
– Testing the algorithm for correctness (if we can)
– Looking at flows of data BETWEEN different AI systems
– Ensuring good AI Governance
 What comprises good governance for AI systems?
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Transparency
 In model-driven approaches, it is usually straightforward to see how a conclusion was reached by the AI
– We can follow through the IF-THEN rules or reason over the causal model
 In contrast, many data-driven approaches are dark (“black boxes”) – We cannot see how a conclusion was reached.
 To gain transparency, we may have to build a second AI to mimic the workings of the first
– A model-driven AI to mimic the workings of the data-driven AI.
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Explainability in Model-driven AI – usually straightforward
 Model-driven or symbolic approaches to AI are usually able to generate explanations
– Because they have a model of the application domain & are transparent
 For example: An Expert System comprising IF . . . THEN . . . Rules
– IF the patient has lost sense of smell THEN the patient could have CV19
– IF the patient has a new persistent cough THEN the patient could have CV19
– …
– IF the patient has all the above symptoms THEN the patient does have CV19
 We can create an explanation for a particular automated diagnosis from the particular IF-THEN rules invoked in the trace of that decision
 Similarly, for other model-driven AI, such as Bayesian Belief Networks. 22

Explainability in data-driven AI – usually difficult
 In AI methods that are data-driven, such as Neural Networks & Deep Learning methods, the machine is manipulating data without it knowing what the data means
 For example, an image classification program may identify faces by:
– Examining pixel colours in the image
– Using pixel colours to identify edges (eg, boundaries of the face)
– Linking edges together to identify shapes of parts in the image
– Comparing shapes in image to a library of shapes (eg, chins, ears, eyes)
– Creating composites of shapes to form faces
– Comparing faces in different images to find matching faces
 At no point, does the program have any understanding of what is a chin, or an ear, or a face.
 Very difficult to create an explanation for how the decision was reached – People don’t understand this description of the process.
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In addition
 Current Machine Learning and Deep Learning methods are still very immature
– The resulting systems are not robust to small changes in inputs
– This makes them easy to hack
 The data-driven approaches require lots of data
 For many situations we do not have enough data
– Particularly for edge cases and rare events (eg, maritime collisions).
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Autonomous Vehicles
Sequence of development of AVs:
 Autonomous aircraft (centralized control of airspace, data from isolated experiments)
 Then, autonomous road vehicles (data gained by experiments off-road)
 Lastly, autonomous ships (very little data, no centralized control of high seas).
Source: Rolls Royce
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AI Governance
 Companies are starting to put in place processes to govern the creation and deployment of AI systems
 Typically, this will involve a special internal AI Governance committee
– With representatives of different departments (eg, IT, Operations, Legal)
– In the best case, including 1-2 outsiders (to avoid “group think”)
– To vet potential AI projects and to oversee their deployment
 Modeled on the Pharmaceutical industry, where these committees are standard
 Companies are also adopting company-wide policies for use of AI
– Example: Vodafone AI Framework
www.vodafone.com/what-we-do/public-policy/policy-positions/artificial- intelligence-framework
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Singapore Model AI Governance Framework
 On 21 January 2020 the Singapore Personal Data Protection Commission (PDPC) released the second edition of the Model AI Governance Framework.
 The framework is a voluntary set of compliance and ethical principles and governance considerations and recommendations that can be adopted by organisations when deploying AI technologies at scale. It is not legally binding.
 The Model Framework is based on two high-level guiding principles:
– Organisations using AI in decision-making should ensure that the
decision-making process is explainable, transparent and fair; and
– AI solutions should be human-centric.
 The 2020 edition of the Framework includes real-life industry case studies demonstrating effective implementation of the AI Framework by organisations.
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Humans in the loop
 A key question is to what extent humans should be involved in automated decision-making processes
– Eg, London Underground – self-driving tube trains on 4 lines, but still have driver sitting in front
 Some regulations only apply to decision-making systems with no humans in the loop
– Eg, MiFID 2 regulations.
 The human role needs to be sincere (not just for show), or it is likely to be rejected by courts.
 The next slide is a diagram presented in the Singapore Model AI Governance Framework to help companies decide the extent of human involvement in AI decision-making processes.
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What level of human involvement is appropriate?
Source: Singapore PDPC Model AI Governance Framework: 29
Compendium of Use Cases, 2020.

Some ethical questions
 The tutorials for this week will include some ethical questions
– There are usually some answers that are definitely wrong
– There may be more than one answer that is right
– There may be some answers which are “grey”
 But what is right or wrong?
 Just following orders without question is never right
– This defence was not accepted in the War Crimes Tribunals after WW II in Nuremberg in November 1945 and in Tokyo in April 1946
 Some situations may require obtaining legal advice
 Many situations can be clarified by discussion (with bosses, colleagues, independent persons).
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Reconsidering your orders
“When faced with untenable alternatives you should consider your imperative.“
– Cain,
Galactica-type Battlestar Source: galactica.fandom.com
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AI and Ethics Summary
 This week we have talked about AI and Ethics
 Why is this topic important
– Trolley problems
 Regulatory and legal constraints
 Ethical dimensions
Fairness (non-bias), transparency, explanation, rectification
 Features of this domain
– ML sensitive, easy to hack
– Expert systems vs deep learning
– Humans in the loop
 Deciding what we ourselves should do in specific situations.
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Thankyou!