CS计算机代考程序代写 AI Ethics

Ethics
COMP90042
Natural Language Processing
Lecture 22
Semester 1 2021 Week 11 Jey Han Lau
COPYRIGHT 2021, THE UNIVERSITY OF MELBOURNE
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What is the right thing to do? Why?
What is Ethics?
How we ought to live — Socrates
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AI technology is increasingly being deployed to real-world applications
Why Should We Care?
Have real and tangible impact to people Whose responsibility is it when things go bad?
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Why Is Ethics Hard?
Often no objective truth, unlike sciences
A new philosophy student may ask whether fundamental ethical theories such as utilitarianism is right


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But unlikely a new physics student would question the laws of thermodynamics
In examining a problem, we need to think from different perspectives to justify our reasons
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Think more about the application you build ‣ Not just its performance
‣ Its social context
‣ Its impact to other people
‣ Unintended harms
Be a socially-responsible scientist or engineer

Learning Outcomes
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Arguments against ethical checks in NLP Core NLP ethics concepts
Group discussion
Outline
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Arguments Against

Ethical Checks in NLP
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A common argument when ethical checks or processes are introduced:
‣ Should there be limits to scientific research? Is it right to censor research?

Ethical procedures are common in other fields: medicine, biology, psychology, anthropology, etc
Should We Censor Science?
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In the past, this isn’t common in computer science But this doesn’t mean this shouldn’t change
Should We Censor Science?
Technology are increasingly being integrated into society; the research we do nowadays are likely to be more deployed than 20 years ago
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Ron Fouchier, a Dutch virologist, discovered how to make bird flu potentially more harmful in 2011
H5N1
Dutch government objected to publishing the research
Raised a lot of discussions and concerns National policies enacted
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Is it always better to publish sensitive research publicly?
Isn’t Transparency Always Better?
Argument: worse if they are done underground
If goal is to raise awareness, scientific publication isn’t the only way
‣ Could work with media to raise awareness ‣ Doesn’t require exposing the technique
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Exposing vulnerability publicly is desirable in cyber-security applications
‣ Easy for developer to fix the problem
But the same logic doesn’t always apply for AI
‣ Not easy to fix, once the technology is out

AI vs. Cybersecurity
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Core NLP Ethics Concepts
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Bias
Most ethics research in NLP focus in this aspect
A biased model is one that performs unfavourably against certain groups of users
‣ typically based on demographic features such as gender or ethnicity
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Bias Bias isn’t necessarily bad
‣ Guide the model to make informed decisions in the absence of more information
‣ Truly unbiased system = system that makes random decisions
‣ Bad when overwhelms evidence


Bias can arise from data, annotations, representations, models, or research design
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Bias in Word Embeddings
• Word Analogy (lecture 10):
‣ v(man) – v(woman) = v(king) – v(queen)
• But!
‣ v(man) – v(woman) = v(programmer) – v(homemaker) ‣ v(father) – v(mother) = v(doctor) – v(nurse)
‣ Word embeddings reflect and amplify gender stereotypes in society
‣ Lots of work done to reduce bias in word embeddings
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Every technology has a primary use, and unintended secondary consequences
‣ nuclear power, knives, electricity
‣ could be abused for things they are not
originally designed to do.

Since we do not know how people will use it, we need to be aware of this duality
Dual Use
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OpenAI developed GPT-2, a large language model trained on massive web data (lecture 1 demo)

GPT-2 also has amazing generation capability
‣ Can be easily fine-tuned to generate fake news, create propaganda
OpenAI GPT-2
Kickstarted the pretrained model paradigm in NLP
‣ Fine-tune pretrained models on downstream tasks (BERT lecture 11)
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Pretrained GPT-2 models released in stages over 9 months, starting with smaller models
OpenAI GPT-2
Collaborated with various organisations to study social implications of very large language models over this time
OpenAI’s effort is commendable, but this is voluntary
Further raises questions about self-regulation
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Privacy
Often conflated with anonymity
Privacy means nobody know I am doing something

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Anonymity means everyone know what I am doing, but not that it is me
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GDPR
• RegulationondataprivacyinEU
• Alsoaddressestransferofpersonaldata
• Aimtogiveindividualscontrolovertheirpersonaldata
• OrganisationsthatprocessEUcitizen’spersonaldata are subjected to it
• Organisationsneedtoanonymisedatasothatpeople cannot be identified
• Butwehavetechnologytode-identifyauthor attributes
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In 2006, AOL released anonymised search logs of users

Lawsuit filed against AOL
AOL Search Data Leak
Log contained sufficient information to de-identify individuals
‣ Through cross-referencing with phonebook listing an individual was identified
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Group discussion
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Prompts
• Primaryuse:doesitpromoteharmorsocialgood? • Bias?
• Dualuseconcerns?
• Privacyconcerns?Whatsortsofdatadoesituse? • Otherquestionstoconsider:
‣ Can it be weaponised against populations (e.g. facial recognition, location tracking)?
‣ Does it fit people into simple categories (e.g. gender and sexual orientation)?
‣ Does it create alternate sets of reality (e.g. fake news)?
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A model that predicts the prison sentence of an individual based on court documents
Automatic Prison Term Prediction
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A model that processes CV/resumes for a job to automatically filter candidates for interview
Automatic CV Processing
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Language Community Classification


A text classification tool that distinguishes LGBTQ from heterosexual language
Motivation: to understand how language used in the LGBTQ community differs from heterosexual community
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Take Away
Think about the applications you build
Be open-minded: ask questions, discuss with others
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NLP tasks aren’t always just technical problems
Remember that the application we build could change someone else’s life
We should strive to be a socially responsible engineer/scientist
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The Elements of Moral Philosophy by James and Stuart Rachels
Readings (Optional)
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