Department of Computer Science CSCI 5622: Machine Learning Chenhao - - PowerPoint PPT Presentation

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Department of Computer Science CSCI 5622: Machine Learning Chenhao - - PowerPoint PPT Presentation

May 10, 2023 410 likes •780 views

Department of Computer Science CSCI 5622: Machine Learning Chenhao Tan Lecture 23: Machine learning and society Slides adapted from Chris Ketelsen 1 Learning objectives Learn about the connection between our society and machine learning

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Department of Computer Science CSCI 5622: Machine Learning Chenhao Tan Lecture 23: Machine learning and society Slides adapted from Chris Ketelsen

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Learning objectives

  • Learn about the connection between our society

and machine learning

  • Make sure that you think about ethics when

applying machine learning

  • Fill in FCQ

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Now you understand the magic behind machine learning!

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Now you understand the magic behind machine learning!

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Machine learning in real life

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Machine learning are commonly used in

  • ur society

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Brainstorm

Where did you see machine learning today?

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Machine learning and our society

  • Machine learning are increasingly connected with
  • ur society
  • Authorizing credit
  • Sentencing guidelines
  • Suggesting medical treatment

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And many more!

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Ethical issues in Machine Learning

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https://www.propublica.org/article/machine-bias-risk- assessments-in-criminal-sentencing

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Case study: Decision making

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https://www.propublica.org/article/m achine-bias-risk-assessments-in- criminal-sentencing

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Case study: Decision making

  • Crime prediction, similar situations arise when

someone is getting a loan

  • Harms of allocation

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https://www.propublica.org/article/machine-bias-risk- assessments-in-criminal-sentencing

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Case study: Decision making

  • Crime prediction, similar situations arise when

someone is getting a loan

  • Harms of allocation
  • What are potential reasons in the machine

learning pipeline?

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https://www.propublica.org/article/machine-bias-risk- assessments-in-criminal-sentencing

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Case study: Word embeddings

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Case study: Word embeddings

Figure credit: Lior Shkiller

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Case study: Word embeddings

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Bolukbasi et al. 2016, Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

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Case study: Word embeddings

SVD to rescue

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Bolukbasi et al. 2016, Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

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@mmantyla

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Case study: Recommender Systems

Filter bubble [Pariser, 2011]

  • Main idea: Personalized search determines what

information you see and what information you don’t see

  • Google, Facebook, Netflix

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Case study: Recommender Systems

  • Blue feed vs Red feed

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http://graphics.wsj.com/blue-feed-red-feed/

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Case study: Recommender Systems

Filter bubble [Pariser, 2011]

  • Main idea: Personalized search determines what

information you see and what information you don’t see

  • Google, Facebook, Netflix
  • Debate: a good thing or a bad thing?

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Case study: Recommender Systems

  • Bakshy, Messing, Adamic, “Exposure to

ideologically diverse news and opinion on Facebook”

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Case study: Recommender Systems

Bakshy, Messing, Adamic, “Exposure to ideologically diverse news and opinion on Facebook”

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Case study: Recommender Systems

  • Latanya Sweeney 2013, “Discrimination in Online

Ad Delivery”

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Case study: Recommender Systems

  • Latanya Sweeney 2013, “Discrimination in Online

Ad Delivery”

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Credit: @math_rachel, Kate Crawford

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Representation harms

  • Denigration
  • Stereotype
  • Recognition
  • Under-representation

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Credit: Solon Barocas, Kate Crawford, Aaron Shapiro, Hanna Wallach

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Case study: Physical Systems

Self-driving cars

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Case study: Physical Systems

Self-driving cars

  • In the event of an inevitable crash leading to likely

loss of life, what should the car do?

  • Example: Car crash will either results in death of
  • Driver and several passengers
  • Several pedestrians
  • Debate: How do we choose?

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At least think about the following question

  • What question can/should we ask?
  • What data is OK to use?
  • What is the boundary between private data and public

data?

  • If something is public, should you use the data?
  • Example: Enron email corpus
  • Who owns your data?
  • What anonymization should be done and is the data

*really* anonymized?

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Plug

  • Thinking more about
  • Why we develop machine learning systems
  • How we develop machine learning systems for

humans

Human-centered Machine Learning in Spring 18 (CS 7000)

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Thanks! Zhenguo Chen Sean Harrison Tyler Scott Most importantly, all of you!

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FCQ time! https://colorado.campuslabs.com/courseeval/