100% satisfaction guarantee Immediately available after payment Both online and in PDF No strings attached
logo-home
Lecture notes AI & Society: Fixing Algorithmic Decision Making (S_AIS) $7.49   Add to cart

Class notes

Lecture notes AI & Society: Fixing Algorithmic Decision Making (S_AIS)

 68 views  3 purchases
  • Course
  • Institution

All lecture notes you need for the midterm exam. Guestlectures are also included.

Preview 3 out of 26  pages

  • May 2, 2023
  • 26
  • 2022/2023
  • Class notes
  • Andreu casas
  • 1 t/m 6
avatar-seller
Aantekeningen colleges – AI & Society

College 1: Introduction

Hallucination in AI  coming up with stuff that seems plausible
- Most of the articles made up by AI are really specific on certain things

What is AI?
Machine learning
 Detection of systematic patterns between input and output
 General task  predict output given specific features of the inputs

 Very similar to “regular” statistical modeling
- Input features  independent variables
- Output class  dependent variable
- (In fact, neutral networks can be seen as a form of logistic regression models)

 Key differences to statistical modeling
- We care about predicting something, not about understanding a (causal)process
- Models are highly complex (and multicollinear) and generally seen as black box

Notes at slides:
 Tegenwoording gaat het bij automatische inhoudsanalyse echter eigenlijk altijd om
machine learning
 Bij machine learning gebruiken we een beparkte set handmatig gecodeerde data om een
model te trainen dat kan generaliseren naar nieuwe data
 Kort gezegd: we hebben een verzameling teksten die we als trainingsdata willen
gebruiken, zoals politieke tweets over klimaatverandering
 Deze laten we handmatig coderen, bijvoorbeeld of de tweet voor of tegen ene strenger
klimaatbeleid is
 Dit geeft de computer voorbeelden van teksten die voor of tegen klimaatbeleid zijn
 Dit elvert een model op, dat we vervolgens kunnen gebruiken om automatisch nieuwe
teksten te coderen
 De studentcodeur is dus nog niet afgeschadt, maar door machine learning is het niet
meer nodig om alle items handmatig te analyseren
 De laatste jaren zijn hierin enorme stappen voorwaarts gezet in wat wel deep learning
genoemd wordt

Deep learning
 fancy term for machine learning with very large models

Based on:
- Very large neutral networks
- Trained on enormous amounts of data, ‘all of the internet’
- Using massive computing power, especially of GPU’s

Key innovations:
- Feature layers find patterns in raw input
- Networks can be (pre-)trained based on unannotated data
- Patterns from (pre-)training are transferred to actual task, and fine-tunned on annotated
data




Natural language processing

,  Core application area of AI
 Research field of NLP or Computational Linguistics

 Can the computer understand, generate, or translate text?
 Generally cast as machine learning problems:
- Predict specific meaning given text
- Generate (=predict most likely) newspaper article given prompt

Recent innovations all based on ‘BERT’  decoder-encoder architecture that builds layers of
understanding training using tasks such as ‘predict the next word’

Robotics
 Embodied intelligence  AI used to navigate/understand and manipulate environment
- Industrial robots
- Automotive AI
- Conversational/care robots

AI in a complex world
1. Scale of digitization  digital traces as research objects
2. AI/ Digitization build on existing technologies
3. Global reach of AI/digitization
4. Ubiquity of AI/digitization
5. Increasing complexity of digitized/AI-systems
6. Intrusiveness of digital/AI technology

Ethics of AI: main debates
Privacy, manipulation, opacity, bias, autonomy, singularity

Example  facial recognition technology is used in many parts of China nowadays that tracks the
emotions of people, and you can only enter some metro stations if your past behavior has gotten
a high score in the nationwide system of data-sharing
- What is the role of privacy in the face of machine learning?
- How do anonymous data dude from decisions based on personal data?
- Is the GDPR a good and sufficient instrument?

Ai & Journalism (JMCQ 2019 invited forum)
Meredith Broussard
- Center humans, consider economics, explain what AI is or isn’t
- Not what can AI do, but what should AI do

Nicholas Diakopoulos
- AI as a medium to express journalistic values, value-centered design
- Need to study human centered (hybridized) AI

Andrea Guzman
- Need to cross divides in research (and applications)
- Disciplinary divides, technological divides, theoretical divides
- Human machine communication as research agenda

Rediet Abebe
- Biases or limitations often discriminatory
- Need for inclusion/representation of communities

Michel Dupagne & Ching-Hua Chan
- More research into fear of and resistance to technology
- Need to adapt education to changing reality
Conclusions

,  AI is a group of techniques clustered around machine learning

 Deep neutral networks can achieve spectacular results
- Text/image generation, understanding, translation, decision making

 AI associated with various problems
- Bias
- Inequality

 Regulating/fixing AI requires a deep understanding of…
- What Ai is
- How it affects (various aspects of) society
- How solutions/regulation interacts with technology, users, owners, society

 Interdisciplinary research and solutions are key

The benefits of buying summaries with Stuvia:

Guaranteed quality through customer reviews

Guaranteed quality through customer reviews

Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.

Quick and easy check-out

Quick and easy check-out

You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.

Focus on what matters

Focus on what matters

Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!

Frequently asked questions

What do I get when I buy this document?

You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.

Satisfaction guarantee: how does it work?

Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.

Who am I buying these notes from?

Stuvia is a marketplace, so you are not buying this document from us, but from seller mvandergreft. Stuvia facilitates payment to the seller.

Will I be stuck with a subscription?

No, you only buy these notes for $7.49. You're not tied to anything after your purchase.

Can Stuvia be trusted?

4.6 stars on Google & Trustpilot (+1000 reviews)

80202 documents were sold in the last 30 days

Founded in 2010, the go-to place to buy study notes for 14 years now

Start selling
$7.49  3x  sold
  • (0)
  Add to cart