INFO901
Introduction to AI Ethics

This graduate course introduces the fast evolving interdisciplinary research area of Artificial Intelligence (AI) Ethics to doctoral students who are interested in either AI as a computer science discipline or students interested in researching the societal and personal impact of AI technologies introduced in society.

Schedule week 10


Day/Period
I
9:30-10:30
II
10:45-11:30
III
11:45-12:30
IV
13:30-14:15
V
14:30-15:30
Monday Course Organisation Module 1: Intro AI
Slides
Module 1: Intro AI
Slides

L
U
N
C
H

Module 1: Intro AI
Slides
Module 1: Intro AI
Slides
Tuesday Reading for Module 2
Wednesday Module 1:AI and decisions
Slides
Module 2:Cancelled Module 2:Cancelled Module 2:Cancelled Module 2:summary
Thursday Reading for Module 3
Friday "What is Privacy?"
by
Tobias Matzner
Introduction to differential privacy
by
Fedor Fomin
(Slides)
Privacy and the law
by
Malgorzata Agnieszka Cyndecka
(Slides)
Overview of relevant legal requirements for AI development
by
Kari Laurmann
(Slides)
Module 3:summary
Saturday Reading for Module 4

Schedule week 11


Day/Period
I
9:30-10:30
II
10:45-11:30
III
11:45-12:30
IV
13:30-14:15
V
14:30-15:30
Monday What is an explanation?
by

Tim Miller
Explainable to whom?
by
Alexander Kempton
and
Polyxeni Vassilakopoulou
(Slides)
How machines explain - an introduction to concepts of XAI
by
Inga Strümke
(Slides)

L
U
N
C
H

Optional
Overview of XAI and Fairness Tutorials for people with programming skills
Module 4:summary
Tuesday Reading for Module 5
Wednesday Fairness and Ai
by
Maja Van Der Velden
(Slides)
Why eliminate discrimination?
by
Kristoffer Chelsom Vogt
(Slides)
Practicalities of conducting fairness assessments on ML models
by
Robindra Prabhu
(NAV)
Debiasing algorithms
by
Marija Slavkovik
(Slides)
Module 5:summary
Thursday Reading for Module 6
Friday Overview of algorithmic accountability
by
Maranke Wieringa
AI accountability in practice
by
Alexander Kempton
and
Polyxeni Vassilakopoulou
AI accountability in practice
by
Alexander Kempton
and
Polyxeni Vassilakopoulou
Discussing exam Transparency
by
Nick Diakopolous
(Slides)
Saturday

Description

This is a detailed description of the modules of the course. Most modules and lectures have a required reading material before class. Make sure you read the texts. If you have a problem accessing some literure, email the course organisers. The lectures will not be recorded. Slides will be made avaialble after the lecture. The references listed under Other material are intended for your further reading, should you want to engage more with the topic.

Module 1: An Introduction to Artificial Intelligence

This module is a crash course into artificial intelligence. We will start with a very brief history of the field, and cover the basic concepts of reasoning, machiene learning, knowledge representation and computational agents.

Module 2: Power and Politics in AI

Module 3: Privacy and AI

This module introduces some of the basic concept of privacy that are relevant for AI.
Lecture 1: What is privacy?

Lecture 2: Introduction to differential privacy Lecture 3: Privacy and the law
  • Lecturer: Malgorzata Agnieszka Cyndecka
  • Abstract: This lecture gives an introduction to the GDPR covering objectives, material and geographical scope, main actors and notions, principles relating to processing of personal data, legal basis for processing of personal data, rights of the data subject, GDPR and risk enforcement.
  • Date: March 11, 2022(Friday)
  • Slots: 11:45-12:30
  • Nota bene: This lecture is a recorded video.
  • Reading before class: Chapters 2 and 3 of the GDPR
  • Link to the video.

Lecture 4: Overview of relevant legal requirements for AI development

Exam

The exam for INFO901 consists in executing, and writing a report, on a project with a topic from AI Ethics connected to at least one of the lectures in the course. The project can be done individually or in pairs. The project should contain research work approximately equivalent to one conference paper. The report should follow the structure of a conference or a journal article:

  • Introduction
    Should include the research problem, hypothesis or topic. Motivation of this problem/hypothesi/topic including grounding in related work. A short description of methodology used (if applicable), scope and success criteria (if applicable). A contribution: how this work advances the state of the art in AI Ethics (and which field are you aspiring to contribute to with this work). Link to a code and/or data repository (if relevant).
  • Preliminaries
    All the relevant information from other work that are necessary to be known in other for your project to be understood by the reader
  • Related work
    Either as second or penultimate section. Describe work that addresses similar research problem, hypothesis or topic as yours. Describe the similarities and differences with your work.
  • 1-3 Chapters of reporting on work, results or argumentation
  • Conclusions
    Outline how research problem, hypothesis or topic has been addressed. Outline directions for future work.

The project reports should be between 10 and 15 pages (excluding references) formatted following one of the templates:

Preferably the projects should be written in English. Norwegian is also allowed. If you want to write in another language, you should secure the availability of a mentor fluent in that language.

The students are free to publish the reports as research articles to the venue of their choice.

Deadline for submitting the report: June 10th, 2022

Office hours with the course responsibles: scheduled by need. The method for submiting the proposal will be specified with the topic aproval notification,

Selection of topics

The students should develop a project research problem, hypothesis or topic and submit it for approval by April 4, 2022 by email to marija.slavkovik@uib.no and miriag@ifi.uio.no. Use subject "INFO901 topic for approval".
The proposal should not exceed one page and include:

  • Working Title
  • Either a research problem, hypothesis or topic
  • At least one related research article
  • Which class from the course is the project related to
  • A short declaration of aspired contribution (and to which field)
  • Planned methodology
All project proposals that satisfy basic feasibility and connectedness to the course will be approved.

Course organisers

Miria Grisot Miria Grisot is an associate professor at the Digitalisation section at the University of Oslo. She works in Information Infrastructures, Digital Infrastructures, Digital Ecosystems, Digitalisation of work, Design of Information Infrastructures, Healthcare, remote care, AI in context, data work, CSCW, and collaborative work. Her publications and project envolvement can be found here.

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Marija Slavkovik Marija Slavkovik is an Professor at the Department of Information Science and Media Studies at the University of Bergen. Her area of expertise is collective reasoning and decision making. She has been doing research in machine ethics since 2012, in particular on formalising ethical decision-making . At present her interest is in machine meta-ethics and problems of reconciling different requirements of various stakeholders in what ethical behaviour should an artificial agent follow in a given context. The details of activities and full list of publications can be found here.