263-5215-00L  Fairness, Explainability, and Accountability for Machine Learning

SemesterSpring Semester 2019
LecturersH. Heidari
Periodicitynon-recurring course
Language of instructionEnglish
CommentNumber of participants limited to 40.

The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the course, will officially fail the course.



Courses

NumberTitleHoursLecturers
263-5215-00 VFairness, Explainability, and Accountability for Machine Learning1 hrs
Wed09:15-10:00CAB G 59 »
H. Heidari
263-5215-00 PFairness, Explainability, and Accountability for Machine Learning2 hrs
Wed10:15-11:00CAB G 59 »
H. Heidari

Catalogue data

Abstract
Objective- Familiarize students with the ethical implications of applying Big Data and ML tools to socially-sensitive domains; teach them to think critically about these issues.
- Overview the long-established philosophical, sociological, and economic literature on these subjects.
- Provide students with a tool-box of technical solutions for addressing - at least partially - the ethical and societal issues of ML and Big data.
ContentAs ML continues to advance and make its way into different aspects of modern life, both the designers and users of the technology need to think seriously about its impact on individuals and society. We will study some of the ethical implications of applying ML tools to socially sensitive domains, such as employment, education, credit ledning, and criminal justice. We will discuss at length what it means for an algorithm to be fair; who should be held responsible when algorithmic decisions negatively impacts certain demographic groups or individuals; and last but not least, how algorithmic decisions can be explained to a non-technical audience. Throughout the course, we will focus on technical solutions that have been recently proposed by the ML community to tackle the above issues. We will critically discuss the advantages and shortcomings of these proposals in comparison with non-technical alternatives.
Prerequisites / NoticeStudents are expected to sufficient knowledge of ML (i.e. they must have taken the "Introduction to Machine Learning" or an equivalent course).

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersH. Heidari
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.
Additional information on mode of examination• 30% written mid-term exam
• 60% project (40% written report + 20% class presentation)
• 10% participation in class discussions

Learning materials

 
Main linkInformation
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

Places40 at the most
Waiting listuntil 02.03.2019

Offered in

ProgrammeSectionType
CAS in Computer ScienceFocus Courses and ElectivesWInformation
Data Science MasterCore ElectivesWInformation
Computer Science MasterFocus Elective Courses Visual ComputingWInformation
Computer Science MasterFocus Elective Courses Information SystemsWInformation
Computer Science MasterElective Focus Courses General StudiesWInformation