- From: Stephen Fancsali <sfancsali@xxxxxxxxx>
- To: edm-announce@xxxxxxxxxxxxx
- Date: Fri, 15 May 2020 10:42:48 -0400
[on behalf of Nigel Bosch]
* EXTENDED DEADLINE *
Fairness, Accountability, and Transparency in Educational Data (FATED): A
1-day workshop colocated with the EDM 2020 conference.
* Apologies for cross-posting *
Call for Papers
Fairness, Accountability, and Transparency in Educational Data
To be hosted online, 10 July 2020
Held in conjunction with Educational Data Mining (EDM) 2020
10-13 July 2020, fully virtual (formerly Ifrane, Morocco)
Submission deadline: 15 June 2020 (extended) (23:59 AoE – Anywhere on Earth)
Notification of acceptance: 1 June 2020
Camera-ready version due: 30 June 2020
Workshop date: 10 July 2020
Anywhere on Earth (AoE) is 12 hours behind Coordinated Universal Time (UTC).
All models are imperfect and as recent experiences have shown, educational
applications of AI are not immune to the risks observed in other domains.
Teachers, students, and parents have protested the use of educational
systems in classrooms across the US, driven in part by a lack of clear
guidance on how the systems are managed, and a lack of clear criteria on
how they can be judged (NEP 2018, Bowles 2019, Herold 2019). In complex
educational environments problems such as bias can go undetected in real
time and may, over the long term and large scale, far outweigh the
potential benefits (Buckingham Shum 2018).
The goal of this workshop is to develop a focus on Fairness,
Accountability, & Transparency in Educational Data (FATED). This will
include discussion of open issues in EDM, and prior research on fairness
accountability and transparency in machine learning systems. It will also
include the presentation of novel peer-reviewed research by the EDM
community, and a practical tutorial on some methods for assessing bias in
trained models. Finally we will work to develop near and long-term goals
for the community.
Topics of Interest
Complex educational environments raise a number of crucial ethical,
research, and policy issues. We invite papers that address questions such
as, but not limited to:
– How do we identify and ameliorate bias in our algorithms?
– How do we address the problem identified by Barocas & Hardt in their
NeurIPS 2017 tutorial: “Different models with the same reported accuracy
can have a very different distribution of error across population”?
– How do we design systems that are accountable to social and policy
concerns (Kroll et al. 2017)?
– How do we ensure student privacy (e.g. Aggrawal and Yu 2008, Dwork
– How do we ensure fairness in educational systems (Holstein & Doroudi
2019, Holstein et al. 2019)?
– How do we address the inevitable ethical challenges (Holmes et al.
2018, Ben-Porath & Ben Shahar 2017)?
– And who really owns the data (Lynch 2017)?
Short position papers
Between 2 and 3 pages, EDM format (including references)
Between 3 and 5 pages, EDM format (including references)
Submissions can be made through EasyChair:
* Nigel Bosch (University of Illinois at Urbana-Champaign)
* Christopher Brooks (University of Michigan)
* Shayan Doroudi (University of California Irvine)
* Josh Gardner (University of Washington)
* Kenneth Holstein (Carnegie Mellon University)
* Andrew Lan (University of Massachusetts at Amherst)
* Collin Lynch (North Carolina State University)
* Beverly Park Woolf (University of Massachusetts at Amherst)
* Mykola Pechenizkiy (Eindhoven University of Technology, The Netherlands)
* Steven Ritter (Carnegie Learning)
* Jill-Jênn Vie (Inria Lille, France)
* Renzhe Yu (University of California Irvine)
Web page: https://fatedm.inria.fr/
Other related posts:
- » [edm-announce] Extended Deadline: Fairness, Accountability, and Transparency in Educational Data (FATED) Workshop – Stephen Fancsali