Fatma Elsafoury

Fatma Elsafoury

I'm a researcher at the Weizenbaum research institute

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Posts

Discrimination in AI and DeepFakes

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Introduction

Artificial intelligence (AI) algorithms are being used in our day-to-day tasks, from search engines, email filtering to content recommendation. AI media coverage can get us to believe in two scenarios. A positive scenario where AI automates all the tasks and solves all problems, and a doomsday scenario where AI takes over humanity [1]. However, this coverage rarely engages in a constructive conversation about the realistic dangers that come with AI and how AI might impact us in the context of society, politics, economics, gender, race, sexual-orientation, social class, and so on [1]. One aspect of AI’s impact on our societies is the consolidation of the existing power dynamics. Research has shown that AI-based systems are prone to reproducing harmful social biases [34]. Which in turn leads to the consolidation of dysfunctional social structures that favour historically advantaged people, like favouring men over women for STEM related jobs [2]. Deepfake videos and the AI systems that make them are another manifestation of this consolidation of power, with the potential risks they impose, especially on women.

Deepfake applications use off-the-shelf AI algorithms to generate fake content. AI algorithms like generative adversarial networks (GAN), variational autoencoders (VAE), and long short-term memory (LSTM) are used in training deepfake applications to swap the persons’ faces in two different videos, or to copy the facial expressions of one person in one video onto the person in the other video. The open-source deepfake applications, FaceSwap and DeepFaceLab, use VAE algorithms [5]. These deepfake applications, similar to other AI-based systems, don’t actually “learn” anything about the task they are supposed to do. But rather, they learn spurious correlations between a group of variables present in the training datasets [6]. AI-based companies claim that their systems make the right decisions, but with no guarantees that they do that for the right reasons. Hence, the term “Black box” is used to describe AI-based systems.

Guidelines to Figure-Eight (CrowdFlower) Platform

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How does Figure-Eight works?

Figure-Eight is based on the idea of creating millions of simple online tasks to create a distributed computing machine. The business model of Figure-Eight is composed on a customer, a task and a contributor. Figure-Eight provides tools for the customer to upload their data and design their task either by drag and drop or using Crow-Flower Mark up Language (CML). Then, the task became available for the contributors (crowd) to perform. Figure-Eight provides a set of tools to set the desired quality, speed and cost of the task. This section describes the mechanisms provided to achieve the most possible quality in the task.

Crowdsourcing

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What is crowdsourcing?

“Remember outsourcing? Sending jobs to India and China is so 2003. The new pool of cheap labor: everyday people using their spare cycles to create content, solve problems, even do corporate R & D.” By Jeff Howe

Spatial and Temporal analysis of Electoral violence of the Kenyan Elections 2008

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In Kenya, after the 2007 presidential elections, the country was drowned in cycles of intensive violence for two months, resulting in 1200 killed and 100,000 displaced (Brown & Sriram, 2012). Kenya is one of the limited case studies in the literature where satellite images were used to detect the locations of violent incidents. This case study can help us to use spatial analysis to understand the diffusion pattern of electoral violence in Kenya and which approach of the aforementioned diffusion approaches, escalation, relocation and violence legitimacy, is applied.

Spatial and Temporal analysis of Electoral violence of the Nigerian Elections 2003

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Nigeria in 2003 held more than one election in the same month.It started with the House of the representative and Senate elections on the same day, 12th of April 2003. Then the presidential election was held on 19th of April 2003. Then on the 3rd of May 2003, the National Assembly election was conducted. According to the data collected by HRW, violence started earlier in Delta state from January to March as it is the most oil-producing state in Nigeria. The violent incidents included clashes between different ethnic groups and other clashes between armed groups and security forces. The most violent incidents were on 12th and 19th of April and the 3rd of May (on election days(s) violence). In most of the incidents the ruling party was the main perpetrator. Some incidents started earlier on 10th and 11th and some took place during the first three days of May. The violence started on 10th of April in Bassambiri area in Bayelsa state in the form of clashing thugs belong to opponent parties. On the 11th different violent incidents took place in different areas (Amadi-amain in River state and Warri in Delta state) in the form of the temporary displacement of people from their homes and attacks waged by the opponent’s party’s supporters.

Whistling Vivaldi: How Stereotypes affect us and what we can do about them?

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I received the “Whistling Vivaldi: how stereotypes affect us and what we can do’’ book by Claude Steele as a Christmas gift and it was the best gift I got this year. The book explains, in a scientific way backed with experiments and publications, how negative stereotypes and stigmatizations related to certain groups influence the intellectual and physical ability of the people who belong to those groups. I found the book clarifying to me a lot of what I went through as a female Egyptian Ph.D. student in Computer Science in the UK. I could relate to the feelings, the struggles, and the experiences of the students who participated in the experiments described in the book. I finished the book in a week and decided to write this summary to spread knowledge.

activities

PhD Support Group Member

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  • Workshop: PhD support Group Sessions
  • Venue: Computing Science School, Glasgow University
  • Location: Glasgow, The UK
  • My responsibilities was to:
    • One of the founders/organisers of the mental health support group for PhD students at computing Science school, Glasgow University.
    • One of the runners of the weekly sessions to support the PhD students.

Python Instructor

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  • Workshop: Programming Workshop in Africa (PWA)
  • Venue: Ibadaan University
  • Location: Ibadaan, Nigeria
  • My responsibilities was to:
    • Preparing course material
    • Teaching Python (Basic programming)
    • Teaching Data science toolkit (Pandas, Numpy and MatplotLib)

Web Development Instructor

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  • Workshop: Code First Girls
  • Location: Glasgow, The UK
  • My responsibilities was to:
    • Teaching HTML, CSS, JavaScript, Bootstrap and JQuery
    • Teaching Github
    • Teaching UX-Design

Member of the students’ mental health advisory board

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  • Activity: Students’ mental health advisory board
  • Venue: The University of the West of Scotland
  • Location: Paisley, The UK
  • My responsibilities was to:
    • the advisory board meets once a month to discuss strategies to support the students’ mental health.

publications

struggles

Rejection 2020

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All the rejections I received in 2020:

  • ECIR 2021 conference

Rejections in 2021

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All the rejections I received in 2021:

  • WOAH, June 2021
  • IEEE Access, May 2021
  • ACM Surveys, Jan 2021

Rejections in 2022

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All the rejections I received in 2022:

  • ACL 2022
  • NAACL 2022
  • I applied for an internship at the FATE group, Microsoft Research, but did not hear back.
  • I applied for an internship at DeepMind Research, but did not hear back.
  • I applied for an internship at FAIR group, Facebook Research, but did not hear back.

Rejections in 2023

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All the rejections I received in 2023:

  • I submitted two papers to TACL and both of them got rejected with an option of re-submission after a year.
  • I applied for an internship at FAIR group, Facebook Research but did not get an interview.
  • Have a couple of interviews for an internship at Google but did not work out.

talks

teaching

Computer Programming: Python

Course lap, Glasgow University, Computing Science, 2017

My responsibilities was to:

  • Tutoring in lab
  • Exam invigilation
  • Code and exam marking
  • Supervising a software engineering master student

Java Programming

course lab, Glasgow University, Computing Science, 2018

My responsibilities was to:

  • Tutoring in lab
  • Exam invigilation
  • Code and exam marking
  • Supervising a software engineering master student

Computer Thinking: Alice

Course lab, Glasgow University, Computing Science, 2018

My responsibilities was to:

  • Tutoring in lab
  • Exam invigilation
  • Code and exam marking
  • Supervising a software engineering master student

IT Programming: Java

Course lab, Glasgow University, Computing Science, 2018

My responsibilities was to:

  • Tutoring in lab
  • Exam invigilation
  • Code and exam marking
  • Supervising a software engineering master student

Data science

Course lab, Dundee University, Computing Science, 2019

My responsibilities was to:

  • Helping students with machine learning and Python assignments.
  • Marking machine learning and Python assignments.

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