Use of Ai in Online Content Moderation
Total Page:16
File Type:pdf, Size:1020Kb

Load more
Recommended publications
-
``At the End of the Day Facebook Does What It Wants'': How Users
“At the End of the Day Facebook Does What It Wants”: How Users Experience Contesting Algorithmic Content Moderation KRISTEN VACCARO, University of Illinois Urbana-Champaign CHRISTIAN SANDVIG, University of Michigan KARRIE KARAHALIOS, University of Illinois Urbana-Champaign Interest has grown in designing algorithmic decision making systems for contestability. In this work, we study how users experience contesting unfavorable social media content moderation decisions. A large-scale online experiment tests whether different forms of appeals can improve users’ experiences of automated decision making. We study the impact on users’ perceptions of the Fairness, Accountability, and Trustworthiness of algorithmic decisions, as well as their feelings of Control (FACT). Surprisingly, we find that none of the appeal designs improve FACT perceptions compared to a no appeal baseline. We qualitatively analyze how users write appeals, and find that they contest the decision itself, but also more fundamental issues like thegoalof moderating content, the idea of automation, and the inconsistency of the system as a whole. We conclude with suggestions for – as well as a discussion of the challenges of – designing for contestability. CCS Concepts: • Human-centered computing → Human computer interaction (HCI); Social media. Additional Key Words and Phrases: content moderation; algorithmic experience ACM Reference Format: Kristen Vaccaro, Christian Sandvig, and Karrie Karahalios. 2020. “At the End of the Day Facebook Does What It Wants”: How Users Experience Contesting Algorithmic Content Moderation. Proc. ACM Hum.-Comput. Interact. 4, CSCW2, Article 167 (October 2020), 22 pages. https://doi.org/10.1145/3415238 1 INTRODUCTION As algorithmic decision making systems become both more prevalent and more visible, interest has grown in how to design them to be trustworthy, understandable and fair. -
Download Paper
Lawless: the secret rules that govern our digital lives (and why we need new digital constitutions that protect our rights) Submitted version. Forthcoming 2019 Cambridge University Press. Nicolas P. Suzor Table of Contents Part I: a lawless internet Chapter 1. The hidden rules of the internet ............................................................................................. 6 Process matters ....................................................................................................................................................... 12 Chapter 2. Who makes the rules?.......................................................................................................... 17 Whose values apply? ............................................................................................................................................... 22 The moderation process .......................................................................................................................................... 25 Bias and accountability ........................................................................................................................................... 28 Chapter 3. The internet’s abuse problem ............................................................................................... 41 Abuse reflects and reinforces systemic inequalities ................................................................................................ 50 Dealing with abuse needs the involvement of platforms ....................................................................................... -
Content Moderation
Robert Lewington (Twitch) & The Fair Play Alliance Executive Steering Committee April 2021 Being ‘Targeted’ about Content Moderation: Strategies for consistent, scalable and effective response to Disruption & Harm 1 Content Moderation: Best Practices for Targeted Reporting & reactive UGC Management At Scale March 2021 Abstract This document provides replicable best practice information on how to moderate User-Generated Content (UGC) in social applications or services (including digital media and video games). Its focus is on reactive moderation, a central component of the growing content moderation toolkit where a service provider responds to reports submitted by users of its service regarding UGC that may violate its Terms of Service. Specifically, the document explores and advocates for a ‘targeted’ approach to the creation of reporting mechanisms. This allows users to closely identify the specific infraction, utilise evidence of the infraction—access to which is facilitated as part of the design of the reporting process—enabling consistent content moderation at scale. Note, however, that we will also make passing-reference to pre, post and proactive (see Appendix A) moderation approaches. Specifics of how best to tailor these best practices to a particular application or service will differ based on various parameters, including: type of service (social media, video game etc.); type of media (text, image, audio, video etc.); sharing mechanism (feed/gallery, avatar, communication etc.); persistence (ephemeral vs. static/umutable) and others, and therefore this document should be considered a set of high-level instructive principles rather than prescriptive guidelines. Contents i. Background ii. The Content Moderation Flywheel ○ Community Guidelines/Code of Conduct ○ Targeted reporting ■ Context ○ Scalable Content Moderation ○ Education ○ Technology iii. -
Getting Started with PX4 for Contributors Mark West PX4 Community Volunteer Format
Getting Started with PX4 For Contributors Mark West PX4 Community Volunteer Format A word about format Limited Time + Big Subject = Compression Some Subjects Skipped = Look in Appendix Demo Videos Clipped = Look in Appendix Who is this for? Anybody Levels: L1: Operate a PX4 vehicle L2: Build a PX4 vehicle L3: Build the source L4: Modify the source L5: Contribute L1: Operate You want to operate a drivable unit See Appendix : Operate Vehicle L2: Build a Vehicle You want to build a drivable unit See Appendix : Build Vehicle L3: Build the Source You want to build the image from source Big step up from L2 Choose Toolchain Container L3: Build the Source : Toolchain All the tools you need to build an image / exe Compilers / Linkers / Tools / Etc Installation: Manual : If needed Convenience Scripts : Preferred L3: Build the Source : Container Container? Like a better VM, with toolchain installed Easy to install, easy to update, no interaction Image = container snapshot L3: Build the Source : Container Container images built in layers px4io/px4-dev-ros-melodic px4io/px4-dev-simulation-bionic px4io/px4-dev-base-bionic ubuntu L3: Build the Source : Container Containers are isolated, all interaction is explicit File System -v ~/src_d/Firmware:/src/firmware/:rw File System Directory Directory Network Port -p 14556:14556/udp Network Port Host Docker run params Container L3: Build the Source : Toolchain or Container? Container if possible Toolchain if no suitable container * But you can make your own containers L3: Build the Source : Toolchain Demo Build image for SITL Simulation 1) Install Toolchain 2) Git PX4 Source 3) Build PX4 4) Takeoff L3: Build the Source : Container Demo Build image for SITL Simulation 1) Install Docker 2) Git PX4 Source 3) Locate Container 4) Run Container 5) Build PX4 6) Build available on host computer L4: Modify the Source Next step up in complexity Why? Fix bugs, add features Loop: Edit, Build, Debug IDE: Visual Studio, Eclipse, QT Creator, etc. -
Deeper Attention to Abusive User Content Moderation
Deeper Attention to Abusive User Content Moderation John Pavlopoulos Prodromos Malakasiotis Ion Androutsopoulos Straintek, Athens, Greece Straintek, Athens, Greece Athens University of Economics [email protected] [email protected] and Business, Greece [email protected] Abstract suffer from abusive user comments, which dam- age their reputations and make them liable to fines, Experimenting with a new dataset of 1.6M e.g., when hosting comments encouraging illegal user comments from a news portal and an actions. They often employ moderators, who are existing dataset of 115K Wikipedia talk frequently overwhelmed, however, by the volume page comments, we show that an RNN op- and abusiveness of comments.3 Readers are dis- erating on word embeddings outpeforms appointed when non-abusive comments do not ap- the previous state of the art in moderation, pear quickly online because of moderation delays. which used logistic regression or an MLP Smaller news portals may be unable to employ classifier with character or word n-grams. moderators, and some are forced to shut down We also compare against a CNN operat- their comments sections entirely. ing on word embeddings, and a word-list We examine how deep learning (Goodfellow baseline. A novel, deep, classification- et al., 2016; Goldberg, 2016, 2017) can be em- specific attention mechanism improves the ployed to moderate user comments. We experi- performance of the RNN further, and can ment with a new dataset of approx. 1.6M manually also highlight suspicious words for free, moderated (accepted or rejected) user comments without including highlighted words in the from a Greek sports news portal (called Gazzetta), training data. -
Reinforcement Learning and Trustworthy Autonomy
Reinforcement Learning and Trustworthy Autonomy Jieliang Luo, Sam Green, Peter Feghali, George Legrady, and Çetin Kaya Koç Abstract Cyber-Physical Systems (CPS) possess physical and software inter- dependence and are typically designed by teams of mechanical, electrical, and software engineers. The interdisciplinary nature of CPS makes them difficult to design with safety guarantees. When autonomy is incorporated, design complexity and, especially, the difficulty of providing safety assurances are increased. Vision- based reinforcement learning is an increasingly popular family of machine learning algorithms that may be used to provide autonomy for CPS. Understanding how visual stimuli trigger various actions is critical for trustworthy autonomy. In this chapter we introduce reinforcement learning in the context of Microsoft’s AirSim drone simulator. Specifically, we guide the reader through the necessary steps for creating a drone simulation environment suitable for experimenting with vision- based reinforcement learning. We also explore how existing vision-oriented deep learning analysis methods may be applied toward safety verification in vision-based reinforcement learning applications. 1 Introduction Cyber-Physical Systems (CPS) are becoming increasingly powerful and complex. For example, standard passenger vehicles have millions of lines of code and tens of processors [1], and they are the product of years of planning and engineering J. Luo () · S. Green · P. Feghali · G. Legrady University of California Santa Barbara, Santa Barbara, CA, USA e-mail: [email protected]; [email protected]; [email protected]; [email protected] Ç. K. Koç Istinye˙ University, Istanbul,˙ Turkey Nanjing University of Aeronautics and Astronautics, Nanjing, China University of California Santa Barbara, Santa Barbara, CA, USA e-mail: [email protected] © Springer Nature Switzerland AG 2018 191 Ç. -
Recommendations for the Facebook Content Review Board
STANFORD Policy Practicum: Creating a Social Media Oversight Board Recommendations for the Facebook Content Review Board 2018-19 PRACTICUM RESEARCH TEAM: Shaimaa BAKR, Ph.D. Electrical Engineering ‘20 Madeline MAGNUSON, J.D. ’20 Fernando BERDION-DEL VALLE, J.D. ’20 Shawn MUSGRAVE, J.D. ’21 Isabella GARCIA-CAMARGO, B.S.’20 Ashwin RAMASWAMI, B.S. ‘21 Julia GREENBERG, J.D. ’19 Nora TAN, B.S. ’19 Tara IYER, B.S.’19 Marlena WISNIAK, LL.M. ’19 Alejandra LYNBERG, J.D. ’19 Monica ZWOLINSKI, J.D. ’19 INSTRUCTORS: Paul BREST, Faculty Director, Law and Policy Lab Daniel HO, William Benjamin Scott and Luna M. Scott Professor of Law Nathaniel PERSILY, James B. McClatchy Professor of Law Rob REICH, Faculty Director, Center for Ethics in Society TEACHING ASSISTANT: Liza STARR, J.D./M.B.A. ’21 SPRING POLICY CLIENT: Project on Democracy and the Internet, Stanford Center on Philanthropy and Civil Society 2019 559 Nathan Abbot Way Stanford, CA https://law.stanford.edu/education/only-at-sls/law-policy-lab// Contents Executive Summary ....................................................................................3 Introduction ................................................................................................8 I. The Need for Governing Principles ..........................................................9 II. Structure and Membership ...................................................................10 A. Selection Process for Board Members ..............................................................10 B. Membership Criteria .............................................................................................12 -
A Simple Platform for Reinforcement Learning of Simulated Flight Behaviors ⋆
LM2020, 011, v2 (final): ’A Simple Platform for Reinforcement Learning of Simulated . 1 A Simple Platform for Reinforcement Learning of Simulated Flight Behaviors ⋆ Simon D. Levy Computer Science Department, Washington and Lee University, Lexington VA 24450, USA [email protected] Abstract. We present work-in-progress on a novel, open-source soft- ware platform supporting Deep Reinforcement Learning (DRL) of flight behaviors for Miniature Aerial Vehicles (MAVs). By using a physically re- alistic model of flight dynamics and a simple simulator for high-frequency visual events, our platform avoids some of the shortcomings associated with traditional MAV simulators. Implemented as an OpenAI Gym en- vironment, our simulator makes it easy to investigate the use of DRL for acquiring common behaviors like hovering and predation. We present preliminary experimental results on two such tasks, and discuss our cur- rent research directions. Our code, available as a public github repository, enables replication of our results on ordinary computer hardware. Keywords: Deep reinforcement learning Flight simulation Dynamic · · vision sensing. 1 Motivation Miniature Aerial Vehicles (MAVs, a.k.a. drones) are increasingly popular as a model for the behavior of insects and other flying animals [3]. The cost and risks associated with building and flying MAVs can however make such models inaccessible to many researchers. Even when such platforms are available, the number of experiments that must be run to collect sufficient data for paradigms like Deep Reinforcement Learning (DRL) makes simulation an attractive option. Popular MAV simulators like Microsoft AirSim [10], as well as our own sim- ulator [7], are built on top of video game engines like Unity or UnrealEngine4. -
Chapter 3 Airsim Simulator
MASTER THESIS Structured Flight Plan Interpreter for Drones in AirSim Francesco Rose SUPERVISED BY Cristina Barrado Muxi Universitat Politècnica de Catalunya Master in Aerospace Science & Technology January 2020 This Page Intentionally Left Blank Structured Flight Plan Interpreter for Drones in AirSim BY Francesco Rose DIPLOMA THESIS FOR DEGREE Master in Aerospace Science and Technology AT Universitat Politècnica de Catalunya SUPERVISED BY: Cristina Barrado Muxi Computer Architecture Department “E come i gru van cantando lor lai, faccendo in aere di sé lunga riga, così vid’io venir, traendo guai, ombre portate da la detta briga” Dante, Inferno, V Canto This Page Intentionally Left Blank ABSTRACT Nowadays, several Flight Plans for drones are planned and managed taking advantages of Extensible Markup Language (XML). In the mean time, to test drones performances as well as their behavior, simulators usefulness has been increasingly growing. Hence, what it takes to make a simulator capable of receiving commands from an XML file is a dynamic interface. The main objectives of this master thesis are basically three. First of all, the handwriting of an XML flight plan (FP) compatible with the simulator environment chosen. Then, the creation of a dynamic interface that can read whatever XML FP and that will transmit commands to the drone. Finally, using the simulator, it will be possible to test both interface and flight plan. Moreover, a dynamic interface aimed at managing two or more drones in parallel has been built and implemented as extra objective of this master thesis. In addition, assuming that two drones will be used to test this interface, it is required the handwriting of two more FPs. -
An Aerial Mixed-Reality Environment for First-Person-View Drone Flying
applied sciences Article An Aerial Mixed-Reality Environment for First-Person-View Drone Flying Dong-Hyun Kim , Yong-Guk Go and Soo-Mi Choi * Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea; [email protected] (D.-H.K.); lattechiff[email protected] (Y.-G.G.) * Correspondence: [email protected] Received: 27 June 2020; Accepted: 3 August 2020; Published: 6 August 2020 Abstract: A drone be able to fly without colliding to preserve the surroundings and its own safety. In addition, it must also incorporate numerous features of interest for drone users. In this paper, an aerial mixed-reality environment for first-person-view drone flying is proposed to provide an immersive experience and a safe environment for drone users by creating additional virtual obstacles when flying a drone in an open area. The proposed system is effective in perceiving the depth of obstacles, and enables bidirectional interaction between real and virtual worlds using a drone equipped with a stereo camera based on human binocular vision. In addition, it synchronizes the parameters of the real and virtual cameras to effectively and naturally create virtual objects in a real space. Based on user studies that included both general and expert users, we confirm that the proposed system successfully creates a mixed-reality environment using a flying drone by quickly recognizing real objects and stably combining them with virtual objects. Keywords: aerial mixed-reality; drones; stereo cameras; first-person-view; head-mounted display 1. Introduction As cameras mounted on drones can easily capture photographs of places that are inaccessible to people, drones have been recently applied in various fields, such as aerial photography [1], search and rescue [2], disaster management [3], and entertainment [4]. -
Online Communities As Semicommons
The Virtues of Moderation: Online Communities as Semicommons James Grimmelmann* IP Scholars workshop draft: not intended for broad distribution or citation I. INTRODUCTION................................................................................................................................................2 II. BACKGROUND ..................................................................................................................................................4 A. PUBLIC AND PRIVATE GOODS ...........................................................................................................................4 B. THE TRAGIC STORY ..........................................................................................................................................6 C. THE COMEDIC STORY .......................................................................................................................................9 D. LAYERING.......................................................................................................................................................12 III. ONLINE SEMICOMMONS .............................................................................................................................14 A. A FORMAL MODEL .........................................................................................................................................14 B. COMMONS VIRTUES........................................................................................................................................17 -
Peter Van Der Perk
Eindhoven University of Technology MASTER A distributed safety mechanism for autonomous vehicle software using hypervisors van der Perk, P.J. Award date: 2019 Link to publication Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain Department of Electrical Engineering Electronic Systems Research Group A Distributed Safety Mechanism for Autonomous Vehicle Software Using Hypervisors graduation project Peter van der Perk Supervisors: prof.dr. Kees Goossens dr. Andrei Terechko version 1.0 Eindhoven, June 2019 Abstract Autonomous vehicles rely on cyber-physical systems to provide comfort and safety to the passen- gers. The objective of safety designs is to avoid unacceptable risk of physical injury to people. Reaching this objective, however, is very challenging because of the growing complexity of both the Electronic Control Units (ECUs) and software architectures required for autonomous operation.