Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors

• URL: http://arxiv.org/abs/2105.02980v2
• Date: Tue, 24 Aug 2021 19:53:22 GMT
• Title: Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors
• Authors: Hong Shen, Alicia DeVos, Motahhare Eslami, Kenneth Holstein
• Abstract summary: We propose and explore the concept of everyday algorithm auditing, a process in which users detect, understand, and interrogate problematic machine behaviors. We argue that everyday users are powerful in surfacing problematic machine behaviors that may elude detection via more centrally-organized forms of auditing.
• Score: 8.360589318502816
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A growing body of literature has proposed formal approaches to audit algorithmic systems for biased and harmful behaviors. While formal auditing approaches have been greatly impactful, they often suffer major blindspots, with critical issues surfacing only in the context of everyday use once systems are deployed. Recent years have seen many cases in which everyday users of algorithmic systems detect and raise awareness about harmful behaviors that they encounter in the course of their everyday interactions with these systems. However, to date little academic attention has been granted to these bottom-up, user-driven auditing processes. In this paper, we propose and explore the concept of everyday algorithm auditing, a process in which users detect, understand, and interrogate problematic machine behaviors via their day-to-day interactions with algorithmic systems. We argue that everyday users are powerful in surfacing problematic machine behaviors that may elude detection via more centrally-organized forms of auditing, regardless of users' knowledge about the underlying algorithms. We analyze several real-world cases of everyday algorithm auditing, drawing lessons from these cases for the design of future platforms and tools that facilitate such auditing behaviors. Finally, we discuss work that lies ahead, toward bridging the gaps between formal auditing approaches and the organic auditing behaviors that emerge in everyday use of algorithmic systems.

Related papers

• Online Decision Mediation [72.80902932543474]
  Consider learning a decision support assistant to serve as an intermediary between (oracle) expert behavior and (imperfect) human behavior. In clinical diagnosis, fully-autonomous machine behavior is often beyond ethical affordances.
  arXiv  Detail & Related papers  (2023-10-28T05:59:43Z)
• Online Corrupted User Detection and Regret Minimization [49.536254494829436]
  In real-world online web systems, multiple users usually arrive sequentially into the system. We present an important online learning problem named LOCUD to learn and utilize unknown user relations from disrupted behaviors. We devise a novel online detection algorithm OCCUD based on RCLUB-WCU's inferred user relations.
  arXiv  Detail & Related papers  (2023-10-07T10:20:26Z)
• Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem [0.971392598996499]
  We provide the first comprehensive field scan of the AI audit ecosystem. We identify emerging best practices as well as methods and tools that are becoming commonplace. We outline policy recommendations to improve the quality and impact of these audits.
  arXiv  Detail & Related papers  (2023-10-04T01:40:03Z)
• Interactive System-wise Anomaly Detection [66.3766756452743]
  Anomaly detection plays a fundamental role in various applications. It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data. We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
  arXiv  Detail & Related papers  (2023-04-21T02:20:24Z)
• From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management [15.22568616519016]
  We introduce ALARM, an end-to-end framework that supports the anomaly mining cycle comprehensively. It offers anomaly explanations and an interactive GUI for human-in-the-loop processes. We demonstrate ALARM's efficacy through a series of case studies with fraud analysts from the financial industry.
  arXiv  Detail & Related papers  (2023-04-06T20:49:36Z)
• System Cards for AI-Based Decision-Making for Public Policy [5.076419064097733]
  This work proposes a system accountability benchmark for formal audits of artificial intelligence-based decision-aiding systems. It consists of 56 criteria organized within a four-by-four matrix composed of rows focused on (i) data, (ii) model, (iii) code, (iv) system, and columns focused on (a) development, (b) assessment, (c) mitigation, and (d) assurance.
  arXiv  Detail & Related papers  (2022-03-01T18:56:45Z)
• Learning Physical Concepts in Cyber-Physical Systems: A Case Study [72.74318982275052]
  We provide an overview of the current state of research regarding methods for learning physical concepts in time series data. We also analyze the most important methods from the current state of the art using the example of a three-tank system.
  arXiv  Detail & Related papers  (2021-11-28T14:24:52Z)
• Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits [0.0]
  This review follows PRISMA guidelines in a review of over 500 English articles that yielded 62 algorithm audit studies. The studies are synthesized and organized primarily by behavior (discrimination, distortion, exploitation, and misjudgement) The paper concludes by offering the common ingredients of successful audits, and discussing algorithm auditing in the context of broader research.
  arXiv  Detail & Related papers  (2021-02-03T19:21:11Z)
• Detecting Suspicious Events in Fast Information Flows [0.0]
  We describe a computational feather-light and intuitive, yet provably efficient algorithm, named HALFADO. HALFADO is designed for detecting suspicious events in a high-frequency stream of complex entries, based on a relatively small number of examples of human judgement. We illustrate HALFADO's efficacy on two challenging applications: (1) for detecting em hate speech messages in a flow of text messages gathered from a social media platform, and (2) for a Transaction Monitoring System (TMS) in detecting fraudulent transactions in a stream of financial transactions.
  arXiv  Detail & Related papers  (2021-01-07T08:19:25Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)
• Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis [75.64261155172856]
  survival analysis (SA) naturally supports censored data and offers appropriate ways to use such data for learning distributional models of algorithm runtime. We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive. In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.
  arXiv  Detail & Related papers  (2020-07-06T15:20:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.