Demographic Benchmarking: Bridging Socio-Technical Gaps in Bias Detection

• URL: http://arxiv.org/abs/2501.15985v1
• Date: Mon, 27 Jan 2025 12:14:49 GMT
• Title: Demographic Benchmarking: Bridging Socio-Technical Gaps in Bias Detection
• Authors: Gemma Galdon Clavell, Rubén González-Sendino, Paola Vazquez,
• Abstract summary: This paper describes how the ITACA AI auditing platform tackles demographic benchmarking when auditing AI recommender systems. The framework serves us as auditors as it allows us to not just measure but establish acceptability ranges for specific performance indicators. Our approach integrates socio-demographic insights directly into AI systems, reducing bias and improving overall performance.
• Score: 0.0
• License:
• Abstract: Artificial intelligence (AI) models are increasingly autonomous in decision-making, making pursuing responsible AI more critical than ever. Responsible AI (RAI) is defined by its commitment to transparency, privacy, safety, inclusiveness, and fairness. But while the principles of RAI are clear and shared, RAI practices and auditing mechanisms are still incipient. A key challenge is establishing metrics and benchmarks that define performance goals aligned with RAI principles. This paper describes how the ITACA AI auditing platform developed by Eticas.ai tackles demographic benchmarking when auditing AI recommender systems. To this end, we describe a Demographic Benchmarking Framework designed to measure the populations potentially impacted by specific AI models. The framework serves us as auditors as it allows us to not just measure but establish acceptability ranges for specific performance indicators, which we share with the developers of the systems we audit so they can build balanced training datasets and measure and monitor fairness throughout the AI lifecycle. It is also a valuable resource for policymakers in drafting effective and enforceable regulations. Our approach integrates socio-demographic insights directly into AI systems, reducing bias and improving overall performance. The main contributions of this study include:1. Defining control datasets tailored to specific demographics so they can be used in model training; 2. Comparing the overall population with those impacted by the deployed model to identify discrepancies and account for structural bias; and 3. Quantifying drift in different scenarios continuously and as a post-market monitoring mechanism.

Related papers

• Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation [2.2241228857601727]
  This paper presents an interdisciplinary meta-review of about 100 studies that discuss shortcomings in quantitative benchmarking practices. It brings together many fine-grained issues in the design and application of benchmarks with broader sociotechnical issues. Our review also highlights a series of systemic flaws in current practices, such as misaligned incentives, construct validity issues, unknown unknowns, and problems with the gaming of benchmark results.
  arXiv  Detail & Related papers  (2025-02-10T15:25:06Z)
• More than Marketing? On the Information Value of AI Benchmarks for Practitioners [42.73526862595375]
  In academia, public benchmarks were generally viewed as suitable measures for capturing research progress. In product and policy, benchmarks were often found to be inadequate for informing substantive decisions. We conclude that effective benchmarks should provide meaningful, real-world evaluations, incorporate domain expertise, and maintain transparency in scope and goals.
  arXiv  Detail & Related papers  (2024-12-07T03:35:39Z)
• Interpretable Rule-Based System for Radar-Based Gesture Sensing: Enhancing Transparency and Personalization in AI [2.99664686845172]
  We introduce MIRA, a transparent and interpretable multi-class rule-based algorithm tailored for radar-based gesture detection. We showcase the system's adaptability through personalized rule sets that calibrate to individual user behavior, offering a user-centric AI experience. Our research underscores MIRA's ability to deliver both high interpretability and performance and emphasizes the potential for broader adoption of interpretable AI in safety-critical applications.
  arXiv  Detail & Related papers  (2024-09-30T16:40:27Z)
• Benchmarks as Microscopes: A Call for Model Metrology [76.64402390208576]
  Modern language models (LMs) pose a new challenge in capability assessment. To be confident in our metrics, we need a new discipline of model metrology.
  arXiv  Detail & Related papers  (2024-07-22T17:52:12Z)
• Evaluating Human Alignment and Model Faithfulness of LLM Rationale [66.75309523854476]
  We study how well large language models (LLMs) explain their generations through rationales. We show that prompting-based methods are less "faithful" than attribution-based explanations.
  arXiv  Detail & Related papers  (2024-06-28T20:06:30Z)
• LoRA-Ensemble: Efficient Uncertainty Modelling for Self-attention Networks [52.46420522934253]
  We introduce LoRA-Ensemble, a parameter-efficient deep ensemble method for self-attention networks. By employing a single pre-trained self-attention network with weights shared across all members, we train member-specific low-rank matrices for the attention projections. Our method exhibits superior calibration compared to explicit ensembles and achieves similar or better accuracy across various prediction tasks and datasets.
  arXiv  Detail & Related papers  (2024-05-23T11:10:32Z)
• Towards a Responsible AI Metrics Catalogue: A Collection of Metrics for AI Accountability [28.67753149592534]
  This study bridges the accountability gap by introducing our effort towards a comprehensive metrics catalogue. Our catalogue delineates process metrics that underpin procedural integrity, resource metrics that provide necessary tools and frameworks, and product metrics that reflect the outputs of AI systems.
  arXiv  Detail & Related papers  (2023-11-22T04:43:16Z)
• ComplAI: Theory of A Unified Framework for Multi-factor Assessment of Black-Box Supervised Machine Learning Models [6.279863832853343]
  ComplAI is a unique framework to enable, observe, analyze and quantify explainability, robustness, performance, fairness, and model behavior. It evaluates different supervised Machine Learning models not just from their ability to make correct predictions but from overall responsibility perspective.
  arXiv  Detail & Related papers  (2022-12-30T08:48:19Z)
• ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning [63.77667876176978]
  Large language models show improved downstream task interpretability when prompted to generate step-by-step reasoning to justify their final answers. These reasoning steps greatly improve model interpretability and verification, but objectively studying their correctness is difficult. We present ROS, a suite of interpretable, unsupervised automatic scores that improve and extend previous text generation evaluation metrics.
  arXiv  Detail & Related papers  (2022-12-15T15:52:39Z)
• Trustworthy AI [75.99046162669997]
  Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, are some of the most prominent limitations. We propose the tutorial on Trustworthy AI to address six critical issues in enhancing user and public trust in AI systems.
  arXiv  Detail & Related papers  (2020-11-02T20:04:18Z)
• Causal Feature Selection for Algorithmic Fairness [61.767399505764736]
  We consider fairness in the integration component of data management. We propose an approach to identify a sub-collection of features that ensure the fairness of the dataset.
  arXiv  Detail & Related papers  (2020-06-10T20:20:10Z)

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.