What Towards a Standard for Identifying and Managing Bias in Artificial Intelligence requires
This special publication describes the challenges of bias in artificial intelligence and provides examples of how and why it can erode public trust. It identifies three categories of bias in AI-systemic, statistical, and human-and describes how and where they contribute to harms. The document also describes three broad challenges for mitigating bias related to datasets, testing and evaluation, and human factors, and introduces preliminary guidance for addressing them. While many organizations seek to utilize information in a responsible manner, biases remain endemic across technology processes and can lead to harmful impacts regardless of intent. These harmful outcomes, even if inadvertent, create significant challenges for cultivating public trust in AI. Successfully meeting this challenge requires taking all forms of bias into account, expanding the perspective beyond the machine learning pipeline to a broader socio-technical view. The intended audience for this document includes individuals and groups who are responsible for designing, developing, deploying, evaluating, and governing AI systems. The core obligation is to provide a roadmap for developing detailed socio-technical guidance for identifying and managing AI bias. NIST intends to develop methods for increasing assurance, governance, and practice improvements for identifying, understanding, measuring, managing, and reducing bias. The guidance is voluntary and intended to be flexible and applicable across contexts, regardless of industry.
Pillar: AI Governance & Law · Authority: National Institute of Standards and Technology · Version: 1.0.0 · Last updated:
Primary source: https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1270.pdf
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Primary Citations — 8 traced to source
- Executive Summary: Harmful impacts stemming from AI are not just at the individual or enterprise level, but are able to ripple into the broader society.
- Section 1: AI risk management seeks to minimize anticipated and emergent negative impacts of AI systems, including threats to civil liberties and rights. One of those risks is bias.
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