[Paper Review] Improving fairness in machine learning systems: What do industry practitioners need?

Kenneth Holstein, Jennifer Wortman Vaughan+3 more|arXiv (Cornell University)|Dec 13, 2018

Ethics and Social Impacts of AI84 references53 citations

TL;DR

The provided text is a SIGCHI extended abstract formatting sample, not a study on fairness; it describes formatting guidelines for SIGCHI submissions and ACM rights, not empirical research.

ABSTRACT

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.

Motivation & Objective

• Present formatting requirements for SIGCHI submissions to ensure consistent, high-quality appearance.
• Describe recommended writing style and language guidelines for an international SIGCHI readership.
• Provide detailed instructions for figures, tables, references, and figure placement to aid authors.
• Explain ACM copyright, permissions, and PDF production policies to authors.

Proposed method

• Use the chi-ext LaTeX class file to format submissions.
• Follow specified fonts (8.5-point Verdana, sans-serif substitutes) and layout rules.
• Adhere to guidelines for figures, tables, captions, and margins as demonstrated in the template.
• Ensure PDFs are ACM DL compliant and test PDFs with specified Acrobat versions before submission.

Experimental results

Key findings

• No empirical research results are reported; the document provides guidelines for formatting and submission readiness.
• Author guidelines cover language, terminology, inclusivity, and cross-cultural readability for SIGCHI audiences.
• The document details required sections (copyright, references, figures, tables) and how to structure them for ACM Digital Library submission.