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PhD thesis examines societal impact of machine learning and fairness

By PulseAugur Editorial · [1 sources] ·

This PhD thesis by Joachim Baumann explores the societal impact of machine learning, focusing on fairness and algorithmic discrimination. It introduces methods for measuring fairness, decomposing ML systems to identify bias, and implementing interventions to reduce discrimination while preserving utility. The work aims to ensure that the integration of ML, including generative AI, aligns with societal values and addresses ongoing challenges. AI

IMPACT Provides a framework for developing fairer ML systems, crucial for responsible AI integration into society.

RANK_REASON This is a PhD thesis published on arXiv, focusing on research into the societal impact and fairness of machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

PhD thesis examines societal impact of machine learning and fairness

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This is a PhD thesis published on arXiv, focusing on research into the societal impact and fairness of machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Joachim Baumann ·

    On the Societal Impact of Machine Learning

    arXiv:2510.23693v2 Announce Type: replace Abstract: This PhD thesis investigates the societal impact of machine learning (ML). ML increasingly informs consequential decisions and recommendations, significantly affecting many aspects of our lives. As these data-driven systems are …

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