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A Brief Overview of AI Governance for Responsible Machine Learning Systems

arXiv 2211.13130 ↗First posted 21 Nov 2022cs.CY cs.AI cs.LGcited by 0

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Abstract (arXiv)

Organizations of all sizes, across all industries and domains are leveraging artificial intelligence (AI) technologies to solve some of their biggest challenges around operations, customer experience, and much more. However, due to the probabilistic nature of AI, the risks associated with it are far greater than traditional technologies. Research has shown that these risks can range anywhere from regulatory, compliance, reputational, and user trust, to financial and even societal risks. Depending on the nature and size of the organization, AI technologies can pose a significant risk, if not used in a responsible way. This position paper seeks to present a brief introduction to AI governance, which is a framework designed to oversee the responsible use of AI with the goal of preventing and mitigating risks. Having such a framework will not only manage risks but also gain maximum value out of AI projects and develop consistency for organization-wide adoption of AI.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 49 references that are in the map. First posted on arXiv 21 Nov 2022.

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References · 16

  1. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
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  4. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
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  11. 2015The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Reviewcited by 27
  12. 2021Weakly-Supervised Classification and Detection of Bird Sounds in the Wild. A BirdCLEF 2021 Solutioncited by 2
4 more
  1. 2021Recognizing bird species in diverse soundscapes under weak supervisioncited by 2
  2. 2022General Image Descriptors for Open World Image Retrieval using ViT CLIPcited by 2
  3. 2020Backtesting the predictability of COVID-19cited by 1
  4. 2022Few-shot Long-Tailed Bird Audio Recognitioncited by 1

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