Call for Papers - Algorithmic Fairness in Artificial intelligence, Machine learning and Decision making 2023

AFair-AMLD 2023 - Minneapolis, US


I’m pleased to annouce that my team and I organize a workshop on Algorithmic Fairness in Artificial intelligence, Machine learning and Decision making as a part of SIAM Data Mining Conference

Important Information

Venue: Graduate Minneapolis Hotel, Minneapolis, Minnesota, USA

Important dates:

  • Paper submission deadline: February 15, 2023
  • Decision notification: March 01, 2023
  • Camera ready paper due: March 20, 2023
  • Application for travel awards January 26, 2023
  • Workshop date: April 27, 2023


The main objective of the workshop is to motivate and enable collaboration of a wider community of interest in AI/ML/DM fair modeling. Further, we plan to stimulate interdisciplinary discussions on cutting edge AI/ML/DM algorithms and their compliance with legal/social definitions of fairness as well as implications of fair (and unfair) AI/ML/DM modelling in real world settings. By accomplishing workshop objectives, we hope that we can produce synergetic effect of legal/social and technical aspects of fairness that would lead to development of novel methods as well to faster adoption of such methods in industry. These objectives will be accomplished by attracting invited talks and direct reach to both technical and social/legal researchers in areas of AI/ML/DM fairness, making sure that both groups are adequately represented.

Topics of interests include:

  • Fair classification, regression and clustering algorithms
  • Envy free classification, regression and clustering algorithms
  • Pre-processing, in-processing, post-processing techniques in fair AI/ML/DM
  • Fair ranking algorithms
  • Fairness in recommendations and recommender systems
  • Fair classification and regression on graphs
  • Fair deep learning algorithms
  • Novel measures of group and individual fairness
  • Fairness and causal inference
  • Novel mathematical formulations of fairness concepts
  • Trade-offs between fairness metrics
  • Trade-offs between algorithmic performance and fairness metrics.
  • Fair embeddings
  • Fair data imputation
  • Fair algorithm applications
  • Fairness-sensitive algorithms in practice
  • Benchmark datasets for AI/ML/DM
  • Applications and case studies of fair AI/ML/DM models in different domains (marketing, healthcare, law, banking etc.)

Submission guidelines:

Paper length: 5-9 pages including abstract, bibliography and appendices. Papers must have an abstract with a maximum of 300 words and a keyword list with no more than six keywords.

Review: Double blind.

Format: Papers should be submitted in pdf format. Papers must be prepared in LaTeX2e, and formatted using SIAM’s double column template. Latex template is available here.

Submission: All papers should be submitted through EasyChair submission system.