@article{3390,
  keywords = {Bias, Fairness, Responsible Artificial Intelligence},
  author = {Rubén González-Sendino and Emilio Serrano and Javier Bajo and Paulo Novais},
  title = {A Review of Bias and Fairness in Artificial Intelligence},
  abstract = {Automating decision systems has led to hidden biases in the use of artificial intelligence (AI). Consequently, explaining these decisions and identifying responsibilities has become a challenge. As a result, a new field of research on algorithmic fairness has emerged. In this area, detecting biases and mitigating them is essential to ensure fair and discrimination-free decisions. This paper contributes with: (1) a categorization of biases and how these are associated with different phases of an AI model’s development (including the data-generation phase); (2) a revision of fairness metrics to audit the data and AI models trained with them (considering agnostic models when focusing on fairness); and, (3) a novel taxonomy of the procedures to mitigate biases in the different phases of an AI model’s development (pre-processing, training, and post-processing) with the addition of transversal actions that help to produce fairer models.},
  year = {9998},
  journal = {International Journal of Interactive Multimedia and Artificial Intelligence},
  volume = {In press},
  chapter = {1},
  number = {In press},
  pages = {1-13},
  month = {11/2023},
  issn = {1989-1660},
  url = {https://www.ijimai.org/journal/sites/default/files/2023-11/ip2023_11_001.pdf},
  doi = {10.9781/ijimai.2023.11.001},
}