Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

@article{Adadi2018PeekingIT,
  title={Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)},
  author={Amina Adadi and M. Berrada},
  journal={IEEE Access},
  year={2018},
  volume={6},
  pages={52138-52160}
}
At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the shift towards a more algorithmic society. However, even with such unprecedented advancements, a key impediment to the use of AI-based systems is that they often lack transparency. Indeed, the black-box nature of these systems allows powerful predictions, but it cannot be directly explained. This issue has… Expand
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References

SHOWING 1-10 OF 166 REFERENCES
Explainable artificial intelligence: A survey
Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation
A Survey of Methods for Explaining Black Box Models
What do we need to build explainable AI systems for the medical domain?
...
1
2
3
4
5
...