The Next Frontier: Artificial Intelligence we can really trust !
In this keynote paper from ECML 2021, I begin my talk with the tremendous advances in the field of statistical machine learning, the availability of large amounts of training data, and the increasing computational power that have ultimately made artificial intelligence (AI) (again) very successful. For certain tasks, algorithms can even achieve performance beyond human levels. Unfortunately, the most powerful methods suffer from both difficulty in explaining why a particular result was obtained and a lack of robustness. Our most powerful machine learning models are very sensitive to even small changes. Perturbations in the input data can have a dramatic impact on the output, leading to completely different results. This is of great importance in virtually all critical domains where we suffer from poor data quality, i.e., we do not have the i.i.d. data we expect. The use of AI in domains that impact human life (agriculture, climate, health, …) has therefore led to an increased need for trustworthy AI. In sensitive domains such as medicine, where traceability, transparency and interpretability are required, explicability is now even mandatory due to regulatory requirements. One possible step to make AI more robust is to combine statistical learning with knowledge representations. For certain tasks, it may be beneficial to include a human in the loop. A human expert can – sometimes, of course, not always – bring experience, domain knowledge, and conceptual understanding to the AI pipeline. Such approaches are not only a solution from a legal perspective, but in many application areas, the “why” is often more important than a pure classification result. Consequently, both explainability and robustness can promote reliability and trust and ensure that humans remain in control, thus complementing human intelligence with artificial intelligence.
See the paper here:
https://www.researchgate.net/publication/358693275_The_Next_Frontier_AI_We_Can_Really_Trust
Reference (Harvard JMLR style):
Andreas Holzinger (2021). The Next Frontier: AI We Can Really Trust. In: Kamp, Michael (ed.) Proceedings of the ECML PKDD 2021, CCIS 1524. Cham: Springer Nature, pp. 1–14, doi:10.1007/978-3-030-93736-2_33
Reference (IEEE style):
[1] A. Holzinger, “The Next Frontier: AI We Can Really Trust,” in Proceedings of the ECML PKDD 2021, CCIS 1524, M. Kamp, Ed. Cham: Springer Nature, 2021, pp. 1–14, 10.1007/978-3-030-93736-2_33