What's Next
The topic of fairness and discrimination in data mining and machine learning is of paramount importance for our society as (semi)automatic decision making via data and (ML/AI) algorithms is becoming a mainstream approach to decision making. Therefore, we expect continuous interest in the topic from the research community but also businesses (e.g., GDPR compliance requirements in Europe).
Moreover, ensuring fairness-aware solutions is a (technically) challenging task as typically fairness competes with predictive performance of the models and moreover, ensuring fairness-aware solutions might require fundamentally different directions from the ones employed thus far (for example, defining new learning tasks instead of adding fairness constraints as extra regularizers in the loss function). Therefore, we expect also the interest of the research community to persist and even increase.
FAcct Network and Related Workshops
The BIAS 2020 workshop is proudly a part of the FAccT network, to research and engage with fairness, accountability, and transparency scholars across connected disciplines.
Other workshops co-located with major conferences on the topic of fairness and responsible AI/ML include:
FATES 2020 : Workshop on Fairness, Accountability, Transparency, Ethics, and Society on the Web, co-located with WebConf 2020.
FATE/MM 2020: Workshop on Fairness Accountability Transparency and Ethics in Multimedia, co-located with MM 2020.
https://project.inria.fr/fatmm/
XKDD 2020: Workshop on eXplainable Knowledge Discovery in Data Mining,co-located with ECML-PKDD 2020.
https://kdd.isti.cnr.it/xkdd2020/
XXAI 2020: 2020 Workshop on Extending Explainable AI Beyond Deep Models and Classifiers, co-located with ICML 2020.
http://interpretable-ml.org/icml2020workshop/
XAI 2020: Workshop on eXplainable Artificial Intelligence, co-located with JCAI-PRICAI 2020.