Spain

ECAI 2020

You Shouldn’t Trust Me: Learning Models Which Conceal Unfairness From Multiple Explanation Methods

unreliability

feature importance

interpretability

explainable ai

fairness

adversarial attacks

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


We were able to keep the registration fees similar to those charged for the virtual ACL 2020. The one fee allows attendance at the main conference and any/all tutorials and workshops. These fees would be $125 Regular Early and $175 Regular Late; $50 Student Early and $75 Student Late. Early registration closes at midnight July 11, 2021 (Eastern Daylight Time).

**Reminder:** It is ACL’s policy that at least one author of each accepted paper (including ACL Finding papers) must register for the conference.

**Reminder2:** Underline site will open closer to the event. If you already registered you will receive access detail

Registration is now open

ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

technical paper

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

Maximal Correlation: An Alternative Criterion for Training Generative Networks

Automated Verification of Social Law Robustness for Reactive Agents

Common and Discriminative Semantic Pursuit for Multi-modal Multi-label Learning

ST-MFM: A Spatiotemporal Multi-modal Fusion Model for Urban Anomalies Prediction

Homogeneous Online Transfer Learning with Online Distribution Discrepancy Minimization

Semantic Point Completion Network for 3D Semantic Scene Completion

Learning Class-based Graph Representation for Object Detection

Generating Instantiated Argument Graphs from Probabilistic Information

Temporal Web Service QoS Prediction via Kalman Filter-Incorporated Latent Factor Analysis

Causal query in observational data with hidden variables

https://d3smihljt9218e.cloudfront.net/transcript/939/subtitle/20a7f0240665cf578ea75c8c62094689.vtt

English (automatic)

You Shouldn’t Trust Me: Learning Models Which Conceal Unfairness From Multiple Explanation Methods

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Maximal Correlation: An Alternative Criterion for Training Generative Networks

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You Shouldn’t Trust Me: Learning Models Which Conceal Unfairness From Multiple Explanation Methods

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Next from ECAI 2020

Maximal Correlation: An Alternative Criterion for Training Generative Networks

Similar lecture

Poisoning Knowledge Graph Embeddings via Relation Inference Patterns

Downloads