TY - GEN
T1 - PORDE
T2 - 28th International Conference on Intelligent User Interfaces, IUI 2023
AU - Lee, Hyunmin
AU - Chun, Minki
AU - Jung, Hyunggu
N1 - Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/3/27
Y1 - 2023/3/27
N2 - Artificial intelligence (AI) gives many benefits to our lives. However, biased AI models created by receiving data poisoning attacks may induce social problems. Therefore, developers must consider carefully whether the training data received a poison attack when developing an AI model. Data visualization is one of the methods to facilitate the analysis of the data required for checking if the training data received a poisoning attack. However, prior studies did not visualize real-world AI training data. Restaurant reviews in delivery apps are one of the cases of a poisoned dataset. Restaurants hold review events on delivery apps to encourage customers to write a positive review in return for certain rewards, thereby creating reviews with bias. In this study, we propose POisoned Real-world Data Explainer (PORDE) that explains data poisoning attacks through visual analytics with food delivery app reviews. The findings of this study suggest implications for securing safe training data and developing less biased AI models.
AB - Artificial intelligence (AI) gives many benefits to our lives. However, biased AI models created by receiving data poisoning attacks may induce social problems. Therefore, developers must consider carefully whether the training data received a poison attack when developing an AI model. Data visualization is one of the methods to facilitate the analysis of the data required for checking if the training data received a poisoning attack. However, prior studies did not visualize real-world AI training data. Restaurant reviews in delivery apps are one of the cases of a poisoned dataset. Restaurants hold review events on delivery apps to encourage customers to write a positive review in return for certain rewards, thereby creating reviews with bias. In this study, we propose POisoned Real-world Data Explainer (PORDE) that explains data poisoning attacks through visual analytics with food delivery app reviews. The findings of this study suggest implications for securing safe training data and developing less biased AI models.
KW - Data Poisoning Attack
KW - Data Visualization
KW - Food Delivery App Reviews
UR - http://www.scopus.com/inward/record.url?scp=85151997611&partnerID=8YFLogxK
U2 - 10.1145/3581754.3584128
DO - 10.1145/3581754.3584128
M3 - Conference contribution
AN - SCOPUS:85151997611
T3 - International Conference on Intelligent User Interfaces, Proceedings IUI
SP - 46
EP - 50
BT - IUI 2023 - Companion Proceedings of the 28th International Conference on Intelligent User Interfaces
PB - Association for Computing Machinery
Y2 - 27 March 2023 through 31 March 2023
ER -