This talk would focus on some real life use cases of bias in AI and how it had socioeconomic impact, along with practical mitigation strategies to make AI more responsible. The session would dive deep into how and where bias manifests in the overall ML cycle, and ways to measure and remove it.
The talk will focus on the considerations while designing a scalable and reliable streaming data platform and how it is different from batch data platforms. Unbounded datasets present very different challenges from bounded data processing, and this session would focus on addressing those.
Priority access to all content
Video hallway track
Community chat
Exclusive promotions and giveaways