As an AI-first company, Google aims to develop the benefits of machine learning for everyone.
Building inclusive machine learning algorithms is crucial to help make the world’s information universally useful and accessible. Google researchers are working in this area, including:
- Text Embedding Models Contain Bias. Here's Why That Matters. (Packer et al., Google 2018)
- Measuring and Mitigating Unintended Bias in Text Classification
(Dixon et al., AIES 2018)
- Exercise demonstrating Pinned AUC metric
- Mitigating Unwanted Biases with Adversarial Learning (Zhang et al., AIES 2018)
- Exercise demonstrating Mitigating Unwanted Biases with Adversarial Learning
- Mind the GAP: A Balanced Dataset of Gendered Ambiguous Pronouns (Webster et al., TACL 2018)
- Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations (Beutel et al., FAT/ML 2017)
- No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World (Shankar et al., NIPS 2017 workshop)
- Equality of Opportunity in Supervised Learning (Hardt et al., NIPS 2016)
- Satisfying Real-world Goals with Dataset Constraints (Goh et al., NIPS 2016)
- Designing Fair Auctions:
- Fair Resource Allocation in a Volatile Marketplace (Bateni et al. EC 2016)
- Reservation Exchange Markets for Internet Advertising (Goel et al., LIPics 2016)
- The Reel Truth: Women Aren’t Seen or Heard (Geena Davis Inclusion Quotient)
For more Machine Learning resources from Google, check out google.ai.
