Detecting Bias in Black-Box Models Using Transparent Model Distillation. - Sarah Tan; Rich Caruana; Giles Hooker; Yin Lou (CoRR, 2017)

Interpretable Approaches to Detect Bias in Black-Box Models. - Sarah Tan (AIES, 2018)

Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation. - Sarah Tan; Rich Caruana; Giles Hooker; Yin Lou (AIES, 2018)

Transparent Model Distillation. - Sarah Tan; Rich Caruana; Giles Hooker; Albert Gordo (CoRR, 2018)

Investigating Human + Machine Complementarity for Recidivism Predictions. - Sarah Tan; Julius Adebayo; Kori Inkpen; Ece Kamar (CoRR, 2018)

Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models. - Xuezhou Zhang; Sarah Tan; Paul Koch; Yin Lou; Urszula Chajewska; Rich Caruana (CoRR, 2018)

Axiomatic Interpretability for Multiclass Additive Models. - Xuezhou Zhang; Sarah Tan; Paul Koch; Yin Lou; Urszula Chajewska; Rich Caruana (KDD, 2019)

Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models. - Benjamin J. Lengerich; Sarah Tan; Chun-Hao Chang; Giles Hooker; Rich Caruana (AISTATS, 2020)

Do I Look Like a Criminal? Examining how Race Presentation Impacts Human Judgement of Recidivism. - Keri Mallari; Kori Inkpen; Paul Johns; Sarah Tan; Divya Ramesh; Ece Kamar (CHI, 2020)

Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable. - Sarah Tan; Matvey Soloviev; Giles Hooker; Martin T. Wells (FODS, 2020)

How Interpretable and Trustworthy are GAMs? - Chun-Hao Chang; Sarah Tan; Benjamin J. Lengerich; Anna Goldenberg; Rich Caruana (KDD, 2021)

Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data. - Zhi Chen 009; Sarah Tan; Harsha Nori; Kori Inkpen; Yin Lou; Rich Caruana (PKDD/ECML Workshops (1), 2021)

Distilling Heterogeneity: From Explanations of Heterogeneous Treatment Effect Models to Interpretable Policies. - Han Wu; Sarah Tan; Weiwei Li 006; Mia Garrard; Adam Obeng; Drew Dimmery; Shaun Singh; Hanson Wang; Daniel R. Jiang; Eytan Bakshy (CoRR, 2021)

Interpretable Personalized Experimentation. - Han Wu; Sarah Tan; Weiwei Li 006; Mia Garrard; Adam Obeng; Drew Dimmery; Shaun Singh; Hanson Wang; Daniel R. Jiang; Eytan Bakshy (KDD, 2022)

Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes. - Leon Yao; Caroline Lo; Israel Nir; Sarah Tan; Ariel Evnine; Adam Lerer; Alex Peysakhovich (CoRR, 2022)

Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? - Zhi Chen 009; Sarah Tan; Urszula Chajewska; Cynthia Rudin; Rich Caruana (CHIL, 2023)

Error Discovery By Clustering Influence Embeddings. - Fulton Wang; Julius Adebayo; Sarah Tan; Diego Garcia-Olano; Narine Kokhlikyan (NeurIPS, 2023)

Practical Policy Optimization with Personalized Experimentation. - Mia Garrard; Hanson Wang; Ben Letham; Shaun Singh; Abbas Kazerouni; Sarah Tan; Zehui Wang; Yin Huang; Yichun Hu; Chad Zhou; Norm Zhou; Eytan Bakshy (CoRR, 2023)

Considerations when learning additive explanations for black-box models. - Sarah Tan; Giles Hooker; Paul Koch; Albert Gordo; Rich Caruana (Mach. Learn., 2023)

MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases. - Rithesh Murthy; Liangwei Yang; Juntao Tan; Tulika Manoj Awalgaonkar; Yilun Zhou; Shelby Heinecke; Sachin Desai; Jason Wu; Ran Xu 001; Sarah Tan; Jianguo Zhang 005; Zhiwei Liu 001; et al. (CoRR, 2024)

XForecast: Evaluating Natural Language Explanations for Time Series Forecasting. - Taha Aksu; Chenghao Liu; Amrita Saha; Sarah Tan; Caiming Xiong; Doyen Sahoo (CoRR, 2024)

How Aligned are Generative Models to Humans in High-Stakes Decision-Making? - Sarah Tan; Keri Mallari; Julius Adebayo; Albert Gordo; Martin T. Wells; Kori Inkpen (CoRR, 2024)

Evaluating Cultural and Social Awareness of LLM Web Agents. - Haoyi Qiu; Alexander R. Fabbri; Divyansh Agarwal; Kung-Hsiang Huang; Sarah Tan; Nanyun Peng 001; Chien-Sheng Wu (NAACL (Findings), 2025)

Enduring Disparities in the Workplace: A Pilot Study in the AI Community. - Yunusa Simpa Abdulsalam; Siobhan Mackenzie Hall; Ana María Quintero-Ossa; William Agnew; Carla Muntean; Sarah Tan; Ashley Heady; Savannah Thais; Jessica Schrouff (CoRR, 2025)