# Responsible AI Challenges in End-to-end Machine Learning

arXiv: https://arxiv.org/abs/2101.05967 · HTML: https://mlsanity.com/paper/2101.05967/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 15 Jan 2021.

## Builds on (most cited first, 16 of 16)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [Deep Learning Scaling is Predictable, Empirically](https://mlsanity.com/paper/1712.00409/) (2017)
- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [Age Progression/Regression by Conditional Adversarial Autoencoder](https://mlsanity.com/paper/1702.08423/) (2017)
- [Stronger Data Poisoning Attacks Break Data Sanitization Defenses](https://mlsanity.com/paper/1811.00741/) (2018)
- [A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective](https://mlsanity.com/paper/1811.03402/) (2018)
- [FairBatch: Batch Selection for Model Fairness](https://mlsanity.com/paper/2012.01696/) (2020)
- [Label Sanitization against Label Flipping Poisoning Attacks](https://mlsanity.com/paper/1803.00992/) (2018)
- [Automated Data Slicing for Model Validation:A Big data - AI Integration Approach](https://mlsanity.com/paper/1807.06068/) (2018)
- [FR-Train: A Mutual Information-Based Approach to Fair and Robust Training](https://mlsanity.com/paper/2002.10234/) (2020)
- [Google Dataset Search by the Numbers](https://mlsanity.com/paper/2006.06894/) (2020)
- [FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions](https://mlsanity.com/paper/1911.12587/) (2019)
- [Slice Tuner: A Selective Data Acquisition Framework for Accurate and Fair Machine Learning Models](https://mlsanity.com/paper/2003.04549/) (2020)
- [Data Cleaning for Accurate, Fair, and Robust Models: A Big Data - AI Integration Approach](https://mlsanity.com/paper/1904.10761/) (2019)

## Built on by (most cited first, 2 of 2)

- [SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems](https://mlsanity.com/paper/2308.00994/) (2023)
- [iFlipper: Label Flipping for Individual Fairness](https://mlsanity.com/paper/2209.07047/) (2022)

## Abstract (arXiv)

Responsible AI is becoming critical as AI is widely used in our everyday lives. Many companies that deploy AI publicly state that when training a model, we not only need to improve its accuracy, but also need to guarantee that the model does not discriminate against users (fairness), is resilient to noisy or poisoned data (robustness), is explainable, and more. In addition, these objectives are not only relevant to model training, but to all steps of end-to-end machine learning, which include data collection, data cleaning and validation, model training, model evaluation, and model management and serving. Finally, responsible AI is conceptually challenging, and supporting all the objectives must be as easy as possible. We thus propose three key research directions towards this vision - depth, breadth, and usability - to measure progress and introduce our ongoing research. First, responsible AI must be deeply supported where multiple objectives like fairness and robust must be handled together. To this end, we propose FR-Train, a holistic framework for fair and robust model training in the presence of data bias and poisoning. Second, responsible AI must be broadly supported, preferably in all steps of machine learning. Currently we focus on the data pre-processing steps and propose Slice Tuner, a selective data acquisition framework for training fair and accurate models, and MLClean, a data cleaning framework that also improves fairness and robustness. Finally, responsible AI must be usable where the techniques must be easy to deploy and actionable. We propose FairBatch, a batch selection approach for fairness that is effective and simple to use, and Slice Finder, a model evaluation tool that automatically finds problematic slices. We believe we scratched the surface of responsible AI for end-to-end machine learning and suggest research challenges moving forward.