# How Technology Impacts and Compares to Humans in Socially Consequential Arenas

arXiv: https://arxiv.org/abs/2211.03554 · HTML: https://mlsanity.com/paper/2211.03554/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 317 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 25 of 25)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Deep Face Recognition: A Survey](https://mlsanity.com/paper/1804.06655/) (2018)
- [One weird trick for parallelizing convolutional neural networks](https://mlsanity.com/paper/1404.5997/) (2014)
- [Fairness without Demographics through Adversarially Reweighted Learning](https://mlsanity.com/paper/2006.13114/) (2020)
- [Censoring Representations with an Adversary](https://mlsanity.com/paper/1511.05897/) (2015)
- [Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations](https://mlsanity.com/paper/1707.00075/) (2017)
- [xView: Objects in Context in Overhead Imagery](https://mlsanity.com/paper/1802.07856/) (2018)
- [The Frontiers of Fairness in Machine Learning](https://mlsanity.com/paper/1810.08810/) (2018)
- [Towards Measuring Fairness in AI: the Casual Conversations Dataset](https://mlsanity.com/paper/2104.02821/) (2021)
- [Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction](https://mlsanity.com/paper/1807.00199/) (2018)
- [ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning](https://mlsanity.com/paper/2010.06398/) (2020)
- [Minimax Group Fairness: Algorithms and Experiments](https://mlsanity.com/paper/2011.03108/) (2020)
- [Group Fairness in Bandit Arm Selection](https://mlsanity.com/paper/1912.03802/) (2019)
- [Robustness Disparities in Commercial Face Detection](https://mlsanity.com/paper/2108.12508/) (2021)
- [A Deep Dive into Dataset Imbalance and Bias in Face Identification](https://mlsanity.com/paper/2203.08235/) (2022)
- [InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender Diversity](https://mlsanity.com/paper/1712.00193/) (2017)
- [Comparing Human and Machine Bias in Face Recognition](https://mlsanity.com/paper/2110.08396/) (2021)
- [Are Commercial Face Detection Models as Biased as Academic Models?](https://mlsanity.com/paper/2201.10047/) (2022)
- [The Affiliate Matching Problem: On Labor Markets where Firms are Also Interested in the Placement of Previous Workers](https://mlsanity.com/paper/2009.11867/) (2020)
- [Overhead Detection: Beyond 8-bits and RGB](https://mlsanity.com/paper/1808.02443/) (2018)
- [How good is good enough for COVID19 apps? The influence of benefits, accuracy, and privacy on willingness to adopt](https://mlsanity.com/paper/2005.04343/) (2020)
- [COVID-19 Contact-Tracing Mobile Apps: Evaluation and Assessment for Decision Makers](https://mlsanity.com/paper/2006.05812/) (2020)
- [The Dichotomous Affiliate Stable Matching Problem: Approval-Based Matching with Applicant-Employer Relations](https://mlsanity.com/paper/2202.11095/) (2022)

## Abstract (arXiv)

One of the main promises of technology development is for it to be adopted by people, organizations, societies, and governments -- incorporated into their life, work stream, or processes. Often, this is socially beneficial as it automates mundane tasks, frees up more time for other more important things, or otherwise improves the lives of those who use the technology. However, these beneficial results do not apply in every scenario and may not impact everyone in a system the same way. Sometimes a technology is developed which produces both benefits and inflicts some harm. These harms may come at a higher cost to some people than others, raising the question: it how are benefits and harms weighed when deciding if and how a socially consequential technology gets developed? The most natural way to answer this question, and in fact how people first approach it, is to compare the new technology to what used to exist. As such, in this work, I make comparative analyses between humans and machines in three scenarios and seek to understand how sentiment about a technology, performance of that technology, and the impacts of that technology combine to influence how one decides to answer my main research question.