Machine learning has emerged with a vast new ecosystem of techniques and infrastructure and we are just beginning to learn their full capabilities. But with the exciting innovations happening, there are also some really concerning problems arising. Forms of bias, stereotyping and unfair determination are being found in computer vision systems, object recognition models, and in natural language processing and word embeddings.
The Conference on Fairness, Accountability, and Transparency (FAT) scheduled on Feb 23 and 24 this year in New York is an annual conference dedicating to bringing theory and practice of fair and interpretable Machine Learning, Information Retrieval, NLP, Computer Vision, Recommender systems, and other technical disciplines. This year’s program includes 17 peer-reviewed papers and 6 tutorials from leading experts in the field. The conference will have three sessions. Session 3 of the two-day conference on Saturday, February 24, is in the field of fairness in computer vision and NLP. In this article, we give our readers a peek into the three papers that have been selected for presentation in Session 3.
You can also check out Session 1 and Session 2, in case you’ve missed them.
The paper talks about substantial disparities in the accuracy of classifying darker and lighter females and males in gender classification systems. The authors have evaluated bias present in automated facial analysis algorithms and datasets with respect to phenotypic subgroups. Using the dermatologist approved Fitzpatrick Skin Type classification system, they have characterized the gender and skin type distribution of two facial analysis benchmarks, IJB-A and Adience. They have also evaluated 3 commercial gender classification systems using this dataset.
The paper studies gender stereotypes and cases of bias in the Hindi movie industry (Bollywood) and propose an algorithm to remove these stereotypes from text. The authors have analyzed movie plots and posters for all movies released since 1970. The gender bias is detected by semantic modeling of plots at sentence and intra-sentence level. Different features like occupation, introductions, associated actions and descriptions are captured to show the pervasiveness of gender bias and stereotype in movies. Next, they have developed an algorithm to generate debiased stories. The proposed debiasing algorithm extracts gender biased graphs from unstructured piece of text in stories from movies and de-bias these graphs to generate plausible unbiased stories.
This paper broadcasts that a knowledge gap exists between data scientists studying NLP and policymakers advocating for the wide adoption of automated social media analysis and moderation. It urges policymakers to understand the capabilities and limits of NLP before endorsing or adopting automated content analysis tools, particularly for making decisions that affect fundamental rights or access to government benefits. It draws on existing research to explain the capabilities and limitations of text classifiers for social media posts and other online content. This paper is aimed at helping researchers and technical experts address the gaps in policymakers knowledge about what is possible with automated text analysis.
The authors have provided an overview of how NLP classifiers work and identified five key limitations of these tools that must be communicated to policymakers:
The paper concludes with recommendations for NLP researchers to bridge the knowledge gap between technical experts and policymakers, including
Don’t miss our coverage on Session 4 and Session 5 on Fair Classification, Fat recommenders, etc.
I remember deciding to pursue my first IT certification, the CompTIA A+. I had signed…
Key takeaways The transformer architecture has proved to be revolutionary in outperforming the classical RNN…
Once we learn how to deploy an Ubuntu server, how to manage users, and how…
Key-takeaways: Clean code isn’t just a nice thing to have or a luxury in software projects; it's a necessity. If we…
While developing a web application, or setting dynamic pages and meta tags we need to deal with…
Software architecture is one of the most discussed topics in the software industry today, and…