Automated Face Recognition Systems (FRSs), developed using deep learning models, are deployed worldwide for identity verification and facial attribute analysis. The performance of these models is determined by a complex interdependence among the model architecture, optimization/loss function and datasets. Although FRSs have surpassed human-level accuracy, they continue to be disparate against certain demographics. Due to the ubiquity of applications, it is extremely important to understand the impact of the three components-- model architecture, loss function and face image dataset on the accuracy-disparity trade-off to design better, unbiased platforms. In this work, we perform an in-depth analysis of three FRSs for the task of gender prediction, with various architectural modifications resulting in ten deep-learning models coupled with four loss functions and benchmark them on seven face datasets across 266 evaluation configurations. Our results show that all three components have an individual as well as a combined impact on both accuracy and disparity. We identify that datasets have an inherent property that causes them to perform similarly across models, independent of the choice of loss functions. Moreover, the choice of dataset determines the model's perceived bias-- the same model reports bias in opposite directions for three gender-balanced datasets of ``in-the-wild'' face images of popular individuals. The facial embeddings show that the models are unable to generalize a uniform definition of what constitutes a ``female face'' as opposed to a ``male face'', due to dataset diversity. We provide recommendations to model developers on using our study as a blueprint for development and subsequent deployment.

k-way edge based partitioning algorithms for processing large streaming graphs, such as social networks and web crawls, assign each arriving edge to one of the k partitions. This can result in vertices being replicated on multiple partitions. Typically, such partitioning algorithms aim to balance the edge counts across partitions while minimizing the vertex replication. However, such objectives ignore the community structure inherently embedded in the graph, which is an important quality metric for clustering and graph mining applications that subsequently operate on the partitions. To address this gap, we propose a novel optimization goal to maximize the number of local triangles in the partitions as an additional objective. Triangle count is an effective metric to measure the conservation of community structure. Further, we propose TriParts a family of heuristics for online partitioning over an edge stream. They use three complementary state data structures: Bloom Filters, Triangle Map and High degree Map. Each state adds tangible value to meet our objectives. We validate TriParts on six diverse real world graphs with up to 1.6B edges and varying triangle densities. Our best heuristic outperforms the state-of-the-art DBH and HDRF streaming graph partitioners on the triangle-count metric by up to 4–8.3x while maintaining competitive vertex replication factor and edge-balancing. We achieve an ingest rate of 500k edges/sec on a 16 node cluster. We also offer detailed results on the configuration parameters, scalability and overheads of TriParts, and its practical benefits for distributed graph analytics.

Facial Recognition Systems (FRSs) are being developed and deployed all around the world at unprecedented rates. Most platforms are designed in a limited set of countries, but deployed in other regions too, without adequate checkpoints for region-specific requirements. This is especially problematic for Global South countries which lack strong legislation to safeguard persons facing disparate performance of these systems. A combination of unavailability of datasets, lack of understanding of how FRSs function and low-resource bias mitigation measures accentuate the problems at hand. In this work, we propose a self-curated face dataset composed of 6,579 unique male and female sports-persons (cricket players) from eight countries around the world. More than 50% of the dataset is composed of individuals from the Global South countries and is demographically diverse. To aid adversarial audits and robust model training, we curate four adversarial variants of each image in the dataset, leading to more than 40,000 distinct images. We also use this dataset to benchmark five popular facial recognition systems (FRSs), including both commercial and open-source FRSs, for the task of gender prediction (and country prediction for one of the open-source models as an example of red-teaming). Experiments on industrial FRSs reveal accuracies ranging from 98.2% (in case of Azure) to 38.1% (in case of Face++), with a large disparity between males and females in the Global South (max difference of 38.5% in case of Face++). Biases are also observed in all FRSs between females of the Global North and South (max difference of ~50%). A Grad-CAM analysis shows that the nose, forehead and mouth are the regions of interest for one of the open-source FRSs. Based on this crucial observation, we design simple, low-resource bias mitigation solutions using few-shot and novel contrastive learning techniques that demonstrate a significant improvement in accuracy with disparity between males and females reducing from 50% to 1.5% in one of the settings. For the red-teaming experiment using the open-source Deepface model we observe that simple fine-tuning is not very useful while contrastive learning brings steady benefits.

Automatic speech recognition (ASR) systems are designed to transcribe spoken language into written text and find utility in a variety of applications including voice assistants and transcription services. However, it has been observed that state-of-the-art ASR systems which deliver impressive benchmark results, struggle with speakers of certain regions or demographics due to variation in their speech properties. In this work, we describe the curation of a massive speech dataset of 8740 hours consisting of ~9.8K technical lectures in the English language along with their transcripts delivered by instructors representing various parts of Indian demography. The dataset is sourced from the very popular NPTEL MOOC platform. We use the curated dataset to measure the existing disparity in YouTube Automatic Captions and OpenAI Whisper model performance across the diverse demographic traits of speakers in India. While there exists disparity due to gender, native region, age and speech rate of speakers, disparity based on caste is non-existent. We also observe statistically significant disparity across the disciplines of the lectures. These results indicate the need of more inclusive and robust ASR systems and more representational datasets for disparity evaluation in them.

AI models have become extremely popular and accessible to the general public. However, they are continuously under the scanner due to their demonstrable biases toward various sections of the society like people of color and non-binary people. In this study, we audit three existing gender analyzers -uClassify, Readable and HackerFactor, for biases against non-binary individuals. These tools are designed to predict only the cisgender binary labels, which leads to discrimination against non-binary members of the society. We curate two datasets - Reddit comments (660k) and, Tumblr posts (2.05M) and our experimental evaluation shows that the tools are highly inaccurate with the overall accuracy being ≈ 50% on all platforms. Predictions for non-binary comments on all platforms are mostly female, thus propagating the societal bias that non-binary individuals are effeminate. To address this, we fine-tune a BERT multi-label classifier on the two datasets in multiple combinations, observe an overall performance of ≈ 77% on the most realistically deployable setting and a surprisingly higher performance of 90% for the non-binary class. We also audit ChatGPT using zero-shot prompts on a small dataset (due to high pricing) and observe an average accuracy of 58% for Reddit and Tumblr combined (with overall better results for Reddit).