Detecting race and gender bias in visual representation of AI on web search engines

Emilio Ferrara

16 April 2023

The significant advancements in applying artificial intelligence (AI) to healthcare decision-making, medical diagnosis, and other domains have simultaneously raised concerns about the fairness and bias of AI systems. This is particularly critical in areas like healthcare, employment, criminal justice, credit scoring, and increasingly, in generative AI models (GenAI) that produce synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including generative biases that affect the representation of individuals in synthetic data. This survey study offers a succinct, comprehensive overview of fairness and bias in AI, addressing their sources, impacts, and mitigation strategies. We review sources of bias, such as data, algorithm, and human decision biases—highlighting the emergent issue of generative AI bias, where models may reproduce and amplify societal stereotypes. We assess the societal impact of biased AI systems, focusing on perpetuating inequalities and reinforcing harmful stereotypes, especially as generative AI becomes more prevalent in creating content that influences public perception. We explore various proposed mitigation strategies, discuss the ethical considerations of their implementation, and emphasize the need for interdisciplinary collaboration to ensure effectiveness. Through a systematic literature review spanning multiple academic disciplines, we present definitions of AI bias and its different types, including a detailed look at generative AI bias. We discuss the negative impacts of AI bias on individuals and society and provide an overview of current approaches to mitigate AI bias, including data pre-processing, model selection, and post-processing. We emphasize the unique challenges presented by generative AI models and the importance of strategies specifically tailored to address these. Addressing bias in AI requires a holistic approach involving diverse and representative datasets, enhanced transparency and accountability in AI systems, and the exploration of alternative AI paradigms that prioritize fairness and ethical considerations. This survey contributes to the ongoing discussion on developing fair and unbiased AI systems by providing an overview of the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the emerging field of generative AI.

M. Scheuerman Kandrea Wade Caitlin Lustig + 1 lainnya

28 Mei 2020

Race and gender have long sociopolitical histories of classification in technical infrastructures-from the passport to social media. Facial analysis technologies are particularly pertinent to understanding how identity is operationalized in new technical systems. What facial analysis technologies can do is determined by the data available to train and evaluate them with. In this study, we specifically focus on this data by examining how race and gender are defined and annotated in image databases used for facial analysis. We found that the majority of image databases rarely contain underlying source material for how those identities are defined. Further, when they are annotated with race and gender information, database authors rarely describe the process of annotation. Instead, classifications of race and gender are portrayed as insignificant, indisputable, and apolitical. We discuss the limitations of these approaches given the sociohistorical nature of race and gender. We posit that the lack of critical engagement with this nature renders databases opaque and less trustworthy. We conclude by encouraging database authors to address both the histories of classification inherently embedded into race and gender, as well as their positionality in embedding such classifications.

Aleksandra Urman R. Ulloa M. Makhortykh

4 Oktober 2021

By filtering and ranking information, search engines shape how individuals perceive both the present and past events. However, these information curation mechanisms are prone to malperformance that can misinform their users. In this article, we examine how search malperformance can influence representation of traumatic past by investigating image search outputs of six search engines in relation to the Holocaust in English and Russian. Our findings indicate that besides two common themes - commemoration and liberation of camps - there is substantial variation in visual representation of the Holocaust between search engines and languages. We also observe several instances of search malperformance, including content propagating antisemitism and Holocaust denial, misattributed images, and disproportionate visibility of specific Holocaust aspects that might result in its distorted perception by the public.

Gaelle Cachat-Rosset Alain Klarsfeld

22 Februari 2023

ABSTRACT Artificial intelligence (AI) is present everywhere in the lives of individuals. Unfortunately, several cases of discrimination by AI systems have already been reported. Scholars have warned on risks of AI reproducing existing inequalities or even amplifying them. To tackle these risks and promote responsible AI, many ethics guidelines for AI have emerged recently, including diversity, equity, and inclusion (DEI) principles and practices. However, little is known about the DEI content of these guidelines, and to what extent they meet the most relevant accumulated knowledge from DEI literature. We performed a semi-systematic literature review of the AI guidelines regarding DEI stakes and analyzed 46 guidelines published from 2015 to today. We fleshed out the 14 DEI principles and the 18 DEI practices recommended underlying these 46 guidelines. We found that the guidelines mostly encourage one of the DEI management paradigms, namely fairness, justice, and nondiscrimination, in a limited compliance approach. We found that narrow technical practices are favored over holistic ones. Finally, we conclude that recommended practices for implementing DEI principles in AI should include actions aimed at directly influencing AI actors’ behaviors and awareness of DEI risks, rather than just stating intentions and programs.

Shrimai Prabhumoye E. Hovy

1 Agustus 2021

Abstract Recently, there has been an increased interest in demographically grounded bias in natural language processing (NLP) applications. Much of the recent work has focused on describing bias and providing an overview of bias in a larger context. Here, we provide a simple, actionable summary of this recent work. We outline five sources where bias can occur in NLP systems: (1) the data, (2) the annotation process, (3) the input representations, (4) the models, and finally (5) the research design (or how we conceptualize our research). We explore each of the bias sources in detail in this article, including examples and links to related work, as well as potential counter‐measures.