Recent advancements in machine learning and deep learning have brought algorithmic fairness into sharp focus, illuminating concerns over discriminatory decision making that negatively impacts certain individuals or groups. These concerns have manifested in legal, ethical, and societal challenges, including the erosion of trust in intelligent systems. In response, this survey delves into the existing literature on algorithmic fairness, specifically highlighting its multifaceted social consequences. We introduce a novel taxonomy based on 'tolerance', a term we define as the degree to which variations in fairness outcomes are acceptable, providing a structured approach to understanding the subtleties of fairness within algorithmic decisions. Our systematic review covers diverse industries, revealing critical insights into the balance between algorithmic decision making and social equity. By synthesizing these insights, we outline a series of emerging challenges and propose strategic directions for future research and policy making, with the goal of advancing the field towards more equitable algorithmic systems.

With the powerful performance of Artificial Intelligence (AI) also comes prevalent ethical issues. Though governments and corporations have curated multiple AI ethics guidelines to curb unethical behavior of AI, the effect has been limited, probably due to the vagueness of the guidelines. In this paper, we take a closer look at how AI ethics issues take place in real world, in order to have a more in-depth and nuanced understanding of different ethical issues as well as their social impact. With a content analysis of AI Incident Database, which is an effort to prevent repeated real world AI failures by cataloging incidents, we identified 13 application areas which often see unethical use of AI, with intelligent service robots, language/vision models and autonomous driving taking the lead. Ethical issues appear in 8 different forms, from inappropriate use and racial discrimination, to physical safety and unfair algorithm. With this taxonomy of AI ethics issues, we aim to provide AI practitioners with a practical guideline when trying to deploy AI applications ethically.

Uber is facing further claims for compensation over racial discrimination from drivers who say they had been falsely dismissed because of malfunctioning face recognition technology. The claims have emerged after Uber introduced an automated system to check the ID of drivers operating its services in April last year. Each time a driver checks in for work, they must take a selfie picture that is then compared, using an automated system, to one on their Uber account profile. Pa Edrissa Manjang, who worked for the Uber Eats takeaway courier service in London, has launched an employment tribunal claim alleging his account was illegally deactivated. He says the automated facial-verification software wrongly decided his selfie pictures were of someone else on several occasions.

Sticks and stones may break my bones, but words will never hurt me. This was a mantra I picked up on the playground at elementary school--something I repeated over and over again anytime I came face to face with racism. It was a coping mechanism meant to guard my heart from the cacophony of discriminatory comments that shaped me as a young Korean American girl growing up in predominantly white spaces. But now that I'm well into adulthood, I think about the girls of color who are also being taught to pretend that words don't hurt--and the people this way of thinking actually protects. It's hard to escape the unrelenting consequences of racism: In the past year alone, we lost Breonna Taylor, George Floyd, Ahmaud Arbery, and the six women of Asian descent murdered in Atlanta (Xiaojie "Emily" Tan, Daoyou Feng, Suncha Kim, Yong Ae Yue, Soon Chung Park, Hyun Jung Grant) at the hands of this insidious disease--and those are just the names that were in the headlines. If we don't acknowledge ...

Researchers often lack the necessary data to credibly estimate racial discrimination in policing. In particular, police administrative records lack information on civilians police observe but do not investigate. In this article, we show that if police racially discriminate when choosing whom to investigate, analyses using administrative records to estimate racial discrimination in police behavior are statistically biased, and many quantities of interest are unidentified--even among investigated individuals--absent strong and untestable assumptions. Using principal stratification in a causal mediation framework, we derive the exact form of the statistical bias that results from traditional estimation. We develop a bias-correction procedure and nonparametric sharp bounds for race effects, replicate published findings, and show the traditional estimator can severely underestimate levels of racially biased policing or mask discrimination entirely.

A high-level goal of many AI projects is to address the ethical implications of algorithms along the lines of fairness and discrimination. It is a known fact that algorithms can facilitate illegal discrimination. For example, it may not surprise that each investor wants to put more capital in loans with a high return of investment and low risk. A modern idea is to use a machine learning model to decide, based on the sliver of known information about the outcome of past loans, which future loan requests give the largest chance of the borrower fully paying it back while achieving the best trade-off with high returns (high-interest rate). There's one problem: the model is trained on historical data, and poor uneducated people, often racial minorities or people with less working experience have a historical trend of being more likely to succumb to loan charge-off than the general population.

It was a sunny afternoon last month when my smartphone decided to ignore me. Well, it didn't ignore my African American self, but it did ignore the carved face of a black man in a sculpture I was trying to photograph. Instead, it bracketed the carving of a white man's face to indicate that it was "seeing" him, while not bracketing the black face in the center of the frame. This problem of artificial intelligence having difficulty with black faces has been around for at least a decade. In addition to causing issues with commonplace activities, like taking pictures, it also raises a bigger question of how we guarantee equal opportunity across race in a world run on AI. AI is already ubiquitous and powerful, and it becomes more so every year.

Sinakhone Keodara reached his breaking point last July. Loading up Grindr, the gay dating app that presents users with potential mates in close geographical proximity to them, the founder of a Los Angeles-based Asian television streaming service came across the profile of an elderly white man. He struck up a conversation, and received a three-word response: "Asian, ew gross." He is now considering suing Grindr for racial discrimination. For black and ethnic minority singletons, dipping a toe into the water of dating apps can involve subjecting yourself to racist abuse and crass intolerance.