The now well-known impossibility results of algorithmic fairness demonstrate that an error-prone predictive model cannot simultaneously satisfy two plausible conditions for group fairness apart from exceptional circumstances where groups exhibit equal base rates. The results sparked, and continue to shape, ...
Algorithmic fairness has attracted increasing attention in the machine learning community. Various definitions are proposed in the literature, but the differences and connections among them are not clearly addressed. In this paper, we review and reflect on various fairness notions previously proposed in ...
A simple way to think about AI is as a series of nested or derivative concepts that have emerged over more than 70 years: Directly underneath AI, we have machine learning, which involves creatingmodelsby training an algorithm to make predictions or decisions based on data. It encompasses a br...
Algorithmic bias results in unfair outcomes due to skewed or limited input data, unfair algorithms, or exclusionary practices during AI development.
Act, a bill introduced in 2023, which would remove tax benefits for investors owning 50 or more single-family homes to rent, and the second is the Preventing the Algorithmic Facilitation of Rental Housing Cartels Act, a bill introduced in 2024 to crack down on companies inflating rental prices...
The article "If the Difference Principle Won't Make a Real Difference in Algorithmic Fairness, What Will?: Response to 'Rawlsian Algorithmic Fairness and a Missing Aggregation Property of the Difference Principle'" examines the challenges of applying John Rawls' difference principle to algorithmic fai...
Protections against discrimination by algorithms.Algorithmic discrimination is when automated systems contribute to unjustified different treatment of people based on their race, color, ethnicity, sex, religion, age, and more. Protections against abusive data practices,via built-in safeguards. Users should...
Algorithmic opacity is one of the main concerns associated with LLMs. These modes are often labeled as ‘black box’ models because of their complexity, which makes it impossible to monitor their reasoning and inner workings. AI providers of proprietary LLMs are often reluctant to provide ...
Using algorithmic fairness tools and frameworks can aid in detecting and mitigating bias in machine learning models.AI Fairness 360, an open source toolkit developed by IBM, provides various metrics to detect bias in data sets and machine learning models, along with algorithms to mitigate bias and...
the discrimination becomes objectionable when it places privileged groups at systematic advantage and certain unprivileged groups at systematic disadvantage, potentially causing varied harms. To encourage fairness, practitioners can try to minimize algorithmic bias across data collection and model design, and ...