Strategies to improve fairness in artificial intelligence:A systematic ...

This paper presents a systematic literature review of technical, feasible, and practicable solutions to improve fairness in artificial intelligence

The main contribution of this paper is to establish common ground regarding the techniques to be used to improve fairness in artificial intelligence, defined as ...

Strategies to improve fairness in artificial intelligence:A systematic ...

Request PDF | Strategies to improve fairness in artificial intelligence:A systematic literature review | Decisions based on artificial intelligence can ...

Strategies to improve fairness in artificial intelligence:A systematic ...

Article on Strategies to improve fairness in artificial intelligence:A systematic literature review, published in Education for Information 40 on 2024-08-27 ...

Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources ...

At its core, fairness refers to the absence of bias or discrimination in AI systems [26]. However, achieving fairness in AI can be challenging, as it requires ...

Strategies for Mitigating Bias and Ensuring Fairness | by Heka.ai

The preceding analysis has shown that algorithmic strategies can enhance fairness in machine learning model predictions. However, these ...

A survey of recent methods for addressing AI fairness and bias in ...

Existing methods for addressing bias due to imbalanced demographic distributions in datasets can help improve the fairness of AI systems. Increasing the ...

Approaches to Improve Fairness when Deploying AI-based ...

To reduce AI's bias and thereby unfair treatment, we conducted a systematic literature review to identify suitable strategies for the context of hiring. We ...

Improving Fairness in Machine Learning Systems: What Do Industry ...

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention.

Fairness Metrics in AI—Your Step-by-Step Guide to Equitable Systems

AI bias happens when machine learning models make unfair decisions based on biased data or flawed algorithms. This bias can show up in many ...

The Pursuit of Fairness in Artificial Intelligence Models: A Survey

This survey offers a synopsis of the different ways researchers have promoted fairness in AI systems.

Applying Fairness to Artificial Intelligence

Much of the discussion on artificial intelligence and machine learning centers around improving efficiency and performance, but how does AI ...

How to address artificial intelligence fairness | World Economic Forum

Not only should AI systems be fair and exercise caution in amplifying human biases, but they should also make sure they are not a source of ...

Addressing fairness issues in deep learning-based medical image ...

In AI research, fairness can be categorized into individual fairness, group fairness, max-min fairness, counterfactual fairness, etc. Among them ...

The Butterfly Effect in artificial intelligence systems: Implications for ...

Additionally, regular monitoring and evaluation of AI systems for fairness and bias, as well as the implementation of privacy-preserving techniques, can help ...

Fairness and Bias in AI Explained | SS&C Blue Prism

In AI, fairness refers to ensuring that systems operate in that way – equitably. In other words, since AI and machine learning (ML) process lots ...

Fairlearn

In each use case, both societal and technical aspects shape who might be harmed by AI systems and how. There are many complex sources of unfairness and a ...

How to Improve Fairness in AI - YouTube

Comments ; Bias and Fairness in AI Systems with Lead Machine Learning Developer at AltaML, Graham Erickson. Institute for Experiential AI · 949 ...

Artificial Intelligence and Algorithmic Fairness Initiative

While AI systems may offer new opportunities for employers, they also have ... Through the initiative, the EEOC will examine more closely how existing and ...

Addressing Fairness, Bias, and Appropriate Use of Artificial ...

If an algorithm is to be applied to a general population, it is necessary to consider the tuning of the algorithm to ensure fairness to all demographic groups.