Blockchain and fairness in the VCM: Customer-Centric Fairness: Unraveling Blockchain's Potential in Voluntary Carbon Trading

Carbon trading sets a price on greenhouse gases (GHG), enabling countries and companies to buy emission rights. The carbon market includes the compliant market, regulated by governments, and the voluntary carbon market (VCM), which lacks strict regulation and allows anyone to offset emissions. In the VCM, carbon credits are created through...

Data Model for Computer Vision Explainability, Fairness, and Robustness

In recent years, there has been a growing interest among researchers in the explainability, fairness, and robustness of Computer Vision models. While studies have explored the usability of these models for end users, limited research has delved into the challenges and requirements faced by researchers investigating these requirements. This study...

Reimagining the smart allocation of road space in Amsterdam for fairness

Roads are currently governed by relatively static rules communicated via road signs, road surface markings and navigation apps. However they could also be dynamic, allowing road space to be allocated for a multitude of activities over time. The Code the Streets project envisions a digital system for managing urban mobility in Amsterdam that...

Surfacing Differences in Practices When Building Fair Machine Learning Systems with Fairness Toolkits: an Empirical Study

The ability to identify and mitigate various risks and harms of using Machine Learning models in industry is an essential task. Specifically because these may produce harmful outcomes for stakeholders, including unfair or discriminatory results. Due to this there has been substantial research into the concepts of fairness and its metrics, bias...

Investigation of the fairness metrics in automated negotiations

This paper aims to define the broad concept of fairness and investigate how it can be measured, especially considering fairness in automated negotiations. The report relies on the work on fairness issues that have been derived from the research of C. Albin [1]. Firstly, the paper elaborates on different fairness metrics from the literature...

BOFA - A Framework for Fairness in Automated Negotiations

As automated negotiating agents become more and more part of our daily life, additional care needs to be taken that the agents can negotiate fairly. Humans each have their own intrinsic view on fairness, which affects the negotiation processes and the degree to which the outcome is viewed as satisfactory. However, most current agents are built...

Integrating fairness in opponent modeling

Is there a way to incorporate fairness in the opponent modeling component of an automated agent? Since opponent modeling plays an important role in a negotiation strategy, it is reasonable to research how fairness can be integrated into this component, as it influences the outcome of the negotiation. A first step towards finding an answer to...

The Unintended Consequences Fairness Brings to Automated Negotiation

In this paper, the unintended consequences, also named edge cases in this paper, of integrating fairness into the automated negotiation process are researched. By finding these unintended consequences, we can deal with them accordingly or avoid them, as to not cause any problems with our fairness metric that might make our negotiation process...

Fairness Information Maximization on social media

The rapid growth of the Internet use has allowed social networks to become the most effective means for marketing, leading to the emergence of "viral marketing" as a business model. The biggest challenge that is facing "viral marketing" is selecting seed users from the whole user set to form a "seed-set" to spread the influence and maximize the...

Causal Fairness of Machine Learning: Bridging Individual- and Population-Level Fairness Methods

As Machine Learning models are being applied to a wide range of fields, the potential impact that these algorithms can have on people's lives is increasing. In a growing number of applications, such as criminal justice, financial assessments, job and college applications, the data points are indeed people's profiles. Therefore, in the presence...

Design and Analysis of a framework for Dynamic Selection of TCP Congestion Control Algorithms

Introduction of new, more advanced services to the networking paradigm has led to an increased heterogeneity of media types and network traffic. Although several transport protocols have been developed over the years to cater to the Quality-of-Service requirements of these network services, the dynamic nature of the network condition is a...

Multi-Level Fairness Framework: A Socio-Technical framework for Fairness Requirements Engineering in Machine Learning

Machine Learning models are begin increasingly used within the industry such as by financial institutions, governments and commercial companies. In the past few years, there have been several incidents where these ML models show discriminatory behavior towards particular groups of people, leading to unfair decisions that can have negative...

Design for fairness in AI: Cooking a fair AI Dish

Artificial intelligence (AI) is an emerging field which unleashes massive new (business) opportunities. The potential growth and broad application of the AI technology has great economic benefits however also severe societal implications. Simultaneously, ethical challenges arise with its development. Questions of values and ethics are becoming...

On the fairness of crowdsourced training data and Machine Learning models for the prediction of subjective properties. The case of sentence toxicity: To be or not to be #$@&%*! toxic? To be or not to be fair?

Training machine learning (ML) models for natural language processing usually requires lots of data that is often acquired through crowdsourcing. In crowdsourcing, crowd workers annotate data samples according to one or more properties, such as the sentiment of a sentence, the violence of a video segment, the aesthetics of an image, ... To...