Bias and Discrimination in AI: A Cross-Disciplinary Perspective

Ferrer, Xavier; Nuenen, Tom van; Such, José M.; Coté, Mark; Criado, Natalia

doi:10.1109/mts.2021.3056293

Cited by 143 publications

(47 citation statements)

References 24 publications

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“…(5) Risk of injustice and gender discrimination: water, gender, and AI are inextricably linked, as the digitalisation of water will likely enhance existing power imbalances in the sector and the lack of representation of women and other gendered identities in water-related interventions. To counteract the bias incorporated in AI applications see also [58], gender should be mainstreamed in planning, implementation, and evaluation of programs. Methodologies such as gender analysis, social mapping, and sexand gender-disaggregated data should be further encouraged.…”

Section: Discussion: a Hydrosocial Approach To Digital Watermentioning

confidence: 99%

The Digital Revolution in the Urban Water Cycle and Its Ethical–Political Implications: A Critical Perspective

et al. 2022

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The development and application of new forms of automation and monitoring, data mining, and the use of AI data sources and knowledge management tools in the water sector has been compared to a ‘digital revolution’. The state-of-the-art literature has analysed this transformation from predominantly technical and positive perspectives, emphasising the benefits of digitalisation in the water sector. Meanwhile, there is a conspicuous lack of critical literature on this topic. To bridge this gap, the paper advances a critical overview of the state-of-the art scholarship on water digitalisation, looking at the sociopolitical and ethical concerns these technologies generate. We did this by analysing relevant AI applications at each of the three levels of the UWC: technical, operational, and sociopolitical. By drawing on the precepts of urban political ecology, we propose a hydrosocial approach to the so-called ‘digital water ‘, which aims to overcome the one-sidedness of the technocratic and/or positive approaches to this issue. Thus, the contribution of this article is a new theoretical framework which can be operationalised in order to analyse the ethical–political implications of the deployment of AI in urban water management. From the overview of opportunities and concerns presented in this paper, it emerges that a hydrosocial approach to digital water management is timely and necessary. The proposed framework envisions AI as a force in the service of the human right to water, the implementation of which needs to be (1) critical, in that it takes into consideration gender, race, class, and other sources of discrimination and orients algorithms according to key principles and values; (2) democratic and participatory, i.e., it combines a concern for efficiency with sensitivity to issues of fairness or justice; and (3) interdisciplinary, meaning that it integrates social sciences and natural sciences from the outset in all applications.

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Section: Discussion: a Hydrosocial Approach To Digital Watermentioning

confidence: 99%

The Digital Revolution in the Urban Water Cycle and Its Ethical–Political Implications: A Critical Perspective

et al. 2022

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“…Empirical findings have shown that data-driven methods can unintentionally encode existing human biases and introduce new ones [18]. According to [19], three causes of biases are:…”

Section: Causes Of Bias In Ai Systemsmentioning

confidence: 99%

Fairness Score and process standardization: framework for fairness certification in artificial intelligence systems

Avinash¹,

Harsh²,

Agarwal

2022

AI Ethics

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Decisions made by various artificial intelligence (AI) systems greatly influence our day-to-day lives. With the increasing use of AI systems, it becomes crucial to know that they are fair, identify the underlying biases in their decision-making, and create a standardized framework to ascertain their fairness. Biases in AI systems lead to unintended ethical, social and even legal issues. In this paper, we propose a novel and versatile Fairness Score and Bias Index for measuring the fairness of a supervised learning AI system. We also propose a standard operating procedure (SOP) for issuing Fairness Certification for such data-driven applications. Fairness Score and audit process standardization will ensure quality, reduce ambiguity, enable comparison and improve the trustworthiness of the AI systems. It will also provide a framework to operationalize the concept of fairness and facilitate the commercial deployment of such systems. Furthermore, a Fairness Certificate issued by a designated third-party auditing agency following the standardized process would boost the conviction of the organizations in the AI systems that they intend to deploy. The Bias Index proposed in this paper reveals comparative bias amongst the various protected attributes within the dataset, and the Fairness Score provides overall fairness with an ideal value of 1. To substantiate the proposed framework, we iteratively train a model on biased and unbiased data using multiple labelled datasets and check that the Fairness Score, Bias Indexes and the proposed process correctly identify the biases and judge the fairness.

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“…While no consistent definition of fairness is yet established and the perception of fairness can vary across consumers [63], fairness is often considered as the absence of any bias, prejudice, favoritism, mistreatment toward individuals, group, classes, or social categories based on their inherent or acquired characteristics [10]. Often, fairness and unfairness are also related to the problem of (digital) discrimination [25,28], which is often characterized as an unfair or unequal treatment of individuals, groups, classes or social categories according to certain characteristics. Discrimination is another phenomenon, which may be reinforced by recommender systems, in particular when they operate on data that have inherent biases.…”

Section: User Modeling Personalization and Engagementmentioning

confidence: 99%

Responsible media technology and AI: challenges and research directions

et al. 2021

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The last two decades have witnessed major disruptions to the traditional media industry as a result of technological breakthroughs. New opportunities and challenges continue to arise, most recently as a result of the rapid advance and adoption of artificial intelligence technologies. On the one hand, the broad adoption of these technologies may introduce new opportunities for diversifying media offerings, fighting disinformation, and advancing data-driven journalism. On the other hand, techniques such as algorithmic content selection and user personalization can introduce risks and societal threats. The challenge of balancing these opportunities and benefits against their potential for negative impacts underscores the need for more research in responsible media technology. In this paper, we first describe the major challenges—both for societies and the media industry—that come with modern media technology. We then outline various places in the media production and dissemination chain, where research gaps exist, where better technical approaches are needed, and where technology must be designed in a way that can effectively support responsible editorial processes and principles. We argue that a comprehensive approach to research in responsible media technology, leveraging an interdisciplinary approach and a close cooperation between the media industry and academic institutions, is urgently needed.

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Bias and Discrimination in AI: A Cross-Disciplinary Perspective

Cited by 143 publications

References 24 publications

The Digital Revolution in the Urban Water Cycle and Its Ethical–Political Implications: A Critical Perspective

The Digital Revolution in the Urban Water Cycle and Its Ethical–Political Implications: A Critical Perspective

Fairness Score and process standardization: framework for fairness certification in artificial intelligence systems

Responsible media technology and AI: challenges and research directions

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