Towards Explainability for AI Fairness

Zhou, Jianlong; Chen, Fang; Holzinger, Andreas

doi:10.1007/978-3-031-04083-2_18

Cited by 27 publications

(12 citation statements)

References 29 publications

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“…The problems that XAI should tackle often require interdisciplinary research [17,41,44]. Preventing discrimination [24,41,74], increasing trustworthiness [37,41,46], allocating responsibility [13,41,59], and generally, promoting human well-being [27,46] is, in principle, possible with explainability -as long as researchers from different disciplines can come together to work on it. Accordingly, confusion in the field may postpone the potentially vast social benefits XAI promises to bring about.…”

Section: Discussionmentioning

confidence: 99%

A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

Speith

2022

2022 ACM Conference on Fairness, Accountability, and Transparency

164

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The recent surge in publications related to explainable artificial intelligence (XAI) has led to an almost insurmountable wall if one wants to get started or stay up to date with XAI. For this reason, articles and reviews that present taxonomies of XAI methods seem to be a welcomed way to get an overview of the field. Building on this idea, there is currently a trend of producing such taxonomies, leading to several competing approaches to construct them. In this paper, we will review recent approaches to constructing taxonomies of XAI methods and discuss general challenges concerning them as well as their individual advantages and limitations. Our review is intended to help scholars be aware of challenges current taxonomies face. As we will argue, when charting the field of XAI, it may not be sufficient to rely on one of the approaches we found. To amend this problem, we will propose and discuss three possible solutions: a new taxonomy that incorporates the reviewed ones, a database of XAI methods, and a decision tree to help choose fitting methods. CCS CONCEPTS• General and reference → Surveys and overviews; • Computing methodologies → Artificial intelligence.

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Section: Discussionmentioning

confidence: 99%

A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

Speith

2022

2022 ACM Conference on Fairness, Accountability, and Transparency

164

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“…A comprehensive examination of fairness and equity in the development and deployment of AI in healthcare is essential. Addressing these challenges is vital for unlocking the full potential of AI while safeguarding privacy, promoting transparency, and ensuring equitable healthcare outcomes for all [ 72 , 73 , 74 , 75 ].…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes

Cau,

Pisu,

Suri

et al. 2024

Diagnostics

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Artificial intelligence (AI) is rapidly being applied to the medical field, especially in the cardiovascular domain. AI approaches have demonstrated their applicability in the detection, diagnosis, and management of several cardiovascular diseases, enhancing disease stratification and typing. Cardiomyopathies are a leading cause of heart failure and life-threatening ventricular arrhythmias. Identifying the etiologies is fundamental for the management and diagnostic pathway of these heart muscle diseases, requiring the integration of various data, including personal and family history, clinical examination, electrocardiography, and laboratory investigations, as well as multimodality imaging, making the clinical diagnosis challenging. In this scenario, AI has demonstrated its capability to capture subtle connections from a multitude of multiparametric datasets, enabling the discovery of hidden relationships in data and handling more complex tasks than traditional methods. This review aims to present a comprehensive overview of the main concepts related to AI and its subset. Additionally, we review the existing literature on AI-based models in the differential diagnosis of cardiomyopathy phenotypes, and we finally examine the advantages and limitations of these AI approaches.

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“…Ensuring sufficient interpretability can help AI research scientists and developers to debug the models they are building and to uncover otherwise hidden or unforeseeable failure modes, thereby improving downstream model functioning and performance (Bastings et al, 2022;Luo & Specia, 2024;. It can also help detect and mitigate discriminatory biases that may be buried within model architectures (Alikhademi et al, 2021;Zhao, Chen, et al, 2024;Zhou et al, 2020). Furnishing understandable and accessible explanations of the rationale behind system outputs can likewise help to establish the lawfulness of AI systems (e.g., their compliance with data protection law and equality law) (Chuang et al, 2024; ICO/Turing, 2020) as well as to ensure responsible and trustworthy implementation by system deployers, who are better equipped to grasp system capabilities, limitations, and flaws and to integrate system outputs into their own reasoning, judgment, and experience (ICO/Turing, 2020; Leslie, Rincón, et al, 2024).…”

Section: Risks From Model Scaling: Model Opacity and Complexitymentioning

confidence: 99%

Future Shock: Generative AI and the International AI Policy and Governance Crisis

Leslie,

Perini

2024

Harvard Data Science Review

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stakeholders from across industry, academia, government, and civil society, and from around the globe, had made concerted efforts to develop standards, policies, and governance mechanisms to ensure the ethical, responsible, and equitable production and use of AI systems.However, as we then show, despite these ostensibly supportive activities and background conditions, several primary drivers of future shock converged to produce an international AI policy and governance crisis in the wake of the dawning of the GenAI era. Such a crisis, we argue, was marked by the disconnect between the strengthening thrust of public concerns about the hazards posed by the hasty industrial scaling of GenAI and the absence of effectual regulatory mechanisms and needed policy interventions to address such hazards. In painting a broad-stroked picture of this crisis, we underscore two sets of contributing factors. First, there have been factors that have demonstrated the absence of various vital aspects of AI policy and governance capability and execution-and thus the absence of key preconditions for readiness and resilience in managing technological transformation. These include prevalent enforcement gaps in existing digital-and data-related laws (e.g., intellectual property and data protection statutes), a lack of regulatory AI capacity, democratic deficits in the production of standards for trustworthy AI, and widespread evasionary tactics of ethic washing and state-enabled deregulation.Second, there have been factors that have significantly contributed to the presence of a new scale and order of systemic-, societal-, and biospheric-level risks and harms. Chief among these were the closely connected dynamics of unprecedented scaling and centralization that emerged as both drivers and by-products of the GenAI revolution. We focus, in particular, on model scaling and industrial scaling. Whereas the scaling of data, model size, and compute were linked to the emergence of serious model intrinsic risks deriving from the unfathomability of training data, model opacity and complexity, emergent model capabilities, and exponentially expanding compute costs, the rapid industrialization of FMs and GenAI systems meant the onset of a new scale of systemic risks that spanned the social, political, economic, cultural, and natural ecosystems in which these systems were embedded. The brute-force commercialization of GenAI ushered in a new age of widespread exposure in which increasing numbers of impacted people and communities at large were made susceptible to the risks and harms issuing from model scaling and to new possibilities for misuse, abuse, and cascading system-level effects.Alongside these aspects of model scaling and industrial scaling, patterns of economic and geopolitical centralization only further intensified conditions of future shock. The steering and momentum of these scaling dynamics lay largely in the hands of a few large tech corporations, which essentially controlled the data, compute, and skills and knowledge infrastructures r...

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Towards Explainability for AI Fairness

Cited by 27 publications

References 29 publications

A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes

Future Shock: Generative AI and the International AI Policy and Governance Crisis

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