Thanks to the great progress of machine learning in the last years, several Artificial Intelligence (AI) techniques have been increasingly moving from the controlled research laboratory settings to our everyday life. AI is clearly supportive in many decision-making scenarios, but when it comes to sensitive areas such as health care, hiring policies, education, banking or justice, with major impact on individuals and society, it becomes crucial to establish guidelines on how to design, develop, deploy and monitor this technology. Indeed the decision rules elaborated by machine learning models are data-driven and there are multiple ways in which discriminatory biases can seep into data. Algorithms trained on those data incur the risk of amplifying prejudices and societal stereotypes by over associating protected attributes such as gender, ethnicity or disabilities with the prediction task. Starting from the extensive experience of the National Metrology Institute on measurement standards and certification roadmaps, and of Politecnico di Torino on machine learning as well as methods for domain bias evaluation and mastering, we propose a first joint effort to define the operational steps needed for AI fairness certification. Specifically we will overview the criteria that should be met by an AI system before coming into official service and the conformity assessment procedures useful to monitor its functioning for fair decisions.
City, University of London（伦敦城市大学）；Fujitsu Laboratories of Europe（富士通欧洲实验室）；Intesa Sanpaolo S.p.A.（意大利联合圣保罗银行）
With Artificial intelligence (AI) to aid or automate decision-making advancing rapidly, a particular concern is its fairness. In order to create reliable, safe and trustworthy systems through human-centred artificial intelligence (HCAI) design, recent efforts have produced user interfaces (UIs) for AI experts to investigate the fairness of AI models. In this work, we provide a design space exploration that supports not only data scientists but also domain experts to investigate AI fairness. Using loan applications as an example, we held a series of workshops with loan officers and data scienti…
EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders
School of Computing Science, University of Glasgow（格拉斯哥大学计算科学学院）；FUJITSU LIMITED（富士通有限公司）；Graduate School of Arts and Sciences, The University of Tokyo（东京大学文理研究生院）；Artificial Intelligence Laboratory, FUJITSU LIMITED（富士通有限公司人工智能实验室）
Closing the AI Trust Gap: The Case for Independent Certification for Trustworthy AI
University College London（伦敦大学学院）；AI Ethics Lab（人工智能伦理实验室）；University of Cambridge（剑桥大学）；Columbia University（哥伦比亚大学）；MATS；Tech with Intention（技术与意图）；University of Southern California（南加州大学）；Kyushu University（九州大学）；University of Chile（智利大学）；BehSci Meets AI（行为科学与人工智能）；Digital Trust Council（数字信任委员会）
Towards a Framework for Supporting the Ethical and Regulatory Certification of AI Systems
St. Pölten University of Applied Sciences（施普伦特应用科学大学）；Digital for Planet - D4P（数字为行星 - D4P）；University of Luxembourg（卢森堡大学）；Netcompany-Intrasoft S.A.（Netcompany-Intrasoft公司）
We consider how fair treatment in society for people with disabilities might be impacted by the rise in the use of artificial intelligence, and especially machine learning methods. We argue that fairness for people with disabilities is different to fairness for other protected attributes such as age, gender or race. One major difference is the extreme diversity of ways disabilities manifest, and people adapt. Secondly, disability information is highly sensitive and not always shared, precisely because of the potential for discrimination. Given these differences, we explore definitions of fairn…
Telecommunication Engineering Centre（电信工程中心）；Ministry of Communications（通信部）；Adobe Inc.（奥多比公司）；Manipal Institute of Technology（马尼帕尔理工学院）
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. In this paper, we propose a novel Fairness Score to measure the fairness of a data-driven AI system and a Standard Operating Procedure (SOP) for issuing Fairness Certification for such systems. Fairness Score and audit process standardization will ensure quality, reduce ambiguity, enable …
Algorithmic Fairness in AI Surrogates for End-of-Life Decision-Making
The Department of Computing & Software Systems University of Washington Bothell Harborview Medical Center, UW Medicine（计算与软件系统系华盛顿大学Bothell分校哈佛湾医疗中心，华盛顿大学医学部）
针对临终决策 AI 替代者，本文将主要公平概念映射到现实临终场景并跨道德传统考察，提出公平应包含道德代表性及对患者价值观、关系与世界观的忠实。
"I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment
School of Computing Science University of Glasgow Glasgow United Kingdom（计算机科学学院格拉斯哥大学格拉斯哥英国）；Fujitsu Research Fujitsu Limited Kawasaki Japan（富士通研究富士通有限公司川崎日本）；University of Glasgow（格拉斯哥大学）；Fujitsu Limited（富士通有限公司）
Recommender systems are facing scrutiny because of their growing impact on the opportunities we have access to. Current audits for fairness are limited to coarse-grained parity assessments at the level of sensitive groups. We propose to audit for envy-freeness, a more granular criterion aligned with individual preferences: every user should prefer their recommendations to those of other users. Since auditing for envy requires to estimate the preferences of users beyond their existing recommendations, we cast the audit as a new pure exploration problem in multi-armed bandits. We propose a sampl…
Fairness Amidst Non-IID Graph Data: A Literature Review
Florida International University（佛罗里达国际大学）；Fudan University（复旦大学）；UNSW Sydney（新南威尔士大学悉尼分校）；CSIRO Data61（澳大利亚联邦科学与工业研究组织Data61）；National Institutes of Health（美国国立卫生研究院）
DeepMind；Centre for Artificial Intelligence, University College London（伦敦大学学院人工智能中心）
Advances in algorithmic fairness have largely omitted sexual orientation and gender identity. We explore queer concerns in privacy, censorship, language, online safety, health, and employment to study the positive and negative effects of artificial intelligence on queer communities. These issues underscore the need for new directions in fairness research that take into account a multiplicity of considerations, from privacy preservation, context sensitivity and process fairness, to an awareness of sociotechnical impact and the increasingly important role of inclusive and participatory research …
Explanations as Bias Detectors: A Critical Study of Local Post-hoc XAI Methods for Fairness Exploration
University of Ioannina（伊奥安纳大学）；Archimedes / Athena RC Greece（阿基米德/亚特兰蒂斯研究所以及希腊）；Freie Universität Berlin（柏林自由大学）；Universität der Bundeswehr München（联邦国防军大学慕尼黑）
Fujitsu Limited（富士通有限公司）；University of Glasgow（格拉斯哥大学）；City, University of London（伦敦城市大学）；Fujitsu Research of Europe Ltd.（富士通欧洲研究有限公司）
Ensuring fairness in artificial intelligence (AI) is important to counteract bias and discrimination in far-reaching applications. Recent work has started to investigate how humans judge fairness and how to support machine learning (ML) experts in making their AI models fairer. Drawing inspiration from an Explainable AI (XAI) approach called \emph{explanatory debugging} used in interactive machine learning, our work explores designing interpretable and interactive human-in-the-loop interfaces that allow ordinary end-users without any technical or domain background to identify potential fairnes…
Algorithm fairness in the application of artificial intelligence (AI) is essential for a better society. As the foundational axiom of social mechanisms, fairness consists of multiple facets. Although the machine learning (ML) community has focused on intersectionality as a matter of statistical parity, especially in discrimination issues, an emerging body of literature addresses another facet -- monotonicity. Based on domain expertise, monotonicity plays a vital role in numerous fairness-related areas, where violations could misguide human decisions and lead to disastrous consequences. In this…
Towards clinical AI fairness: A translational perspective
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；University of Antwerp（安特卫普大学）；University of Florida（佛罗里达大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）；SingHealth AI Office（新加坡保健服务集团AI办公室）；Singapore Health Services（新加坡保健服务集团）；Singapore General Hospital（新加坡中央医院）；Weill Cornell Medicine（威尔康奈尔医学院）；Massachusetts Institute of Technology（麻省理工学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard T.H. Chan School of Public Health（哈佛陈曾熙公共卫生学院）；National University of Singapore（新加坡国立大学）
本文从转化医学视角分析 AI 公平性在医疗场景中技术与临床认知的错位及落地障碍，主张多学科协作并提出应对临床公平性问题的解决方案。
This paper summarizes and evaluates various approaches, methods, and techniques for pursuing fairness in artificial intelligence (AI) systems. It examines the merits and shortcomings of these measures and proposes practical guidelines for defining, measuring, and preventing bias in AI. In particular, it cautions against some of the simplistic, yet common, methods for evaluating bias in AI systems, and offers more sophisticated and effective alternatives. The paper also addresses widespread controversies and confusions in the field by providing a common language among different stakeholders of …
University of Bristol（布里斯托尔大学）；RMIT University（皇家墨尔本理工大学）
Today, artificial intelligence systems driven by machine learning algorithms can be in a position to take important, and sometimes legally binding, decisions about our everyday lives. In many cases, however, these systems and their actions are neither regulated nor certified. To help counter the potential harm that such algorithms can cause we developed an open source toolbox that can analyse selected fairness, accountability and transparency aspects of the machine learning process: data (and their features), models and predictions, allowing to automatically and objectively report them to rele…
Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction
University of Gondar（贡德尔大学）；College of Engineering and Applied Science, University of Colorado, Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校工程与应用科学学院）；Can Tho University（芹苴大学）；Ewha Womans University（梨花女子大学）；University of Colorado, Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校）
An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence (AI) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop AI algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision-making may be inherently prone to unfairness, even when there is no intention for it. This paper presents an overview o…
Organizations are rapidly deploying artificial intelligence (AI) systems to manage their workers. However, AI has been found at times to be unfair to workers. Unfairness toward workers has been associated with decreased worker effort and increased worker turnover. To avoid such problems, AI systems must be designed to support fairness and redress instances of unfairness. Despite the attention related to AI unfairness, there has not been a theoretical and systematic approach to developing a design agenda. This paper addresses the issue in three ways. First, we introduce the organizational justi…
University of Amsterdam（阿姆斯特丹大学）；AIRLab, University of Amsterdam（阿姆斯特丹大学 AIRLab）
In this work, we explain the setup for a technical, graduate-level course on Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence (FACT-AI) at the University of Amsterdam, which teaches FACT-AI concepts through the lens of reproducibility. The focal point of the course is a group project based on reproducing existing FACT-AI algorithms from top AI conferences and writing a corresponding report. In the first iteration of the course, we created an open source repository with the code implementations from the group projects. In the second iteration, we encouraged…
In recent years, there has been a stimulating discussion on how artificial intelligence (AI) can support the science and engineering of intelligent educational applications. Many studies in the field are proposing actionable data mining pipelines and machine-learning models driven by learning-related data. The potential of these pipelines and models to amplify unfairness for certain categories of students is however receiving increasing attention. If AI applications are to have a positive impact on education, it is crucial that their design considers fairness at every step. Through anonymous s…
Humans are increasingly coming into contact with artificial intelligence and machine learning systems. Human-centered artificial intelligence is a perspective on AI and ML that algorithms must be designed with awareness that they are part of a larger system consisting of humans. We lay forth an argument that human-centered artificial intelligence can be broken down into two aspects: (1) AI systems that understand humans from a sociocultural perspective, and (2) AI systems that help humans understand them. We further argue that issues of social responsibility such as fairness, accountability, i…
"AI as a Service" (AIaaS) is a rapidly growing market, offering various plug-and-play AI services and tools. AIaaS enables its customers (users) - who may lack the expertise, data, and/or resources to develop their own systems - to easily build and integrate AI capabilities into their applications. Yet, it is known that AI systems can encapsulate biases and inequalities that can have societal impact. This paper argues that the context-sensitive nature of fairness is often incompatible with AIaaS' 'one-size-fits-all' approach, leading to issues and tensions. Specifically, we review and systemat…
ELLIS Unit Alicante Foundation（ELLIS 阿利坎特分部基金会）；Oslo Metropolitan University（奥斯陆都会大学）；Oslo National Academy of the Arts（奥斯陆国立艺术学院）；Politecnico di Torino（都灵理工大学）；EURECOM（欧洲通信学院）
While Artificial Intelligence (AI) technologies are being progressively developed, artists and researchers are investigating their role in artistic practices. In this work, we present an AI-based Brain-Computer Interface (BCI) in which humans and machines interact to express feelings artistically. This system and its production of images give opportunities to reflect on the complexities and range of human emotions and their expressions. In this discussion, we seek to understand the dynamics of this interaction to reach better co-existence in fairness, inclusion, and aesthetics.
The Robust Artificial Intelligence System Assurance (RAISA) workshop will focus on research, development and application of robust artificial intelligence (AI) and machine learning (ML) systems. Rather than studying robustness with respect to particular ML algorithms, our approach will be to explore robustness assurance at the system architecture level, during both development and deployment, and within the human-machine teaming context. While the research community is converging on robust solutions for individual AI models in specific scenarios, the problem of evaluating and assuring the robu…
University of Maryland（马里兰大学）；Harvard University（哈佛大学）
Rideshare and ride-pooling platforms use artificial intelligence-based matching algorithms to pair riders and drivers. However, these platforms can induce inequality either through an unequal income distribution or disparate treatment of riders. We investigate two methods to reduce forms of inequality in ride-pooling platforms: (1) incorporating fairness constraints into the objective function and (2) redistributing income to drivers to reduce income fluctuation and inequality. To evaluate our solutions, we use the New York City taxi data set. For the first method, we find that optimizing for …
Autonomous Systems Dependability in the era of AI: Design Challenges in Safety, Security, Reliability and Certification
Colorado State University, US（科罗拉多州立大学）；The University of North Carolina at Chapel Hill, US（北卡罗来纳大学教堂山分校）；The TUM Institute for Advanced Study, Germany（慕尼黑工业大学高级研究 institute）；Infineon Technologies, Germany（英飞凌科技）
Fair representation learning transforms user data into a representation that ensures fairness and utility regardless of the downstream application. However, learning individually fair representations, i.e., guaranteeing that similar individuals are treated similarly, remains challenging in high-dimensional settings such as computer vision. In this work, we introduce LASSI, the first representation learning method for certifying individual fairness of high-dimensional data. Our key insight is to leverage recent advances in generative modeling to capture the set of similar individuals in the gen…
Recent works in artificial intelligence fairness attempt to mitigate discrimination by proposing constrained optimization programs that achieve parity for some fairness statistic. Most assume availability of the class label, which is impractical in many real-world applications such as precision medicine, actuarial analysis and recidivism prediction. Here we consider fairness in longitudinal right-censored environments, where the time to event might be unknown, resulting in censorship of the class label and inapplicability of existing fairness studies. We devise applicable fairness measures, pr…
