Recent AI algorithms are black box models whose decisions are difficult to interpret. eXplainable AI (XAI) is a class of methods that seek to address lack of AI interpretability and trust by explaining to customers their AI decisions. The common wisdom is that regulating AI by mandating fully transparent XAI leads to greater social welfare. Our paper challenges this notion through a game theoretic model of a policy-maker who maximizes social welfare, firms in a duopoly competition that maximize profits, and heterogenous consumers. The results show that XAI regulation may be redundant. In fact, mandating fully transparent XAI may make firms and consumers worse off. This reveals a tradeoff between maximizing welfare and receiving explainable AI outputs. We extend the existing literature on method and substantive fronts, and we introduce and study the notion of XAI fairness, which may be impossible to guarantee even under mandatory XAI. Finally, the regulatory and managerial implications of our results for policy-makers and businesses are discussed, respectively.
J. P. Morgan AI Research（摩根大通人工智能研究院）；J. P. Morgan（摩根大通）
Regulators have signalled an interest in adopting explainable AI(XAI) techniques to handle the diverse needs for model governance, operational servicing, and compliance in the financial services industry. In this short overview, we review the recent technical literature in XAI and argue that based on our current understanding of the field, the use of XAI techniques in practice necessitate a highly contextualized approach considering the specific needs of stakeholders for particular business applications.
Non-technical end-users are silent and invisible users of the state-of-the-art explainable artificial intelligence (XAI) technologies. Their demands and requirements for AI explainability are not incorporated into the design and evaluation of XAI techniques, which are developed to explain the rationales of AI decisions to end-users and assist their critical decisions. This makes XAI techniques ineffective or even harmful in high-stakes applications, such as healthcare, criminal justice, finance, and autonomous driving systems. To systematically understand end-users' requirements to support the…
Ca' Foscari University, Venice, Italy（威尼斯卡弗里大学）；Polytechnic University of Turin, Turin, Italy（都灵理工学院）；European Centre for Living Technology, Venice, Italy（欧洲生活技术中心）；Zhejiang Normal University, Jinhua, China（浙江师范大学）；Denmark Technical University, Lyngby, Denmark（丹麦技术大学）
Applications of Explainable artificial intelligence in Earth system science
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)（南方海洋科学与工程广东省实验室（珠海））；Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies（广东省气候变化与自然灾害研究重点实验室）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；Shanwei Meteorological Service（汕尾市气象局）；College of Computer Science and Technology, Changchun Normal University（长春师范大学计算机科学与技术学院）；ELLIS Unit Jena（ELLIS耶拿单元）
To make Explainable AI (XAI) systems trustworthy, understanding harmful effects is just as important as producing well-designed explanations. In this paper, we address an important yet unarticulated type of negative effect in XAI. We introduce explainability pitfalls(EPs), unanticipated negative downstream effects from AI explanations manifesting even when there is no intention to manipulate users. EPs are different from, yet related to, dark patterns, which are intentionally deceptive practices. We articulate the concept of EPs by demarcating it from dark patterns and highlighting the challen…
Regulating the Machine Contributor: Governance and Policy Alignment in Open Source
Faculty of Sciences of Tunis (FST), University of Tunis El Manar（突尼斯科学学院（FST），突尼斯El Manar大学）；National Institute of Applied Science and Technology (INSAT), University of Carthage（应用科学和技术国家研究所（INSAT），卡塔赫季大学）
Intesa Sanpaolo S.p.A.（意大利圣保罗银行）；Fujitsu Laboratories of Europe（富士通欧洲实验室）
Explainable Artificial Intelligence (XAI) is a set of techniques that allows the understanding of both technical and non-technical aspects of Artificial Intelligence (AI) systems. XAI is crucial to help satisfying the increasingly important demand of \emph{trustworthy} Artificial Intelligence, characterized by fundamental characteristics such as respect of human autonomy, prevention of harm, transparency, accountability, etc. Within XAI techniques, counterfactual explanations aim to provide to end users a set of features (and their corresponding values) that need to be changed in order to achi…
Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression
Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Technical University of Berlin（柏林工业大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所（BIFOLD））
The spread of disinformation on social platforms is harmful to society. This harm may manifest as a gradual degradation of public discourse; but it can also take the form of sudden dramatic events such as the 2021 insurrection on Capitol Hill. The platforms themselves are in the best position to prevent the spread of disinformation, as they have the best access to relevant data and the expertise to use it. However, mitigating disinformation is costly, not only for implementing detection algorithms or employing manual effort, but also because limiting such highly viral content impacts user enga…
Explainable machine learning (ML) enables human learning from ML, human appeal of automated model decisions, regulatory compliance, and security audits of ML models. Explainable ML (i.e. explainable artificial intelligence or XAI) has been implemented in numerous open source and commercial packages and explainable ML is also an important, mandatory, or embedded aspect of commercial predictive modeling in industries like financial services. However, like many technologies, explainable ML can be misused, particularly as a faulty safeguard for harmful black-boxes, e.g. fairwashing or scaffolding,…
University of South Carolina（南卡罗来纳大学）；Embible, Inc.（Embible公司）
AI systems have seen significant adoption in various domains. At the same time, further adoption in some domains is hindered by inability to fully trust an AI system that it will not harm a human. Besides the concerns for fairness, privacy, transparency, and explainability are key to developing trusts in AI systems. As stated in describing trustworthy AI "Trust comes through understanding. How AI-led decisions are made and what determining factors were included are crucial to understand." The subarea of explaining AI systems has come to be known as XAI. Multiple aspects of an AI system can be …
It is very important to adhere strictly to ethical and social influences when delivering most of our life to artificial intelligence systems. With industry 4.0, the internet of things, data analysis and automation have begun to be of great importance in our lives. With the Yapanese version of Industry 5.0, it has come to our attention that machine-human interaction and human intelligence are working in harmony with the cognitive computer. In this context, robots working on artificial intelligence algorithms co-ordinated with the development of technology have begun to enter our lives. But the …
