AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings
حکمرانی هوش مصنوعی در بخش عمومی: سه داستان از مرزهای تصمیم گیری خودکار در تنظیمات دموکراتیک-2020
The rush to understand new socio-economic contexts created by the wide adoption of AI is justified by its far-ranging consequences, spanning almost every walk of life. Yet, the public sector’s predicament is a tragic double bind: its obligations to protect citizens from potential algorithmic harms are at odds with the temptation to increase its own efficiency - or in other words - to govern algorithms, while governing by algorithms. Whether such dual role is even possible, has been a matter of debate, the challenge stemming from algorithms’ intrinsic properties, that make them distinct from other digital solutions, long embraced by the governments, create externalities that rule-based programming lacks. As the pressures to deploy automated decision making systems in the public sector become prevalent, this paper aims to examine how the use of AI in the public sector in relation to existing data governance regimes and national regulatory practices can be intensifying existing power asymmetries. To this end, investigating the legal and policy instruments associated with the use of AI for strenghtening the immigration process control system in Canada; “optimising” the employment services” in Poland, and personalising the digital service experience in Finland, the paper advocates for the need of a common framework to evaluate the potential impact of the use of AI in the public sector. In this regard, it discusses the specific effects of automated decision support systems on public services and the growing expectations for governments to play a more prevalent role in the digital society and to ensure that the potential of technology is harnessed, while negative effects are controlled and possibly avoided. This is of particular importance in light of the current COVID-19 emergency crisis where AI and the underpinning regulatory framework of data ecosystems, have become crucial policy issues as more and more innovations are based on large scale data collections from digital devices, and the real-time accessibility of information and services, contact and relationships between institutions and citizens could strengthen – or undermine - trust in governance systems and democracy.
Keywords: Artificial intelligence | Public sector innovation | Automated decision making | Algorithmic accountability
An explainable AI decision-support-system to automate loan underwriting
یک سیستم پشتیبانی تصمیم گیری هوش مصنوعی قابل توضیح برای اتوماسیون پذیره نویسی وام-2020
Widespread adoption of automated decision making by artificial intelligence (AI) is witnessed due to specular advances in computation power and improvements in optimization algorithms especially in ma- chine learning (ML). Complex ML models provide good prediction accuracy; however, the opacity of ML models does not provide sufficient assurance for their adoption in the automation of lending decisions. This paper presents an explainable AI decision-support-system to automate the loan underwriting pro- cess by belief-rule-base (BRB). This system can accommodate human knowledge and can also learn from historical data by supervised learning. The hierarchical structure of BRB can accommodates factual and heuristic rules. The system can explain the chain of events leading to a decision for a loan application by the importance of an activated rule and the contribution of antecedent attributes in the rule. A business case study on automation of mortgage underwriting is demonstrated to show that the BRB system can provide a good trade-offbetween accuracy and explainability. The textual explanation produced by the activation of rules could be used as a reason for denial of a loan. The decision-making process for an application can be comprehended by the significance of rules in providing the decision and contribution of its antecedent attributes.
Keywords: Explainable artificial intelligence | Interpretable machine learning | Loan underwriting | Evidential reasoning | Belief-rule-base | Automated decision making
Automated vehicle’s behavior decision making using deep reinforcement learning and high-fidelity simulation environment
تصمیم گیری خودکار وسیله نقلیه با استفاده از یادگیری تقویتی عمیق و محیط شبیه سازی با وفاداری بالا-2019
Automated vehicles (AVs) are deemed to be the key element for the intelligent transportation system in the future. Many studies have been made to improve AVs’ ability of environment recognition and vehicle control, while the attention paid to decision making is not enough and the existing decision algorithms are very preliminary. Therefore, a framework of the decisionmaking training and learning is put forward in this paper. It consists of two parts: the deep reinforcement learning (DRL) training program and the high-fidelity virtual simulation environment. Then the basic microscopic behavior, car-following (CF), is trained within this framework. In addition, theoretical analysis and experiments were conducted to evaluate the proposed reward functions for accelerating training using DRL. The results show that on the premise of driving comfort, the efficiency of the trained AV increases 7.9% and 3.8% respectively compared to the classical adaptive cruise control models, intelligent driver model and constant-time headway policy. Moreover, on a more complex three-lane section, we trained an integrated model combining both CF and lane-changing behavior, with the average speed further growing 2.4%. It indicates that our framework is effective for AV’s decision-making learning.
Keywords: Automated vehicle | Decision making | Deep reinforcement learning | Reward function
سیستم های اطلاعاتی مدیریت و تصمیم گیری در مورد کسب و کار: بررسی ، تجزیه و تحلیل و توصیه ها
سال انتشار: 2011 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 12
نقش سیستم اطلاعاتی مدیریت در پرتو قابلیت هایش در تصمیم گیری، توصیف و تجزیه و تحلیل شده است. فرآیند تصمیم گیری و تاثیر آن بر مدیریت در سطوح بالا در یک سازمان تجاری بوسیله تاکید بر تصمیم گیری خودکار بیان می شود. محدودیت ها و چالش های سیستم اطلاعاتی مدیریت مورد بحث قرار گرفته است و مجموعه از شش توصیه به منظور افزایش اثربخشی سیستم اطلاعاتی مدیریت در فرآیند تصمیم گیری ارائه شده است.
کلمات کلیدی: سیستم های اطلاعاتی | سیستم پردازش مبادلات | TPS | سیستم های اطلاعاتی مدیریت | MIS | سیستم های خبره
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