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ردیف | عنوان | نوع |
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91 |
Build confidence and acceptance of AI-based decision support systems - Explainable and liable AI
اعتماد به نفس و پذیرش مبتنی بر هوش مصنوعی ایجاد کنید سیستم های پشتیبانی تصمیم - هوش مصنوعی قابل توضیح و مسئولیت پذیر-2020 Artificial Intelligence has known an incredible
development since 2012. It was due to the impressive
improvement of sensors, data quality and quantity, storage and
computing capacity, etc. The promises AI offered led many
scientific domains to implement AI-based decision support tool.
However, despite numerous amazing results, very serious
failures have raised Human mistrust, fear and scorn against AI.
In Industries, staff members cannot afford to use tools that
might fail them. This is especially true for Transportation
operators where security and safety are at risk. Then, the
question that arises is how to build Human confidence and
acceptance of AI-based decision support system. In this paper,
we combine different points of view to propose a structured
overview of Transparency, Explicability and Interpretability,
with new definitions arising as a consequence. Then we discuss
the need for understandable information from the AI system, to
legitimate or refute the tool’s proposal. To conclude we offer
ethical reflexions and ideas to develop confidence in AI. Keywords: explainable AI | liable AI | decision support system | confidence | technology |
مقاله انگلیسی |
92 |
Framework for Sustainable Risk Management in the Manufacturing Sector
چارچوبی برای مدیریت پایدار ریسک در بخش تولید-2020 Risk management is a huge challenge for business managers especially in the manufacturing engineering sector, and if not proactively
controlled can lead to under performance and sometimes cessation of activities for some companies. It is common knowledge that poorly
managed risks can have an adverse effect on performance while proactive and systematic control of key risk variables in a business
environment could generate successful outcomes. The work carried out here has developed a framework for risk management affordable and
suitable for use especially by small and medium size enterprises in the manufacturing sector. Using a combination of Bayesian Belief Network
(BBN) and Analytical Hierarchical Process (AHP) search algorithms, it was possible to search and identify key risk indicators that could
undermine business performance (measured in terms of cost, time, quality and safety) from a system database, and thereby manage (monitor,
identify, analyse, reduce, accept or reject their impact) them. The conclusion drawn from the study is that risk management for a manufacturing
process can be successfully achieved if risk factors which have a negative impact on project cost, quality of delivery, lead cycle and takt time
and health and safety of workers can be identified using BBN and minimised using the framework developed in this study.
Keywords: Risk Management | Manufacturing | Framework | Bayesian Belief Network | SMES | Software | Performance |
مقاله انگلیسی |
93 |
Message framing to reduce stigma and increase support for policies to improve the wellbeing of people with prior drug convictions
قالب بندی پیام برای کاهش بیماری روانی و افزایش حمایت از سیاست های بهبود رفاه افراد با محکومیت قبلی دارویی-2020 Background: Individuals with drug convictions are at heightened risk of poor health, due in part to punitive
public policies. This study tests the effects of message frames on: (1) public stigma towards individuals with
felony drug convictions and (2) support for four policies in the United States (U.S.) affecting social determinants
of health: mandatory minimum sentencing laws, ‘ban-the-box’ employment laws, and restrictions to supplemental
nutrition and public housing programs.
Methods: A randomized experiment (n = 3,758) was conducted in April 2018 using a nationally representative
online survey panel in the U.S. Participants were randomized to a no-exposure arm or one of nine exposure arms
combining: (1) a description of the consequences of incarceration and community reentry framed in one of three
ways: a public safety issue, a social justice issue or having an impact on the children of incarcerated individuals,
(2) a narrative description of an individual released from prison, and (3) a picture depicting the race of the
narrative subject. Logistic regression was used to assess effects of the frames.
Results: Social justice and the impact on children framing lowered social distance measures and increased
support for ban-the-box laws.
Conclusion: These findings can inform the development of communication strategies to reduce stigma and advocacy
efforts to support the elimination of punitive polices towards individuals with drug convictions. Keywords: Stigma | Messaging | Policy | Criminal justice |
مقاله انگلیسی |
94 |
Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study
رانندگان ، موانع و ملاحظات اجتماعی برای پذیرش هوش مصنوعی در تجارت و مدیریت: یک مطالعه عالی-2020 The number of academic papers in the area of Artificial Intelligence (AI) and its applications across business and
management domains has risen significantly in the last decade, and that rise has been followed by an increase in
the number of systematic literature reviews.
The aim of this study is to provide an overview of existing systematic reviews in this growing area of research
and to synthesise the findings related to drivers, barriers and social implications of the AI adoption in business
and management.
The methodology used for this tertiary study is based on Kitchenham and Charter’s guidelines [14], resulting
in a selection of 30 reviews published between 2005 and 2019 which are reporting results of 2021 primary
studies.
These reviews cover the AI adoption across various business sectors (healthcare, information technology,
energy, agriculture, apparel industry, engineering, smart cities, tourism and transport), management and business
functions (HR, customer services, supply chain, health and safety, project management, decision-support,
systems management and technology adoption).
While the drivers for the AI adoption in these areas are mainly economic, the barriers are related to the
technical aspects (e.g. availability of data, reusability of models) as well as the social considerations such as,
increased dependence on non-humans, job security, lack of knowledge, safety, trust and lack of multiple
stakeholders’perspectives.
Very few reviews outside of the healthcare management domain consider human, organisational and wider
societal factors of the AI adoption.
In addition to increased focus on social implications of AI, the reviews are recommending more rigorous
evaluation, increased use of hybrid solutions (AI and non-AI) and multidisciplinary approach to AI design and
evaluation.
Furthermore, this study found that there is a lack of systematic reviews in some of the early AI adoption sectors
such as financial industry and retail. Keywords: Artificial intelligence | Business | Machine learning | Management | Systematic literature review | Tertiary study |
مقاله انگلیسی |
95 |
A narrative review of international legislation regulating fitness to stand trial and criminal responsibility: Is there a perfect system?
مروری بر قوانین بینالمللی که تناسب محاکمه و مسئولیت کیفری را تنظیم میکند: آیا سیستم کاملی وجود دارد؟-2020 Mentally ill offenders have a right to a fair trial and adequate treatment in terms of the law. The diversion of mentally ill offenders between the criminal justice and the mental health care systems is linked to the forensic psychiatric assessment and based on two psycho-legal constructs, fitness to stand trial (FTST) and criminal re- sponsibility (CR). Forensic psychiatric assessment is therefore an important element in criminal law and plays a major role in the court’s decisions regarding the sentence, detention, placement, or treatment of mentally ill offenders. The legislation aims to ensure balancing the rights of mentally ill offenders to psychiatric care and society’s safety. A narrative overview of the literature summarizing the findings on legislation regulating FTST and CR assessment and their practice in different areas of the world was conducted. It offers insight into the advantages and disadvantages of the various approaches and examines the way court proceedings function in these different geographical and psychosocial-legal contexts. This may have policy implications for individual systems and allow countries to consider feasible mechanisms to refine the relevant legislation to improve their practices in forensic psychiatric assessments. Worldwide, relevant legislation is considered as essential in pro- tecting mentally ill offender’s right; it has been established for many years regarding specifying the procedures and responsibilities for the mental health care and criminal justice systems. Despite similarities in the principles of the psycho-legal constructs in different countries, there are differences in the way legislations are imple- mented, often depending on the available resources. Keywords: Legislation for mentally ill offenders | Fitness to stand trial | Criminal responsibility | Insanity defence | Competency to stand trial |
مقاله انگلیسی |
96 |
Applying artificial intelligence to explore sexual cyberbullying behaviour
استفاده از هوش مصنوعی برای کشف رفتار مزاحمت اینترنتی-2020 Sexual cyberbullying is becoming a serious problem in todays society. In the workplace, this issue is more complex because of the power imbalance between potential perpetrators and victims. Preventing sexual cyber- bullying in organizations is very important for a safety and respectful workplace. Occupational Safety and Health (OSH) standards establish certain policies to be considered to create an organizational culture based on zero tolerance to sexual cyberbullying. The research aims to broaden knowledge about personality and sexual cyberbullying. Therefore, this paper proposes a crucial tool to explore potential sexual cyberbullying behavior. This study analyzed how personality traits, particularly those related to the Dark Triad (psychopathy, Machia-vellianism and narcissism), might influence this behavior. Participants (N ¼ 374) were Spanish young adults, using the convenience sampling to recruit them. The methodology focused on the use of structural equation modelling and ensemble classification tree. First, we tested the proposed hypotheses with structural equation method based on covariance using the Lavaan R-package. Second, for the ensemble of classification trees, we applied the package random Forest and Adabag (bagging and boosting) in R. Results proposed high levels of psychopathy and Machiavellianism are more likely to be related to sexual cyberbullying behaviors. Organizations could use the tool proposed in this research to develop internal policies and procedures for detection and deterrence of potential cyberbullying behaviors. By raising awareness about cyberbullying behaviour including its conceptualization and measurement in training courses, organizations might build an organizational culture based on a respectful workplace without sexual cyberbullying behaviours. Keywords: Cyberbullying | Dark triad | Machiavellianism | Narcissism | Psychopathy | Structural equation modelling | Ensemble classification tree | Artificial intelligence | Machine learning | Business | Human resource management |
مقاله انگلیسی |
97 |
“Bed Bugs and Beyond”: An ethnographic analysis of North Americas first women-only supervised drug consumption site
"اشکالات بستر و فراتر از آن": تجزیه و تحلیل مردم نگاری اولین سایت مصرف مواد مخدر تحت نظارت فقط در زنان در آمریکای شمالی-2020 Background: Attention to how women are differentially impacted within harm reduction environments is salient
amidst North Americas overdose crisis. Harm reduction interventions are typically ‘gender-neutral’, thus failing
to address the systemic and everyday racialized and gendered discrimination, stigma, and violence extending
into service settings and limiting some womens access. Such dynamics highlight the significance of North
Americas first low-threshold supervised consumption site exclusively for women (transgender and non-binary
inclusive), SisterSpace, in Vancouver, Canada. This study explores womens lived experiences of this unique
harm reduction intervention.
Methods: Ethnographic research was conducted from May 2017 to June 2018 to explore womens experiences
with SisterSpace in Vancouvers Downtown Eastside, an epicenter of Canadas overdose crisis. Data include more
than 100 hours of ethnographic fieldwork, including unstructured conversations with structurally vulnerable
women who use illegal drugs, and in-depth interviews with 45 women recruited from this site. Data were
analyzed in NVivo by drawing on deductive and inductive approaches.
Findings: The setting (non-institutional), operational policies (no men; inclusive), and environment (diversity of
structurally vulnerable women who use illegal drugs), constituted a space affording participants a temporary reprieve
from some forms of stigma and discrimination, gendered and social violence and drug-related harms, including
overdose. SisterSpace fostered a sense of safety and subjective autonomy (though structurally constrained) among those
often defined as ‘deviant’ and ‘victims’, enabling knowledge-sharing of experiences through a gendered lens.
Conclusion: SisterSpace demonstrates the value and effectiveness of initiatives that engage with socio-structural
factors beyond the often narrow focus of overdose prevention and that account for the complex social relations
that constitute such initiatives. In the context of structural inequities, criminalization, and an overdose crisis,
SisterSpace represents an innovative approach to harm reduction that accounts for situations of gender inequality
not being met by mixed-gender services, with relevance to other settings. Keywords: women | drugs | violence | harm reduction | overdose | supervised consumption sites | Canada |
مقاله انگلیسی |
98 |
AI and Reliability Trends in Safety-Critical Autonomous Systems on Ground and Air
روند هوش مصنوعی و قابلیت اطمینان در سیستمهای خودمختار ایمنی در زمین و هوا-2020 Safety-critical autonomous systems are
becoming more powerful and more integrated to enable
higher-level functionality. Modern multi-core SOCs are
often the computing backbone in such systems for which
safety and associated certification tasks are one of the key
challenges, which can become more costly and difficult to
achieve. Hence, modeling and assessment of these systems
can be a formidable task. In addition, Artificial Intelligence
(AI) is already being deployed in safety critical autonomous
systems and Machine Learning (ML) enables the
achievement of tasks in a cost-effective way.
Compliance to Soft Error Rate (SER) requirements is an
important element to be successful in these markets. When
considering SER performance for functional safety, we need
to focus on accurately modeling vulnerability factors for
transient analysis based on AI and Deep Learning
workloads. We also need to consider the reliability
implications due to long mission times leading to high
utilization factors for autonomous transport. The reliability
risks due to these new use cases also need to be
comprehended for modeling and mitigation and would
directly impact the safety analysis for these systems. Finally,
the need for telemetry for reliability, including capabilities
for anomaly detection and prognostics techniques to
minimize field failures is of paramount importance. Index Terms : SER | safety | AI | ML. reliability |
مقاله انگلیسی |
99 |
Toward a Safer Battery Management System: A Critical Review on Diagnosis and Prognosis of Battery Short Circuit
به سمت یک سیستم مدیریت باتری ایمن تر: یک بررسی مهم در تشخیص و پیش بینی اتصال کوتاه باتری-2020 Lithium-ion batteries are commonly used as sources of power for electric vehicles (EVs). Battery safety is a major concern, due to a large number of accidents, for which short circuit has been considered as one of the main causes. Therefore, diagnosing and prognosticating short circuit are of great significance to improve EV safety. This work reviews the current state of the art about the diagnosis and prognosis of short circuit, covering the method and the key indicators. The findings provide important insights regarding how to improve the battery safety. |
مقاله انگلیسی |
100 |
Effectiveness of implementing the criminal administrative punishment law of drunk driving in China: An interrupted time series analysis, 2004-2017
اثربخشی اجرای قانون مجازات اداری رانندگی در مستی در چین: تجزیه و تحلیل سری زمانی قطع شده ، 2004-2017-2020 In 2011, a more severe drunk driving law was implemented in China, which criminalized driving under the
influence of alcohol for the first time and increased penalties for drunk driving. The present study aimed to assess
effectiveness of the drunk driving law in China in reducing traffic crashes, injuries, and mortality. Data used in
this study was obtained from the Traffic Management Bureau of the Ministry of Public Security of the People’s
Republic of China. An interrupted time series analysis was conducted to analyze annual data from 2004 to 2017,
including the number of road traffic crashes, deaths, and injuries caused by drunk driving in China. The average
annual incidences of crashes, mortality, and injuries have decreased after the promulgation of drunk driving law
in 2011. In the post-intervention period, the increased slope for crashes, mortality and injury rates were, respectively,
-0.140 to -0.006, -0.052 to -0.005 and -0.150 to -0.008, indicating a weaker downward trend of
dependent variables. The more stringent drunk driving law is not as effective as expected. Drunk driving is still a
severe traffic safety problem to be addressed in China. Both legislation and other prevention programs should be
adopted to reduce road traffic injuries caused by drunk driving in China. Keywords: Drunk driving | Interrupted time series analysis | Road traffic law | Injury | Evaluation | China |
مقاله انگلیسی |