scholarly journals Taking Principles Seriously: A Hybrid Approach to Value Alignment in Artificial Intelligence

2021 ◽  
Vol 70 ◽  
pp. 871-890
Author(s):  
Tae Wan Kim ◽  
John Hooker ◽  
Thomas Donaldson
Keyword(s):  
Artificial Intelligence ◽  
Action Plan ◽  
Ethical Reasoning ◽  
Hybrid Approach ◽  
Naturalistic Fallacy ◽  
Ethical Principles ◽  
Rule Base ◽  
Empirical Observation ◽  
Deontological Ethics

An important step in the development of value alignment (VA) systems in artificial intelligence (AI) is understanding how VA can reflect valid ethical principles. We propose that designers of VA systems incorporate ethics by utilizing a hybrid approach in which both ethical reasoning and empirical observation play a role. This, we argue, avoids committing “naturalistic fallacy,” which is an attempt to derive “ought” from “is,” and it provides a more adequate form of ethical reasoning when the fallacy is not committed. Using quantified model logic, we precisely formulate principles derived from deontological ethics and show how they imply particular “test propositions” for any given action plan in an AI rule base. The action plan is ethical only if the test proposition is empirically true, a judgment that is made on the basis of empirical VA. This permits empirical VA to integrate seamlessly with independently justified ethical principles. This article is part of the special track on AI and Society.

scholarly journals New Hybrid Approach for Developing Automated Machine Learning Workflows: A Real Case Application in Evaluation of Marcellus Shale Gas Production

Fuels ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 286-303
Author(s):  
Vuong Van Pham ◽  
Ebrahim Fathi ◽  
Fatemeh Belyadi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Shale Gas ◽  
Field Data ◽  
Marcellus Shale ◽  
Hybrid Approach ◽  
Gas Production ◽  
Bayesian Optimization ◽  
Real Field ◽  
Engineering Problems

The success of machine learning (ML) techniques implemented in different industries heavily rely on operator expertise and domain knowledge, which is used in manually choosing an algorithm and setting up the specific algorithm parameters for a problem. Due to the manual nature of model selection and parameter tuning, it is impossible to quantify or evaluate the quality of this manual process, which in turn limits the ability to perform comparison studies between different algorithms. In this study, we propose a new hybrid approach for developing machine learning workflows to help automated algorithm selection and hyperparameter optimization. The proposed approach provides a robust, reproducible, and unbiased workflow that can be quantified and validated using different scoring metrics. We have used the most common workflows implemented in the application of artificial intelligence (AI) and ML in engineering problems including grid/random search, Bayesian search and optimization, genetic programming, and compared that with our new hybrid approach that includes the integration of Tree-based Pipeline Optimization Tool (TPOT) and Bayesian optimization. The performance of each workflow is quantified using different scoring metrics such as Pearson correlation (i.e., R2 correlation) and Mean Square Error (i.e., MSE). For this purpose, actual field data obtained from 1567 gas wells in Marcellus Shale, with 121 features from reservoir, drilling, completion, stimulation, and operation is tested using different proposed workflows. A proposed new hybrid workflow is then used to evaluate the type well used for evaluation of Marcellus shale gas production. In conclusion, our automated hybrid approach showed significant improvement in comparison to other proposed workflows using both scoring matrices. The new hybrid approach provides a practical tool that supports the automated model and hyperparameter selection, which is tested using real field data that can be implemented in solving different engineering problems using artificial intelligence and machine learning. The new hybrid model is tested in a real field and compared with conventional type wells developed by field engineers. It is found that the type well of the field is very close to P50 predictions of the field, which shows great success in the completion design of the field performed by field engineers. It also shows that the field average production could have been improved by 8% if shorter cluster spacing and higher proppant loading per cluster were used during the frac jobs.

The Frame Problem in Ethics

2020 ◽  
Vol 2 ◽  
pp. 53-73
Author(s):  
Sebastian Gałecki
Keyword(s):  
Artificial Intelligence ◽  
Moral Judgment ◽  
First Principles ◽  
Practical Reasoning ◽  
Ethical Principles ◽  
Frame Problem ◽  
Classical Philosophy ◽  
The Frame Problem ◽  
The Invisible

Although the “frame problem” in philosophy has been raised in the context of the artificial intelligence, it is only an exemplification of broader problem. It seems that contemporary ethical debates are not so much about conclusions, decisions, norms, but rather about what we might call a “frame”. Metaethics has always been the bridge between purely ethical principles (“this is good and it should be done”, “this is wrong and it should be avoided”) and broader (ontological, epistemic, anthropological etc.) assumptions. One of the most interesting meta-ethical debates concerns the “frame problem”: whether the ethical frame is objective and self-evident, or is it objective but not self-evident? In classical philosophy, this problem takes the form of a debate on the first principles: nonprovable but necessary starting points for any practical reasoning. They constitute the invisible but essential frame of every moral judgment, decision and action. The role of philosophy is not only to expose these principles, but also to understand the nature of the moral frame.

Hybrid Fluid Flow Simulation Combining Full Physics Simulation and Artificial Intelligence

2021 ◽  
Author(s):  
Mokhles Mezghani ◽  
Mustafa AlIbrahim ◽  
Majdi Baddourah
Keyword(s):  
Artificial Intelligence ◽  
Reservoir Simulation ◽  
History Matching ◽  
Hybrid Approach ◽  
Flow Simulation ◽  
Latin Hypercube Sampling ◽  
Simulation Models ◽  
Fine Scale ◽  
Simulation Results ◽  
Optimization Loop

Abstract Reservoir simulation is a key tool for predicting the dynamic behavior of the reservoir and optimizing its development. Fine scale CPU demanding simulation grids are necessary to improve the accuracy of the simulation results. We propose a hybrid modeling approach to minimize the weight of the full physics model by dynamically building and updating an artificial intelligence (AI) based model. The AI model can be used to quickly mimic the full physics (FP) model. The methodology that we propose consists of starting with running the FP model, an associated AI model is systematically updated using the newly performed FP runs. Once the mismatch between the two models is below a predefined cutoff the FP model is switch off and only the AI model is used. The FP model is switched on at the end of the exercise either to confirm the AI model decision and stop the study or to reject this decision (high mismatch between FP and AI model) and upgrade the AI model. The proposed workflow was applied to a synthetic reservoir model, where the objective is to match the average reservoir pressure. For this study, to better account for reservoir heterogeneity, fine scale simulation grid (approximately 50 million cells) is necessary to improve the accuracy of the reservoir simulation results. Reservoir simulation using FP model and 1024 CPUs requires approximately 14 hours. During this history matching exercise, six parameters have been selected to be part of the optimization loop. Therefore, a Latin Hypercube Sampling (LHS) using seven FP runs is used to initiate the hybrid approach and build the first AI model. During history matching, only the AI model is used. At the convergence of the optimization loop, a final FP model run is performed either to confirm the convergence for the FP model or to re iterate the same approach starting from the LHS around the converged solution. The following AI model will be updated using all the FP simulations done in the study. This approach allows the achievement of the history matching with very acceptable quality match, however with much less computational resources and CPU time. CPU intensive, multimillion-cell simulation models are commonly utilized in reservoir development. Completing a reservoir study in acceptable timeframe is a real challenge for such a situation. The development of new concepts/techniques is a real need to successfully complete a reservoir study. The hybrid approach that we are proposing is showing very promising results to handle such a challenge.

Social media users and cybersecurity awareness: predicting self-disclosure using a hybrid artificial intelligence approach

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Naurin Farooq Khan ◽  
Naveed Ikram ◽  
Hajra Murtaza ◽  
Muhammad Aslam Asadi
Keyword(s):  
Artificial Intelligence ◽  
Social Networking Sites ◽  
Hybrid Approach ◽  
Area Under The Curve ◽  
Protective Behavior ◽  
Self Disclosure ◽  
Content Type ◽  
Privacy Risks ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

PurposeThis study aims to investigate the cybersecurity awareness manifested as protective behavior to explain self-disclosure in social networking sites. The disclosure of information about oneself is associated with benefits as well as privacy risks. The individuals self-disclose to gain social capital and display protective behaviors to evade privacy risks by careful cost-benefit calculation of disclosing information.Design/methodology/approachThis study explores the role of cyber protection behavior in predicting self-disclosure along with demographics (age and gender) and digital divide (frequency of Internet access) variables by conducting a face-to-face survey. Data were collected from 284 participants. The model is validated by using multiple hierarchal regression along with the artificial intelligence approach.FindingsThe results revealed that cyber protection behavior significantly explains the variance in self-disclosure behavior. The complementary use of five machine learning (ML) algorithms further validated the model. The ML algorithms predicted self-disclosure with an area under the curve of 0.74 and an F1 measure of 0.70.Practical implicationsThe findings suggest that costs associated with self-disclosure can be mitigated by educating the individuals to heighten their cybersecurity awareness through cybersecurity training programs.Originality/valueThis study uses a hybrid approach to assess the influence of cyber protection behavior on self-disclosure using expectant valence theory (EVT).

scholarly journals A Hybrid Approach to Call Admission Control in 5G Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Mohammed Al-Maitah ◽  
Olena O. Semenova ◽  
Andriy O. Semenov ◽  
Pavel I. Kulakov ◽  
Volodymyr Yu. Kucheruk
Keyword(s):  
Artificial Intelligence ◽  
Quality Of Service ◽  
Admission Control ◽  
Mobile Communications ◽  
Communication Systems ◽  
Call Admission Control ◽  
Hybrid Approach ◽  
5G Networks ◽  
Call Admission

Artificial intelligence is employed for solving complex scientific, technical, and practical problems. Such artificial intelligence techniques as neural networks, fuzzy systems, and genetic and evolutionary algorithms are widely used for communication systems management, optimization, and prediction. Artificial intelligence approach provides optimized results in a challenging task of call admission control, handover, routing, and traffic prediction in cellular networks. 5G mobile communications are designed as heterogeneous networks, whose important requirement is accommodating great numbers of users and the quality of service satisfaction. Call admission control plays a significant role in providing the desired quality of service. An effective call admission control algorithm is needed for optimizing the cellular network system. Many call admission control schemes have been proposed. The paper proposes a methodology for developing a genetic neurofuzzy controller for call admission in 5G networks. Performance of the proposed admission control is evaluated through computer simulation.

scholarly journals AdaptFlow: Protocol-based Medical Treatment Using Adaptive Workflows

2005 ◽  
Vol 44 (01) ◽  
pp. 80-88 ◽  
Author(s):  
R. Mueller ◽  
E. Rahm ◽  
J. Ramsch ◽  
B. Heller ◽  
M. Loeffler ◽  
...  
Keyword(s):  
Evaluation Studies ◽  
Hybrid Approach ◽  
Workflow Management ◽  
Rule Base ◽  
Consultation System ◽  
Number Of Patients ◽  
Automatic Adaptation ◽  
The Status ◽  
Trial Protocols ◽  
Workflow Adaptation

Summary Objectives: In many medical domains investigator-initiated clinical trials are used to introduce new treatments and hence act as implementations of guideline-based therapies. Trial protocols contain detailed instructions to conduct the therapy and additionally specify reactions to exceptional situations (for instance an infection or a toxicity). To increase quality in health care and raise the number of patients treated according to trial protocols, a consultation system is needed that supports the handling of the complex trial therapy processes efficiently. Our objective was to design and evaluate a consultation system that should 1) observe the status of the therapies currently being applied, 2) offer automatic recognition of exceptional situations and appropriate decision support and 3) provide an automatic adaptation of affected therapy processes to handle exceptional situations. Methods: We applied a hybrid approach that combines process support for the timely and efficient execution of the therapy processes as offered by workflow management systems with a knowledge and rule base and a mechanism for dynamic workflow adaptation to change running therapy processes if induced by changed patient condition. Results and Conclusions: This approach has been implemented in the AdaptFlow prototype. We performed several evaluation studies on the practicability of the approach and the usefulness of the system. These studies show that the AdaptFlow prototype offers adequate support for the execution of real-world investigator-initiated trial protocols and is able to handle a large number of exceptions.

scholarly journals Medical Ethics during the COVID-19 Pandemic

Acta Medica ◽  
2021 ◽  
pp. 1-10
Author(s):  
Sevim Coşkun ◽  
Nüket Örnek Büken
Keyword(s):  
Healthcare Professionals ◽  
Human Life ◽  
Action Plan ◽  
Disaster Medicine ◽  
Ethical Principles ◽  
Duty Of Care ◽  
Protective Measures ◽  
Life Threatening ◽  
Correct Application ◽  
Difficult Situations

WHO states five ethical principles for the care of patients with COVID-19: Equal moral respect, duty of care, non-abandonment, protection of the community, and confidentiality. Healthcare professionals might have to make difficult decisions such as selecting patients and withholding or withdrawing mechanical ventilation of critically ill patients. In such difficult situations, a well-prepared action plan which considers ethical principles and prioritizes both public health and the safety of healthcare professionals, can help them. In this case, the development of an effective pandemic action plan, together with a triage plan based on emergency and disaster medicine is necessary. The only parameter of selection in this plan must be the correct application of triage, which respects every human life and depends on the criteria of clinical suitability. In this context, the fundamental ethical principles and human rights must be considered when allocating resources and prioritizing patients. Additionally, all protective measures for healthcare professionals must be taken, including all necessary equipment being adequately provided. If healthcare professionals become infected or face a life-threatening risk, then their obligations will be limited. Therefore, it is necessary to realize these limitations which may arise while providing appropriate health services.

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