Ethics of Using Artificial Intelligence to Augment Drafting Legal Documents
In the recent year, it has been seen that there was immense progress in Artificial Intelligence (AI). The field of research is thriving which increases the important areas of research with a number of the application having core technology. In AI there is rapid progress which often increases the operational power with hardware advancements. There are many practical applications which have an AI, and it has enabled technologies to cover different fields of understanding of speech recognition, predictive analytics, process automation, biometrics, natural language processing, machine and deep learning (Ghahramani, 2015). In the past, the researchers of AI envisaged a system that is computational where human intelligence is exhibited and achieve a level of skills for decision-making and problem solving. The organisation has been running for 20 years with around 200 staff working in it. The organisation has the capabilities to provide logistics solutions for manufacturing, mining and warehousing. Therefore, the organisation has explored options to provide services that are based on AI. The main head office is in Sydney and operates in other states of Australia and Oceania region. There are high symbolic, formalised AI constraints especially board games attempt a complex and decision making environments. Many businesses have seen AI has increases the cost of employment of human and different ways were used by industries (Russell, Dewey & Tegmark, 2015). There are developments and implementation in small cities and medical sciences, in movies there are some special effects and the type of work the back-office could even manage. Many critics have been rise from the fields of ICT which uses AI for an unethical takeover over a human by the machines (Müller & Bostrom, 2016).
Artificial Intelligence (AI) consist of different fields from machine vision to expert systems. John Mccarthy has coined the term AI in the year 1956. The computer system mainly processes the Artificial Intelligence were learning, reasoning and self-correction are included. Size, speed and data diversity increase business globally. AI can recognise the data patterns more efficiently than a human for business insights (Hill, Ford & Farreras, 2015). The history of AI has been a pioneer in computer science. The AI goal is to stimulate the performance of the human for the task to make the program to be the best. The use of artificial intelligence captures the human brains that have limited domains. In the revolution of the computer the system develops intellectually, reason rationally and effectively interprets the real time environment (Scherer, 2015). The mathematician and scientist have changed there thinking about artificial intelligence. The artifact of intelligent in Greek mythology has appeared to be available after World War-II. It was possible for the complex activities to get stimulated by professional expertise. The best example of an intellectual system is the chess playing program (Hricik, Morgan & Williams, 2018). The chess engine is designed to play as the opponent can count the move in a million ways which the human beings are incapable of. The gaming, business, medicine, controlling flights, academia, weather forecasting is getting revolutionised by artificial intelligence. The technique of AI organises and efficiently use the knowledge that is perceivable, easily modifiable and useful in many situations (Wong & Bressler, 2016).
Artificial Intelligence to Win the Nobel Prize and Beyond: Creating the Engine for Scientific Discovery
It is essential that the organisation work processes are operating with a highest possible level that has a well-oiled logistics team. The world of professional has grown with digitisation, and with the addition of artificial intelligence (AI) the resources are getting maximise the time, and spending of money get reduces (Lu & Burton, 2017). The four important things to know about AI and their role it plays in logistics are discussed below:
- Common Solutions and Problems: The Artificial Intelligence has explored the businesses through the breakthrough of recent technologies. The shipper’s demand has increased as it has pushed the businesses to the logistical teams (Yaseen et al., 2015). The technology can offer the most common solutions such as the risk mitigation and redundancies for cost reduction, forecast the traditional techniques, resource management. The automation business can seamlessly update the IT systems and enhance the process of data analysis that could bolster the logistical processes (Schölkopf, 2015).
- Load cost: It is tricky to predict the price because the shipping cost varies in every season and even on a day-to-day basis. Such conditions of AI could be monitored that could choose the price at the time of delivery and headed a shipment (Chen et al., 2017). The algorithm as monitors the traffic through a series of parameters for traffic, weather and socio-economic which help the organisation to challenge the socio-economic to reach a price that is fair for both the parties to agree upon.
- Optimizing Inventory: In democratisation and information accessibility the role of AI is essential as it offers a fair price for the technology were both parties ensure a fair deal which could monitor the load capacity and inventory to avoid faltering of trucks at the t, time of delivery(Patil, 2016). The number of trucks and the inventory supplier could be managing and secure by the technology for delivery. AI has offered data analysis to know about the movement of the carriers for the freight will the level of price and service.
- Unforeseen CIA circumstances are tackled: For the logistical business, the organisation may find the series of circumstances that could affect the product expected delivery date(Yang, 2018). The logistical workflow of the organisation gets affected by the carrier bankruptcies, strikes of the employee, hurricanes and floods.
- Chatbots for Operational Procurement: Chatbots have being develop through artificial intelligence. The specialist is now implementing chatbots in to the activities of supply chain management. This help the organization to utilize the tech-aided work-tools. The chatbot with machine learning and deep AI can hold conversation that are small and have patterns to understand. Chatbots can be used in value chain and are applicable for information acquisition and customer service. The chatbot technology is the AI application that the organization realize that the marketplace of AI gets explore to automation and computer assisted activity so that the organization could stay competitive.The tasks are related to streamlining procurement through augmentation and automation that has Chatbot capability. It require access to set intelligent data and robust them. The procuebot is a brain or a frame of reference. Chatbots could be utilized so that:
- Supplier could speak during the trivial conversations.
- Actions could be set and send to suppliers with regard to governance and compliance materials.
- Placing the request for purchasing.
- Internal questions are research and answer in regard to procurement functionalities or supplier.
- Documenting or filing or receiving invoices that could requests for payments.
- Supplier Relationship Management (SRM) for Predictive and Machine Learning Analytics: The Suppler Relationship Management (SRM) provide strategic and operative processes that begin with the strategy of purchasing, procurement controlling and supplier management for ordering process. The SRM system objective is for close link suppliers. The selection and sourcing form a right supplier increases the concern that could enhance the CSR, supply chain ethics and supply chain sustainability. There are risk related to supplier which have become a ball and chain for the brands that is globally visible. The Supplier Relationship Management (SRM) has generated an action for Data sets such as the audits, supplier assessments and providing credit scoring for further decisions in regard to the supplier. The passive gathering of data could be made active by Machine Learning and intelligence algorithms. The supplier selection has become intelligible and productive. It has a platform were success could be achieve with first collaborations. For the inspections of the human information are easily available and it get generated through machine to machine automation. It provides multiple scenarios with best supplier as per the user desires.
- Machine Learning for Warehouse Management:The application that run the Machine Learning algorithms could easily analyze large, diverse data sets, improve demand forecasting accuracy which is the most challenging aspects for supply chain management and predict the production that are demandable for the future (Pavlik, 2016). The Machine learning is very effective with factors that have no tracking or it may even quantify over time. It reduce the freight costs, improve the delivery performance of the supplier and minimize the risk that the supplier receive with collaborative supply chain networks. The Machine learning has its core constructs that could ideally provide insight to improve the performance of the supply chain management. New products are forecasting that are drive to new sales were strong results are obtained through machine learning (Kitano, 2016). The organization could extend the supply chain assets that include engines, machinery, warehouse equipment and transportation that could be collected through IoT sensors.
- Natural Language Processing (NLP) for Data Robustness and Cleaning:The Natural Language Processing has the ability were the program of the computer could be easily understood through human speech when spoken. NLP interact with computer and human languages. It has an activity for the computer which is easy to analyze, understand and generate the language that are natural. It has a linguistic forms, methods and activities of communication that could publish, translate, and read. With advances in NLP we could easily send text message through phone. It is not enough to get a sequence of words even parsing sentences are not enough either (Meiring & Myburgh, 2015). There is a very limited domain the computer has that provide an understanding which is presently possible for limited domains. The interaction is possible for the computer were human can understand the spoken language that is natural.
- Logistics and Shipping Autonomous Vehicles:The local and regional shipping could be considered as the most and largest antiquated industries. There are several trucks that require on-site human judgment. The technology are autonomous and the process is highly unlikely. The features for automation are payment, scheduling and pricing that could streamline the cumbersome process for shippers, carriers and drivers. Cargo ships are container ships that put larger effort to automate for the industry (Lieto et al., 2015). The individual self-driving trucks are automated and has big change for the truck industry. The individual self-driving trucks could not reduce the costs of transportation but could test with the convoys that are connected to sensors, GPS and Wi-Fi were it has cameras so that it can be connected to the trucks. The speed and the direction is determined by the leading vehicle along with other convoy to automatically steer and change the speed.
The organisation need to use some AI based application which helps in expanding the business logistics. There had been made some analysis for some of the AI application by considering its potential advantages and disadvantages based on the investigation done of the above application and provide a legal, social and ethical point of view. For the organisation to grow it has proposed the following system and provides with some potential advantages and disadvantages:
- Machine Learning for Warehouse Management:
The warehouse and inventory-based management forecast supply flaws has become a disaster for the company that is based on customer. The forecasting engine along with the machine learning keeps on looking for algorithms and data streams with different forecasting hierarchies. For the forecasting loop there is an endless Machine Learning with self-improving output. It has the capabilities to reshape the warehouse management.
- Logistics and Shipping Autonomous Vehicles:
The organization could use the intelligence in logistics and shipping that could focus on the supply chain management. The lead time and transportation expenses of the shipping get reduce and add operations of elements that are environmental friendly, reducing the costs of the labor and widen the gap in between the competitors.
- Natural Language Processing (NLP) for Data Robustness and Cleaning:
AI and Machine Learning has NLP element to stagger the potential that is deciphering the foreign language in large amounts in a streamlined manner. NLP will be built in the data sets with regard to the suppliers and decipher for information that are untapped, it has language barrier. The technology of NLP could be streamline the compliance and auditing actions which are unable because of the language barriers that is existing between the bodies of the buyer and the suppliers.
Conclusion
The above study provides information that the logistic world which is a complicated one as it needs a lot of planning, the ability to adjust and resilience for unforeseen circumstances that happen. The organisation could logistically automate the work process which is an alternate route for derailing the vehicles for bad weather and road construction. The technology can reduce the amount of time spent and money that determine the logistics to replenish the organisation by determining the best vehicle to carry a load. This has proved that AI machines are more capable than the human intelligence. Prediction is difficult for AI to achieve cognitive ability and in-depth knowledge about the human being. Thus, the impact of logistics turns to innovate the technology with practical solutions. The business systems data, machine learning, create operational efficiencies to make the business decisions better. The use of AI computing techniques can teach systems to recognise the patterns and issue an action or recommendation on it.
After investigating about the AI and its application that has proposed the recommendation being provided as per the study:
- AI in Logistics and Supply Chain: From the experience of the customer friction could be removed with the physical artificial intelligence by combining the analytics and the customer data. The “Uberization” and mobile technology would be better to improve the businesses where there is a demand for shorter delivery from retailers and the same is expected from the manufacturers.
- Connecting to Logistic Technology providers: The organization could have risk and opportunities for corporate supply chain. The e-commerce and its operation is demandable as it increase the efficiency were the organization could need automation and technology for their supply chain market.The purchasing behavior and expectations of delivery are closely aligned with the consumers’ habits. The organization businesses are growing keener which could the inventory closer to the customers. The AI and IoT will help the logistic industry to successfully support other verticals.
- Self-driving car and Flying warehouse: The self-driving vehicles is the astonishing technology to safe the human drivers. The data is gathered through multitude of sensors which include 360-degree car views. In that way accidents and traffic jams could be avoided by potentially communicating. Self-driving cars are particularly useful to save costs, make deliveries more efficient and faster, and are useful for the delivery companies. Vehicles are widely use to improve the driving autonomously on the busy roads. It could also ensure proper legal framework. The patent of flying warehouse that is patent by Amazon could visit places that make autonomous drones for developing a project. Technology has been evolving rapidly that flying warehouse in the upcoming future it would resemble the portrayed futuristic movies.
References
Chen, M., Challita, U., Saad, W., Yin, C., & Debbah, M. (2017). Machine learning for wireless networks with artificial intelligence: A tutorial on neural networks. arXiv preprint arXiv:1710.02913.
Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452.
Hill, J., Ford, W. R., & Farreras, I. G. (2015). Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversations. Computers in Human Behavior, 49, 245-250.
Hricik, D., Morgan, A. L. S., & Williams, K. H. (2018). Ethics of Using Artificial Intelligence to Augment Drafting Legal Documents. Texas A&M Journal of Property Law, 4(5), 465-484.
Kitano, H. (2016). Artificial intelligence to win the nobel prize and beyond: Creating the engine for scientific discovery. AI magazine, 37(1), 39-49.
Lieto, A., Radicioni, D. P., Castelfranchi, C., Frixione, M., Sandini, G., & Sharkey, A. (2015). Advances in artificial intelligence and cognition. In Proceedings of EAP-COGSCI 2015 conference, Euro-Asian Pacific joint conference on cognitive science (pp. 25-26).
Lu, S., & Burton, S. L. (2017). Man vs Robots? Future Challenges and Opportunities within Artificial Intelligence (AI) Health Care Education Model. Proceedings of the RAIS Conferece I, 6, 7.
Meiring, G. A. M., & Myburgh, H. C. (2015). A review of intelligent driving style analysis systems and related artificial intelligence algorithms. Sensors, 15(12), 30653-30682.
Müller, V. C., & Bostrom, N. (2016). Future progress in artificial intelligence: A survey of expert opinion. In Fundamental issues of artificial intelligence (pp. 555-572). Springer, Cham.
Patil, P. (2016). Artificial intelligence in cybersecurity. International Journal of Research in Computer Applications and Robotics, 4(5), 1-5.
Pavlik, J. (2016). Cognitive computing and journalism: implications of algorithms, artificial intelligence and data for the news media and society. Brazilian Journal of Technology, Communication, and Cognitive Science, 4, 1-14.
Russell, S., Dewey, D., & Tegmark, M. (2015). Research priorities for robust and beneficial artificial intelligence. Ai Magazine, 36(4), 105-114.
Scherer, M. U. (2015). Regulating artificial intelligence systems: Risks, challenges, competencies, and strategies. Harv. JL & Tech., 29, 353.
Schölkopf, B. (2015). Artificial intelligence: Learning to see and act. Nature, 518(7540), 486.
Wong, T. Y., & Bressler, N. M. (2016). Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. Jama, 316(22), 2366-2367.
Yang, S. (2018). Brain-inspired artificial intelligence and bioengineering. Life Research, 1(1), 23-29.
Yaseen, Z. M., El-Shafie, A., Jaafar, O., Afan, H. A., & Sayl, K. N. (2015). Artificial intelligence based models for stream-flow forecasting: 2000–2015. Journal of Hydrology, 530, 829-844.
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