The focus of knowledge management (KM) is to enable people and organizations to collaborate, share, create and use knowledge. Understanding this KM is leveraged to improve performance, increase innovation, and grow the knowledge base of both people and the organization. Knowledge must be Dynamic, Accurate and Personal to be applied in the decision-making process. Artificial Intelligence (AI) through machine learning allows machines to acquire, process and use knowledge to perform tasks and to unlock knowledge that can be delivered to people to improve the decision-making process. AI plays an important part to delivering knowledge in a digitized organization by elevating how the delivery of knowledge occurs to the people who need it. AI is used to scale the volume and effectiveness of knowledge distribution. It is imperative that when AI is applied to deliver knowledge for people to make decisions; including when AI is used to make decisions without human involvement; that the knowledge is without bias and the decisions made with the knowledge are ethical.
AI provides the mechanisms to enable machines to learn. Incorporating AI in the delivery of knowledge will facilitate fast, efficient, and accurate decision making. AI provides the capabilities to expand, use, and create knowledge in ways we have not yet imagined. AI systems which use machine learning, can detect patterns in enormous volumes of data and model complex, interdependent systems to generate outcomes that improve the efficiency of decision making. The use of AI (machine learning) in delivering knowledge is based on the data that is used to train the machine learning algorithms. We must keep in mind that when it comes to AI, we need both responsible use and responsible design.
Scale the delivery of knowledge
AI plays an important part to delivering knowledge in a digitized organization by elevating how the delivery of knowledge occurs to the people who need it. AI is used to scale the volume and effectiveness of knowledge distribution by:
Ethical issues of AI delivery of knowledge
There are various ethical issues that arise with certain uses of AI technologies. Each type of AI technology will raise different types of ethical issues when it comes to making decisions based on AI. AI technologies include, text analysis, natural language processing (NLP), logical reasoning, game-playing, decision support systems, data analytics, predictive analytics, autonomous vehicles, and Digital Assistants (chatbots), just to name a few. AI also involves a number of computational techniques such as classical symbol-manipulating inspired by natural cognition, or machine learning via neural networks.
Some examples of the decisions being made with knowledge that is provided by AI applications include:
In financial services (including insurance companies) many organizations are deploying AI solutions. In many cases financial service organizations are combining different AI solutions combined with machine learning (i.e., Robotic Process Automation, Language processing/NLP, Deep Learning Decision solutions) to deliver knowledge to its users to make better decisions. There are several benefits to deploying AI solutions in the financial services sector, which include improving customer service, providing smarter investment tools, credit analysis and scoring, and smarter financial analysis tools. However, these AI empowered tools raise policy questions related to ensuring accuracy, preventing discrimination and bias (especially in credit analysis and scoring) as well as impacting jobs.
As it pertains to AI being used for credit analysis and scoring, the difficulty to explain results from these algorithms have been a problem (OECD Artificial Intelligence in Society, 2019). This is driven by the legal standards in several countries, including the United States that require high levels of transparency. For example, in the United States the Fair Credit Reporting Act (1970), and the Equal Credit Opportunity Act (1974) implies that the process and the output of any algorithm has to be explainable.
AI applications in healthcare and pharmaceuticals has produced many benefits by delivering knowledge to detect health conditions early, deliver preventative services, optimizing clinical decision making, discovering new treatments and medications, delivering personalized healthcare, while providing powerful self-monitoring tools, applications, and trackers. Although AI in healthcare offers many benefits it also raises policy questions and concerns that include access to (health) data and privacy, which includes personal data protection.
The healthcare sector is a knowledge-intensive industry and it depends on data and analytics to improve the delivery of healthcare (treatments, practices, procedures). There has been tremendous growth in the range of information collected, including clinical, genetic, behavioral and environmental data, with healthcare professionals, biomedical researchers and patients producing vast amounts of data from an array of devices (i.e., electronic health records (EHRs), genome sequencing machines, high-resolution medical imaging, and smartphone applications). How this data is collected used and protected will also bring challenges that all countries driven by its legal standards will have to address. In the United States organizations such as the Food and Drug Administration (FDA) and policies such as the Health Insurance Portability and Accountability Act (HIPAA) of 1996 are in place to ensure standards, guidelines, data security and privacy is adhered to and enforced.
There are many challenges for organizations implementing AI in technology that provides access to knowledge for its employees. One specific challenge is around personal data protection principles of collection, use and purpose. To train and optimize AI systems, Machine Learning (ML) algorithms require vast quantities of data. This creates an incentive to maximize, rather than minimize, data collection. With the growth in use of AI devices, and the Internet of Things (IoT), more data are gathered, more frequently and more easily. They are linked to other data, sometimes with little or no awareness or consent on the part of the data subjects concerned.
The patterns identified and evolution of the “learning” are difficult to anticipate. Therefore, the collection and use of data can extend beyond what was originally known, disclosed and consented to by a data subject. AI/ML applications will be able to learn over time and be used to offer individuals tailored personalized knowledge services based on their personal privacy preferences (see Knowledge-as-a-Service below). AI systems being developed around the principles of privacy as detailed by the U.S. Department of Labor and the GDPR will be essential because AI carries the potential to enhance personal data; in turn this enhanced personal data has the potential to cause organizations to violate these policies.
Knowledge-as-a-Service (KaaS) is the framework that combines KM and AI in delivering knowledge services in a more dynamic and personal way. In its implementation, KM is an effort to benefit from the knowledge that resides in an organization by using it to achieve the organization’s mission and the activities of its users. AI and KM facilitated through KaaS makes use of AI through predictive analytics and Knowledge Flow Optimization; by providing a dynamic, accurate and personal delivery of knowledge; through predicting trending knowledge areas/topics that your knowledge workers need and through AI powered search that will be able to understand what knowledge is needed by its users by understanding the intended use of that knowledge.
The ethicality of AI applications must be examined to understand if the outcomes of that specific application of AI is fully understood and it’s not violating our (human) moral compass. The most immediate concern for many is that AI-enabled systems will replace workers across a wide range of industries. AI brings mixed emotions and opinions when referenced in the context of jobs.
However, it’s becoming increasingly clear that although AI may replace some jobs, it will create others. This will create the need to re-skill the workforce to fill these new jobs being created. Research and experience are showing that it’s inevitable that AI will replace entire categories of work, especially in transportation (through autonomous vehicles), retail, government, professional services employment, and customer service. Conversely, companies will be able to deliver knowledge to its workforce in ways that will increase productivity, improve the execution of tasks, and connect its workforce in ways that will enable the ability to take on higher level and higher value tasks.
Info about the Author:
Dr. Anthony J Rhem, PhD., is a recognized thought leader in Artificial Intelligence, Knowledge Management, and technology innovation. Since 1990 has served as CEO/Principal Consultant of A.J. Rhem & Associates, a privately held system integration consulting, training & research firm specializing in Knowledge Management (KM) and Artificial Intelligence (AI). Dr. Rhem consults with venture capitalist and investment firms specifically as it pertains to technology innovations, best practices and trends and has written books and dozens of articles for trade journals in technology, management, and law. As an educator, Dr Rhem has had the pleasure of training hundreds of personnel across many corporations, government and military agencies in the principles, practice and application of strategic management, software engineering, knowledge management, data/information architecture and artificial intelligence. Dr. Rhem continues to be an active presenter at KM and AI conferences both domestic and international.
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If you would like to read more about AI, check out "Bringing AI to Business Intelligence" and "In Search of Corporate Info Pros" by our guest blogger Mary Ellen Bates.