February 08, 2021
Everybody is a genius. But if you judge a fish by its ability to climb a tree, it will live its whole life believing that it is stupid- Albert Einstein
The purpose of any AI application is to reshape enterprise functions, capable enough to amplify human cognitive capabilities. Lately, natural language processing, machine learning, and AI applications are widely used to provide personalized recommendations using predictive capabilities, starting from chatbot to serve end-customers to complex predictions in diversified AIOps and healthcare research. There are growing expectations on machine learning and AI applications, but finding the right balance between AI and human is a challenging and crucial task. Underestimating or neglecting this partnership will cause a huge impact on efficacy.
Base variants of decision-making methodologies
The level of transparency in the decision-making process is vital for the greater acceptance of AI applications. Unfortunately, most of these AI systems are available in the form of a black box.
‘Closed decision-making’ models work well with preset variables, structured KPIs, and pre-established rules for making decisions. It expects data in a specific form and structure. An automatic language translator is a great example of a closed decision-making approach where it runs with predefined rules of grammar and meaning.
In contrast, “open decision-making” models are for situations that are unknown or undefined. These AI applications are meant to learn and act like certain human experts in an unknown environment. These are built with huge data-processing capabilities to process unstructured, unanticipated, and multi-sourced data. Natural language processing algorithms developed to access contextual information from both human and external dynamic systems such as vendor contract terms and dynamic ITSM service catalog items are good examples of an open decision-making approach.
Erroneous or delayed decisions will lead to high risk, reputational damage, and financial loss. Human inputs are particularly critical in situations where it’s necessary to make timely and precise decisions. Human experts can enrich natural language processing, machine learning, and AI applications, and lay a path for further learning.
Humans over AI applications
When sensitive business domains are leveraging AI applications, the level of complexity and risk is high, consequences of poor decisions can cause serious impact. Algorithms may be good at identifying results but making AI applications rely on human experience is necessary to mitigate risks. The need for the human mind is essential to bring efficiency and accuracy with a mutual learning experience. Humans can train AI systems regularly as a coach. In this context, the human expert is a long-term partner under a peer-to-peer relationship.
AI applications over humans
There are several different forms to bond AI applications with human awareness. When contextual parameters are well defined with properly coded algorithms to learn through data under a supervised machine learning model, the need for human intelligence is low in the decision-making process. In such cases, humans are only involved as chiefs but not as active players. These include allowing AI applications to fire notifications during failures or pauses, warm transferring to humans, and transferring control to humans for further actions.
Human and AI applications together
AI applications are faster and better than humans in transcribing audio to text. But humans are far better than AI applications in differentiating cats and dogs in a picture. Identifying, qualifying, and facilitating interaction between human and AI applications is an art, that depends on the context and expected outcomes. To be effective, allow AI applications to share the right piece of information to humans whenever it’s required. Also design should be implemented in a way so as to leverage human intelligence.
Who is winning?
The right balance of power between human intelligence and AI applications is essential to maximize outcomes. It is critical to understand and design systems to know which side should take the final call and when. In both closed and open decision-making models, transparency in decision-making methodology is a key factor. In low-risk situations, the necessity of human skill to control outcomes might be less, but in high-risk situations, AI applications might require constant human support. To achieve maximized ROI, when humans make decisions, AI applications should learn. Likewise, humans should be skilled enough to understand the insights of AI applications.
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He is passionate and keen to remain knowledgeable about IT infrastructure, Artificial Intelligence (AI), Automation, Neural nets, Machine Learning (ML), Natural Language Processing (NLP), Image Processing, Client Computing, Blockchain, and Public/Private Clouds. His research interest circles around stack solutions to enterprise problems.
Kumar has a MS in Software Engineering (SE) along with several technical certifications like MIT’s Artificial Intelligence (AI), Machine Learning (ML- Information Classification), Service Oriented Architecture (SOA), Unified Modeling Language (UML), Enterprise Java Beans (EJB), Cloud and Blockchain, etc.