ACTIVATING ENTERPRISE PROCESSES WITH HUMAN - AI TEAM

Alpana Dubey

We have seen great advancements in the adoption of AI system in the last couple of decades. Improvements in AI technology has changed our perception about what can be achieved by computing machinery. Even though AI has been largely impacting the way we work, one of the common relationships between human and AI system, that has been leveraged till date, is using AI system as a “tool” to perform certain task not much as a “partner” to work together to solve a common goal by augmenting each other.   This view has largely influenced AI research to push towards achieving highest level of automation through AI system by enhancing AI systems with more data, better algorithms, and better computing power. These efforts have brought forth several new approaches and better systems. However, one important aspect that has not been well leveraged in this journey is the critical role human could play to boost the performance of Human-AI system even with the current set of technologies [2, 3].

A symbiosis between Human and AI systems can result in much efficient and effective system than the human or the AI alone. One of the common reasons for this is that human and AI bring unique and complementary strengths. This phenomenon has been also expressed in the detail explanation of Moravec’s paradox [4, 5]. For instance, Human are good at reasoning with smaller number of observations, but AI system needs a large amount of data to provide the same level of reasoning. Human cannot process large amount of data at speed for interpretation whereas AI system are good at generating interpretation from large data. Humans exhibit conformation bias while interpreting data whereas AI system can be designed to be un-biased by appropriate un-biasing algorithms. Humans are good at dealing with novel unfamiliar and dynamically changing situations whereas AI system are not good at handling new situations or adopting to a new situation. Humans are good at setting goals and hypothesis. AI systems are good at executing the goals once the rules of goal planning are established. A set of interesting examples of human and AI complementing each other can be seen in the Hollywood movie Upgrade released in 2018 where humans are transplanted with AI chips to complement their strength.

Considering the orthogonal strengths human and AI brings, this is high time to view and model business processes as Human-AI team. Paul et al. in their book “Human+Machine: Reimagining Work in the Age of AI” [1] have touched upon various relationships between Human and AI and how they can be leveraged by the organizations for AI based transformation. In this article, we discuss the design principles that one must take in account while designing a Human-AI teaming application.

Example of Human-AI Team in Business Process

We draw a small example from service industry as shown in above Figure 1 to understand a Human-AI team. Consider a service engineer troubleshooting a machine. We can imagine several AI systems augmenting the service engineer while (s)he is troubleshooting the machine. For instance, an AI system, namely “Knowledge retrieval Assistant” which is based on information retrieval and natural language processing (NLP) can retrieve the troubleshooting instructions and related help documentation about the machine. As these documents may be huge and large in number, fetching the relevant sections from the documents is going to be very time consuming for the service engineer whereas computer algorithms can do this at much faster rate. At the same time how to use all the information together to really fix the machine is something where service engineer could apply his creativity. There may be some situations where the service engineer may not be able to fix the machine with current set of instructions. Here, another system, namely “Expert Assistant”, helps in finding the experts who can provide support to the service engineer while he is troubleshooting and fixing the machine. Here, we see that two AI assistant are amplifying the service engineer capabilities by providing relevant details and connecting with experts and human is finally solving the issue.

Properties of Effective Teaming

Imagine that the AI assistants in the previous examples are not only acting as mere tools; but they are intelligent entities which have visibility of what actions other AI assistants are performing, what actions the service engineer has taken, the type of machine (s)he is fixing, what (s)he is trying to fix, what kind of fixes (s)he has made in the past, and what are his/her observations from the machine. In addition, consider the assistants also understand the intention of the service engineer and status of the machine. All this information would make AI assistants smarter and intelligent because with this information they can proactively support the engineer based on the current context and the goal. This kind of teaming intelligence is of utmost importance for effective Human-AI teaming where individual entities are “context aware”, “goal aware”, “self-aware”, and “proactive”. In addition, for effective teaming, interactions among the entities need to be fluid either with a proper communication language and or with appropriate GUIs. We have developed a taxonomy of Human-AI teaming concepts by combining concepts available in literature as shown in Figure 2.  The taxonomy is broadly developed around 4 key dimensions and 15 sub-dimensions. While designing Human-AI teaming application, one needs to consider these dimensions and assess whether the essential characteristics are met in the design.

Steps to realize Human-AI Team in Business Process

Successfully realizing Human-AI team in a business process requires a thorough analysis of the process. The key steps that one needs to pursue are Analyze, Model and Develop / Deploy as shown in the side bar.

Once these steps are executed, one need to use his/her favorite implementation platform. However, the current platforms do not support all the design elements that we have discussed so far. These concepts have been discussed at an abstract level in the literature and lack a proper guidance on how these can be realized concretely.

HACO Platform

Through our research efforts at Accenture Labs, we are advancing approaches for software application development to transform enterprise processes with more teaming intelligence.  Our platform namely Human AI Collaboration Platform (HACO) helps in realizing the essential properties a Human-AI teaming application must have. The key features and the workflow involved in the implementing Human-AI teaming application is shown in Figure 3. A short video about the platform can be access at https://youtu.be/lNyrrk8dMqU

A research paper, based on this work, will be published in the proceedings of 13^th Innovations in Software Engineering Conference ISEC 2020, to be held on Feb 27^th – 29^th, 2020. 

References:

[1] Human + Machine: Reimagining Work in the Age of AI. Paul R. Daugherty, H. James Wilson.

[2] https://beta.techcrunch.com/2016/04/12/the-era-of-ai-human-hybrid-intelligence/?_ga=2.184699881.13417694.1526480637-1368337914.1526480637

[3] https://techcrunch.com/2016/11/01/how-combined-human-and-computer-intelligence-will-redefine-jobs/

[4] Minsky, Marvin (1986), The Society of Mind, Simon and Schuster, p. 29

[5] Hans Moravec, Mind Children: The Future of Robot and Human Intelligence, p. 15.

[6] HACO: A Framework for Developing Human-AI Teaming. Accepted at 13^th Innovations in Software Engineering Conference (ISEC 2020)

About the Author

Alpana is a senior researcher at Accenture Labs, Bangalore. She leads Digital Experiences R&D group at Bangalore. Alpana has close to 14 years of experience working in industrial research labs. During her research career, she started new research areas and drove company-wide adoption of software engineering best practices. She has several patents, has published over two dozen research papers in scientific journals, and peer reviewed technical conferences of repute in the area of software engineering, crowdsourcing, static analysis, reverse engineering, and pattern mining. Before joining Accenture, she worked with research labs of ABB, Siemens, and Philips. She is a senior member of IEEE and received her PhD in Computer Science and Engineering from IIT Kanpur (India) in 2006.