Harnessing the benefits of human-AI interactions in unstructured decision making: the quest for performance-conditioning factors
Project title: Harnessing the benefits of human-AI interactions in unstructured decision making: the quest for performance-conditioning factors
Department: Department of Informatics and Media
Area of research: Information systems
This research proposal focuses on the performance benefits from the use of artificial intelligence (AI). Our focus is on the identification of factors that condition decision-making performance when humans and AI work together in unstructured decision situations. This documents starts with an introduction into the topic and the research direction assumed, which is followed with the specification of objectives for the AI4 Sabbatical work.
Research Project Focus
Even though firms and government organisations are increasing their investment in various kinds of AI systems, there is a lack of knowledge of how to ensure that the use of AI applications is beneficial. The intended outcomes of the here proposed research are expected to offer guidance on the use of AI in complex unstructured decision-making situations.
Uses of AI have found their way into domains such as logistics, industrial operations and areas of services including banking, finance and insurance. Until recently, the use of AI primarily targeted structured situations. In such situations, work processes and decision making are repetitive and well understood, with notable examples provided by robotics in car manufacturing and automation in the processing of insurance claims and mortgage applications. Such routinized decision making is characterised by unambiguous outputs, inputs and rules of the decision-making process. These characteristics make it possible to automate structured work processes and the associated decisions and thus replace human decision making with decisions by machines.
In addition to this established yet restricted use of AI in structured situations, recent attempts have been made to use AI to support, or augment, human decision making in unstructured situations. Real-life examples include Unilever’s talent acquisition process, Netflix’s decisions on movie plots, directors and actors, and Pfizer’s drug discovery and development activities. Unstructured decision making is characterised by ambiguous and/or changing objectives and decision criteria, with human judgement required to complete the decision-making process.
Three influential books on the use of AI for decision making provide normative guidance on how AI can improve organisational performance: Brynjolfsson and McAfee (2014), Daugherty and Wilson (2018), and Davenport and Kirby (2016). These books collectively suggest that the use of AI in augmented unstructured decision making benefits organisations more than mere automation of structured processes. Consequently, Microsoft will “build intelligence that augments human abilities and experiences. Ultimately, it’s not going to be about human vs. machine”. Similarly, in the preamble to its AI guidelines, Deutsche Telekom states that “AI is intended to extend and complement human abilities rather than lessen or restrict them”, whereas IBM affirms that “the purpose of AI and cognitive systems developed and applied by the IBM company is to augment human intelligence”.
Whilst all these intentions are well meant, extrapolations based on isolated lab studies are not satisfactory or rigorous enough. There is currently no conclusive evidence from real-life settings that AI-augmented decision-making processes perform better than unstructured decisions by humans alone. We know little about the contingent factors that condition the success of AI-augmented decisions. Therefore, the key objectives of the intended empirical research are (a) to provide rigorous empirical evidence that AI-augmented decision making in unstructured situations outperforms human decision making, and (b) to identify the factors that condition the success of AI-augmented decision making. Achieving these objectives will extend the currently limited knowledge base in this domain and will provide important normative guidance for organisations regarding the use of AI applications.
The here specified research direction builds on our recent project that received the 2019 Best Paper award from the “Strategic Management Society” for its identification of counterintuitive performance in AI-augmented decision making in unstructured situations. That PostNord co-sponsored study rests on a quasi-natural experiment, where a group of sales representatives in a firm performed significantly better in their sales decision making when supported by an AI-powered sales support system designed to provide a fit of several complementary factors. That fit was achieved by the matching of a decision maker’s cognitive style with two factors: the information representation mode offered by the AI system and the mode of work process conduct through which a given individual executes work tasks, uses the AI system and makes decisions. All this was compared with three control groups. Two groups used the same AI-powered system, without ensuring a fit with these complementary factors, whilst the other group used an older decision-support system without AI. The results show that the group with complementary fit performed best. Surprisingly, however, the results also show that the group without the AI-powered system performed better than the two groups with the AI-powered system unmatched to the complementary factors.
Overall, that study shows that AI-augmented decision-making performance is contingent upon a set of factors that must complement each other to generate the desired outcome. Crucially, this finding shows that any organisation – business, health or government – that invests in a wrongly configured use of an AI system may actually be worse off than not making any such investment and continuing with business as usual. Despite breaking new ground, that study has some limitations because it disregards two well-established factors that condition an individual’s decision performance (i.e. decision-maker’s motivation and decision authority span). My AI4Research Sabbatical intends to prepare for an empirical study that targets that gap.
Aim & Objectives for the AI4Research Sabbatical
Given the above, the overall aim for my AI4Research Sabbatical is to prepare for a successful execution of the above outlined research direction. That aim is to be achieved through realization of the following sabbatical objectives:
- To have exposed the here assumed research direction to the AI4Research Fellows
- To have established a researcher network within the AI4 context
- To have established a research team for a joint research proposal
- To have established cooperation with one or more industrial firms that commit to a participation in an empirical study
- To have developed a full project proposal for empirical filed study that is to investigate the use of AI augmented decision making in unstructured situations
- To have submitted the formulated project proposal to one or several funding agencies
- To have developed detailed measurement instruments for the collection of data in execution of the proposed empirical study
The here assumed research focus involves theoretical components from several disciplines such as psychology, social-psychology, sociology, information systems and AI, and managerial economics. Uppsala University’s AI4Research initiative, with its Fellows from various disciplines, represents therefore an important intellectual environment for the formulation of a successful research project, as proposed here. The AI4Research Fellows may inform the present initiative (i) about additional theoretical components of relevance for this research direction, (ii) about how to link the various theoretical factors into a coherent framework, and then (iii) about how to practically utilise those theoretical components in an empirical study. The benefit of the AI4Research initiative may also have the other direction, as the research proposal outlined here will inform the Research Fellows about the state of the art in AI research that focuses the exploration of operational mechanisms which generate value creation from the use of AI systems.
Beside myself, this application includes also Sandra Bergman, a doctoral student at the Department of Informatics and Media. Her research is co-funded by Department and the Swedish National Research School on Management and Information Technology. Her interest focuses particularly on the conduct of decision-making processes in virtual teams, where at least one decision-actor is an AI-application, such as Google’s current use of its AI system Deep Mind to help scientists spread globally, working together to understand the devastating corona virus.