Abstract
Artificial intelligence (AI) transits from merely adopted technology to fueling everyday decision-making systems from medication to navigation. With this combination of AI in decision-making systems (ADMS), the present study explores how text-based users' data from social media helps organize the users' perspectives of ADMS? To investigate our research questions, we used a framework consisting of three phases, exploratory, confirmatory, and validatory. We applied hierarchy clustering and topic modeling in the exploratory study, hypothesis building, and empirical analysis during the confirmatory study and support vector machine (SVM) in the validatory study. Our findings suggest that users are primarily concerned about the risk involved in using ADMS. Factors like accountability, self-efficacy, knowledge of ADMS individuals' attitudes towards ADMS impact the perception of ADMS among individuals. This study's theoretical and practical implications have great scope as ADMS is still in its elementary stage.
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Appendices
Appendix 1
Table 3
Appendix 2 Questionnaire used for construct measures
In this questionnaire, ADMS is the acronym for Artificial Intelligence in decision-making systems. Kindly provide your rating between 1 and 5, where 1 represents least significant and 5 is highly significant.
Constructs | Description | Items | Rate between 1–5 |
|---|---|---|---|
Privacy/security | Privacy/safety is associated with users' concern about their personal, historical inputs or privacy being exposed while ML models are built, and the individual working on the model could use it | On what scale do you think words like "safe-guard, identity, access, unauthorized, privacy, confidential, non-disclosure, unprotected, threat, spams" are in users' text/comments associated with privacy/security of using ADMS | |
Perceived risk | Perceived risk indicates the user's concern of risk associated with the use of ADMS in their planning. Taking help from the model owner makes the user confident about the risk associated with using ADMS in their decision-making | On what scale do you think words like "volatility, loss, touch, violations, repetitive, superintelligence, safety, unwittingly, surefire, establish" is in users' text/comments associated with perceived risk of using ADMS | |
Fairness | Fairness represents the process for making fair decisions and indicates that users’ certainty about the use of ADMS has no bias over quarterly/annual business plans, and it may impact the business | On what scale do you think words like "simulation, intelligence, processes, confused, AI_salary, speech, machine-vision, interviews, fear, specialization" is in users' text/comments associated with the fairness of using ADMS | |
Accountability | Accountability indicates the ability to take responsibility, decide an action plan, and be accountable for action. Using ADMS, users need a considerable opportunity or independence in making quarterly/annual business plans and have significant autonomy in determining | On what scale do you think words like "biases, mistreatment, agreed, responsible, control, explain, wrong, justification, stakeholders, intentions" are in users' text/comments associated with the accountability of using ADMS | |
Trust | Trust indicates users' trust in the decision made by AI. Like, using ADMS makes it easier to have faith in quarterly/annual business plans | On what scale do you think words like "actions, judgments, diagnosis, peers, harm, safety, worthy, reliability, transparency, dimensions" are in users' text/comments associated with the trust of using ADMS | |
Technological complexity | Technological complexity assesses the required technological level for the design, manufacturing, and operation of the ADMS. This construct indicates users’ indication of ease of use and skillfulness in system operation during decision making | On what scale do you think words like "bureaucracy, service, delivery, rate, SLA, time, substitution, hours, slow, iot" are in users' text/comments associated with the technological complexity of using ADMS | |
Attitude | Attitude indicates users' felling or opinion of using ADMS for business functionalities. Like it is a wise and good idea to use ADMS | On what scale do you think words like "caucasian, black, asian, reactive, aware, know, consciousness, synthetic, AIsoul, AIemotion" are in users' text/comments associated with the attitude toward using ADMS | |
AI readiness | AI readiness represents the steps or principles to collect information about the system, procedure, and equipment so that it can be accessible in the future. Like, Users intend to use ADMS for future business plan activities | On what scale do you think words like "Cloud, blogspot, salary, generic, concept, un-restricted, eval, verify, persistent, principles" are in users' text/comments associated with AI readiness to use ADMS | |
Self-efficacy | Self-efficacy indicates individual belief to execute the task confidently. Like, users’ belief in the requirement of strong confidence and behavioral intention to run and use ADMS | On what scale do you think words like "representative, breach, strong, belief, capacity, performing, task, affectivity, social, robots" are in users' text/comments associated with self-efficacy of using ADMS | |
Demographic | Demographic represents users experience and opportunities till time they get, in decision making | On what scale do you think words like "installation, version, frequency, gender, prejudicial, race, loan, penalizing, women, men" are in users' text/comments associated with the demographic of using ADMS | |
Knowledge of ADMS | Knowledge about ADMS represents the users knowledge surrounding ADMS internal algorithm and operational procedure | On what scale do you think words like "teachers, statistics, intent, large, computing, algorithms, development, UI, backend, set-up" are in users' text/comments associated with knowledge of ADMS |
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Kushwaha, A.K., Pharswan, R., Kumar, P. et al. How Do Users Feel When They Use Artificial Intelligence for Decision Making? A Framework for Assessing Users’ Perception. Inf Syst Front 25, 1241–1260 (2023). https://doi.org/10.1007/s10796-022-10293-2
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DOI: https://doi.org/10.1007/s10796-022-10293-2