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Artificial Intelligence $($AI$)$ and Intelligence Augmentation $($IA$)$ differ fundamentally in how they interact with managerial decision$-$making. AI refers to systems that operate autonomously, making decisions or recommendations without human intervention, whereas IA focuses on enhancing human intelligence by providing data$-$driven insights to assist in decision$-$making $($Davenport & Ronanki, $2018).$

Liability for AI$-$Based Decisions

When a decision is made solely based on an AI$-$driven intelligent application, liability often depends on the level of autonomy and the organization$$s oversight. If an AI system operates without human intervention and results in a wrong decision, the company deploying the AI could be held accountable. For example, in automated trading systems, an AI$-$driven algorithm may execute trades leading to financial losses. If the AI system malfunctioned due to flawed programming, the developer or the deploying firm may bear responsibility $($Bathaee$,2018).$ This is because AI lacks legal personhood and decision$-$making accountability, meaning responsibility must be assigned to the organization or developers overseeing the system.

Liability for IA$-$Based Decisions

With IA$,$ humans remain central to decision$-$making, using AI$-$generated insights as a supporting tool. If a manager relies on IA for strategic planning and the decision leads to negative consequences, the responsibility typically lies with the human decision$-$maker. This is because IA does not function autonomously but rather augments human cognition by providing enhanced analytical capabilities. For instance, in healthcare, IBM Watson assists doctors in diagnosing diseases by analyzing vast datasets. If a physician misdiagnoses a condition despite IA$$s recommendations, the liability still falls on the doctor, as they have the final decision$-$making authority $($McKinsey & Company, $2021).$

Examples of AI and IA Applications

AI Application: Chatbots in customer service operate independently to resolve queries without human oversight. An example is AI$-$powered chat assistants like Google$$s Dialogflow.

IA Application: Predictive maintenance systems in manufacturing provide managers with data$-$driven insights to prevent equipment failure. These systems do not make decisions independently but support human oversight.

Privacy Concerns

AI Privacy Issues: Fully automated systems can raise concerns about data misuse and bias. For instance, AI chatbots collecting customer interactions may store sensitive data without explicit user consent, posing risks of data breaches.

IA Privacy Issues: Since IA combines human oversight with AI$-$driven insights, privacy risks involve potential misuse of augmented intelligence recommendations. In predictive maintenance, employee performance metrics could be misused for workforce monitoring, leading to ethical concerns over workplace surveillance $($Acemoglu & Restrepo, $2020).$

In conclusion, AI and IA play distinct roles in managerial decision$-$making, with AI functioning autonomously and IA assisting human decisions. Liability in AI$-$based decisions typically falls on the deploying organization, while IA$-$based decisions place responsibility on human users. However, both approaches raise privacy challenges that require careful governance.