Employing intelligent systems on mobile data

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• Analytics can be used to free up managers to focus on more human aspects of their job. For example, analytics can be used to automate routine tasks, such as performance reviews, so that managers can spend more time on coaching and developing their employees.
• Analytics can be used to give managers more control over their decision-making process. For example, analytics can be used to develop models that predict the outcomes of different decisions.

Major privacy concerns in employing intelligent systems on mobile data

• Collection of sensitive data: Mobile devices collect a wide range of sensitive data, such as location data, contact information, and browsing history. This data can be used to track users' movements, monitor their online activity, and create detailed profiles of them.
• Use of data without consent: Intelligent systems often use mobile data without users' consent. This can be done through the use of hidden trackers or through the terms of service of mobile apps.
• Sharing of data with third parties: Intelligent systems often share mobile data with third parties, such as advertisers and data brokers. This data can be used for targeted advertising or for other purposes that users may not be aware of.

Cases of violations of user privacy from current literature and their impact on data science as a profession

• Cambridge Analytica scandal: In 2018, it was revealed that Cambridge Analytica, a political consulting firm, had harvested the personal data of millions of Facebook users without their consent. This data was then used to target voters with political ads. The scandal raised serious concerns about the privacy of user data and the ethical use of data science.
• Google location tracking scandal: In 2018, it was revealed that Google was tracking the location of Android users even when they had opted out of location sharing. This scandal further eroded trust in the tech industry and raised concerns about the privacy of user data.

These scandals have had a significant impact on the data science profession. Data scientists are now under increased scrutiny and are expected to adhere to high ethical standards. Data science organizations are also taking steps to improve data privacy and security.

Examples of how intelligent systems can facilitate activities such as empowerment, mass customization, and teamwork

• Empowerment: Intelligent systems can empower people by providing them with access to information and tools that they can use to make better decisions. For example, intelligent systems can be used to provide personalized recommendations, identify trends and patterns, and automate tasks.
• Mass customization: Intelligent systems can be used to create mass customization solutions. For example, intelligent systems can be used to design and manufacture products that are tailored to the individual needs of customers.
• Teamwork: Intelligent systems can facilitate teamwork by providing tools for communication, collaboration, and coordination. For example, intelligent systems can be used to share files, manage projects, and schedule meetings.

Here are some specific examples:

• Netflix uses intelligent systems to recommend movies and TV shows to its users. This empowers users to find content that they are likely to enjoy without having to spend hours browsing.
• Amazon uses intelligent systems to personalize its product recommendations and to optimize its supply chain. This allows Amazon to offer a wide range of products at competitive prices.
• Google uses intelligent systems to develop new products and services, such as Google Maps and Google Translate. These products and services have empowered people to navigate the world and to communicate with each other more effectively.

Overall, intelligent systems have the potential to both dehumanize and empower managerial activities. It is important to use intelligent systems in a way that respects the privacy of users and that enhances human judgment and intuition.

Sample Answer Arguments for the dehumanization of managerial activities by analytics Analytics can lead to a focus on numbers and metrics at the expense of human judgment and intuition. For example, a manager might rely on analytics to make decisions about hiring and promotion, without taking into account the human qualities of the candidates. Analytics can lead to a sense of alienation and isolation for managers. If managers are constantly focused on data and metrics, they may lose their connection to the people they manage. Analytics can lead to a loss of control for managers. If managers are relying on algorithms to make decisions, they may have less control over the outcomes of those decisions. Arguments against the dehumanization of managerial activities by analytics Analytics can be used to improve human judgment and intuition. For example, analytics can be used to identify patterns and trends that would be difficult for humans to see on their own.