Aloisi's Chapter on Digital Transformation

Aloisi believes the ongoing digital transformation is altering the structure of work relationships, not necessarily reducing overall employment.

Coase noted that within a company, market transactions are replaced by entrepreneurs (directors, coordinators, or managers) directing production.

Historically, employment relationships were seen as a private system with clear roles for task designers and executors.

In 2019, UK workers, supported by a union, filed lawsuits alleging they were unfairly dismissed by automated systems.

The GDPR aims to ensure automatic decisions are fair, transparent, and adequately explained.

Contrary to worries about automation eliminating jobs, modern technology is transforming the type of work, stressing quality over quantity.

Algorithms are rapidly changing workplaces, making it necessary for researchers and lawmakers to examine new regulations for employers.

The shift to online platforms and digital tools for work tasks, requiring workers to use building access systems, time-tracking tools, health data apps, or collaborative platforms.

Increasing use of data collection and analysis to manage work tasks and worker performance.

Using algorithms and AI to perform human resource tasks.

Traditional management rights within a company, often linked to the contract of employment.

A situation where data holders (managers) have more information than data subjects (workers).

Systems that automatically make decisions in workplaces and other institutions.

Rights granted to employers regarding the management and control of their employees, and employees' tasks.

The current laws regarding employment relationships.

The relationship between holders of authority and those subject to it, considering how that relationship has evolved.

It's the ability to analyze vast amounts of data to understand personality traits that aren't immediately obvious. This is an important advancement because it can help us predict and analyze employee behavior.

They use data to create schedules that adapt to worker preferences and optimize shift allocation in real-time. Basically, it's like a smarter version of a traditional schedule.

This refers to subtly influencing employee behavior through rewards and punishments implied by the system, rather than explicit rules. For example, an app might prioritize tasks based on worker performance, implicitly encouraging better work.

They are flexible and can adapt to different tasks, even collaborating with humans on physically demanding work. This allows humans to focus on problem-solving and knowledge-based activities.

These robots are connected to the internet and use sensors to collect data, providing insights into the production process and helping to optimize efficiency.

They can enhance human capabilities, detect safety hazards, and improve productivity by analyzing data from the user's physical activity.

They can help workers perform better and safer by overcoming physical limitations, detecting potential health issues, and improving productivity.

It leads to a shift towards a more structured and data-driven approach, with technology influencing worker behavior and decisions. This creates new challenges for defining traditional employer-employee relationships.

The increased use of technology can lead to issues like limited employee autonomy and potential algorithmic biases, impacting fair treatment and decision-making.

It's important to consider regulations and ethical guidelines to ensure transparency, fairness, and individual rights are respected in the context of digital HR practices.

The General Data Protection Regulation (GDPR) sets rules for transparent and explainable automated decisions in the workplace, including the right to human intervention and an explanation of the decision.

Under the GDPR, individuals have the right to understand how an automated decision-making system reached a conclusion about them. This includes getting an explanation of the logic behind the decision.

Algorithms can be used to make unfair decisions about employees based on protected characteristics like race, gender, or age.

The GDPR and other legal frameworks aim to prevent discriminatory practices by promoting transparency and accountability in the use of algorithms in workplaces.

The GDPR's focus on procedural fairness allows workers to challenge discriminatory practices without needing to access the code of the algorithms ('black box').

Management approach using data collection and analysis to optimize operations and decision-making.

Algorithms can influence worker rights by impacting scheduling, access to jobs, performance evaluations, and even employee termination.

The right of workers to understand how algorithms are used to assess their performance and make decisions about their work.

Judges actively interpreting laws and precedents to address modern issues and adapt to changing circumstances.

The lack of transparency and understanding around how algorithms function and make decisions.

When algorithmic systems unintentionally push certain groups of workers to the periphery of opportunities.

The process of applying penalties or consequences for worker behavior, potentially influenced by automated systems.

The General Data Protection Regulation (GDPR) aims to ensure transparency and fairness in automated systems that affect individuals.

Being dismissed or having work opportunities restricted by an automated system.