Algorithms and Machine Learning Overview

Algorithmic affect refers to how algorithms influence people's emotions and feelings, and vice versa.

Understanding how algorithms affect us requires examining the 'forces of encounter' - how algorithms interact with and influence our experiences.

The way we perceive and interpret algorithms, including their social and ethical implications, shapes their performance and impact.

Moral panic describes widespread fear and anxiety about a perceived threat to societal values or well-being.

This thought experiment encourages us to question the power and influence attributed to algorithms, exploring their potential for shaping our thoughts and beliefs.

A way of thinking and acting that embraces technology as a replacement for traditional values and beliefs, particularly in the realm of spirituality.

A mix of data-informed assumptions and subjective narratives often used by individuals attempting to leverage algorithms, especially in online spaces.

A type of algorithm specifically designed to analyze, optimize, and manage various aspects of urban environments.

The ability of those who control algorithms to influence our lives, potentially creating both positive and negative outcomes.

A belief that technology, particularly algorithms, will solve complex problems and improve our lives, even if it means overlooking potential ethical issues.

A belief that technology, particularly algorithms, will solve complex problems and improve our lives, even if it means overlooking potential ethical issues.

A group of individuals who leverage algorithms to gain influence, often using a combination of data and storytelling.

The use of algorithms to create a sense of authority or trustworthiness in online spaces, often by emphasizing data-driven approaches.

Artificial intelligence (AI) refers to the development of computer systems capable of performing tasks requiring human intelligence, such as learning, reasoning, problem-solving, and natural language understanding.

Machine learning (ML) is a subset of AI that uses algorithms and data to enable computer systems to learn and improve from experience without explicit programming.

Supervised machine learning involves training algorithms on labeled data to make predictions or classifications based on patterns identified in the data.

Unsupervised machine learning uses algorithms to discover patterns and structures in unlabeled data, enabling clustering, anomaly detection, and dimensionality reduction.

AI systems often rely on massive datasets for training and development. This data can be collected from various sources, including user interactions, social media, and sensors.

The creation and annotation of datasets for training AI models is a crucial but often laborious process. It involves collecting, labeling, and verifying large amounts of data.

Biases and discrimination can be embedded in AI systems during the data collection, annotation, and training processes, reflecting and amplifying existing societal inequalities.

Crowdsourcing platforms, such as Amazon Mechanical Turk, enable AI developers to leverage a large workforce to perform tasks like data annotation, potentially introducing biases from the diverse perspectives of annotators.

A business model emphasizing speed and innovation with minimal regard for regulations, often associated with tech startups.

The idea that platform companies can operate outside of traditional regulations due to their reliance on data and technology, rather than traditional industries.

The resistance to platform companies from established businesses within the same industry, often driven by concerns over market competition.

Platforms often highlight economic empowerment and opportunity as a key benefit, attracting investors and consumers alike.

Platforms can operate more freely in informal economic sectors, facing less resistance from established actors.

Media or content that operates outside the constraints of specific platforms, offering more flexibility in distribution and interaction.

Media or content that relies on a specific platform for its distribution, monetization, and often, its creation.

The process of platforms shaping how content is produced, distributed, and monetized, often influencing the creative choices of creators.

Apps that provide a wide range of services, essentially becoming platforms for various cultural and economic activities, often found in East Asian markets.

The importance of understanding platforms beyond the dominant US-Chinese tech world, considering the historical and cultural differences in platform development.

The evolving nature of news production, where journalists increasingly adapt to platform trends, sometimes compromising independence.

The rise of independent news outlets with online subscriber bases, avoiding advertising revenue and potentially maintaining a higher degree of editorial freedom.

The trend of cultural and economic activities shifting online, with platforms playing a central role in shaping these online spaces.

Platform firms are hybrid entities that resemble both marketplaces and workplaces with workers engaging in both autonomous work and controlled environments. They establish rules for pricing, data collection, and task allocation but cede control over work processes and methods to workers. This hybridity results in a precarious model, where workers gain autonomy but experience uncertainties associated with fluctuating demand, algorithmic oversight, and limited job security.

These platforms, designed to be 'permissive potentates,' centralize control over crucial aspects like pricing, task allocation, data collection, and revenue while allowing workers flexibility in their work processes and schedules. This creates a seemingly independent work environment but with underlying control and limitations.

Platforms exploit a fragmented workforce by externalizing traditional employer responsibilities such as insurance and maintenance. They leverage economies of scale through digital intermediation, allowing them to maximize profits while minimizing costs and responsibilities. This model benefits from a dispersed workforce, hindering worker solidarity and promoting competition.

The gig economy's rise can be attributed to factors like technological advancements, a flexible workforce, and the demand for on-demand services. It's a multifaceted phenomenon with roots in technological innovation and societal shifts toward flexible working arrangements.

The study investigates the evolving nature of the gig economy in Amsterdam, Berlin, and New York City, focusing on food delivery and domestic cleaning services. Through multi-sited ethnographic fieldwork, interviews, and analysis of various data sources, the research aims to shed light on the complexities of this dynamic labor market.

The research utilizes ethnographic fieldwork techniques including participant observation and semi-structured interviews to gain a deep understanding of the gig economy's realities from different perspectives. This approach allows for nuanced insights into the experiences, challenges, and opportunities faced by workers in the gig economy.

By analyzing policy reports, industry documents, and platform company data, the research aims to understand the overarching economic and social implications of the gig economy. This will help inform the development of policies and regulations that address the concerns of workers in this rapidly changing landscape.

The gig economy presents a complex landscape where workers simultaneously experience freedom and constraints. Platforms leverage technology to create a seemingly independent work environment, but this autonomy is often governed by algorithmic oversight, rating systems, and fluctuating demand. The study aims to unpack this complex relationship between perceived freedom and underlying control.