Podcast
Questions and Answers
What is a core concern about the 'Black Box Effect' in algorithms?
What is a core concern about the 'Black Box Effect' in algorithms?
not exactly know how it works
What are the 3 types of Algorithmic bias mentioned in the content?
What are the 3 types of Algorithmic bias mentioned in the content?
Correlation is the same as Causation in Machine Learning.
Correlation is the same as Causation in Machine Learning.
False
Ghost work involves unpaid workers contributing to building, maintaining, and testing ___ systems.
Ghost work involves unpaid workers contributing to building, maintaining, and testing ___ systems.
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Match the following labor-related terms with their descriptions:
Match the following labor-related terms with their descriptions:
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What type of learning involves feeding a model a lot of data, but fine-tuning is needed?
What type of learning involves feeding a model a lot of data, but fine-tuning is needed?
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What is the purpose of ghost work in algorithm training?
What is the purpose of ghost work in algorithm training?
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Algorithmic management aims to replace labor entirely.
Algorithmic management aims to replace labor entirely.
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____ is used by big tech companies to conceal their activities.
____ is used by big tech companies to conceal their activities.
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What is the definition of an algorithm in computer science?
What is the definition of an algorithm in computer science?
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Match the following terms with their descriptions:
Match the following terms with their descriptions:
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What is the calculation problem related to?
What is the calculation problem related to?
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What are the 5 properties of an algorithm?
What are the 5 properties of an algorithm?
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What happens if machine learning is given biased or lacking data?
What happens if machine learning is given biased or lacking data?
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What are the environmental concerns related to artificial intelligence?
What are the environmental concerns related to artificial intelligence?
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Machine learning can only solve problems that we already know how to solve.
Machine learning can only solve problems that we already know how to solve.
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Rule-based learning involves telling the computer a set of rules, then the computer will complete the __________.
Rule-based learning involves telling the computer a set of rules, then the computer will complete the __________.
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Match the following machine learning terms with their descriptions:
Match the following machine learning terms with their descriptions:
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What is representational harm in algorithms?
What is representational harm in algorithms?
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What is historical bias in algorithms?
What is historical bias in algorithms?
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Evaluation bias occurs when the benchmark data used for a particular task represents the use population.
Evaluation bias occurs when the benchmark data used for a particular task represents the use population.
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____ bias arises when there is a mismatch between the problem a model is intended to solve and the way in which it is actually used.
____ bias arises when there is a mismatch between the problem a model is intended to solve and the way in which it is actually used.
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Match the following bias types with their descriptions:
Match the following bias types with their descriptions:
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What type of blurriness is unacceptable?
What type of blurriness is unacceptable?
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What is crucial for curation and documentation of datasets?
What is crucial for curation and documentation of datasets?
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Large language models do basic logic and are trained to understand logical thought.
Large language models do basic logic and are trained to understand logical thought.
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String prediction models predict the likelihood of a token given its ______ context.
String prediction models predict the likelihood of a token given its ______ context.
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Match the following critical theorists with their viewpoints:
Match the following critical theorists with their viewpoints:
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What issue does the critique of algorithmic fairness focus on?
What issue does the critique of algorithmic fairness focus on?
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What type of systems model students' socio-emotional states to influence desired behaviors?
What type of systems model students' socio-emotional states to influence desired behaviors?
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Proctoring software recognizes darker skin.
Proctoring software recognizes darker skin.
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Techno-solutionism suggests that social problems can be solved by __________.
Techno-solutionism suggests that social problems can be solved by __________.
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Match the following areas of concern related to biopolitical algorithms:
Match the following areas of concern related to biopolitical algorithms:
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What is an example of a technology that enhances security through isolation and compartmentalization, discussed in the text?
What is an example of a technology that enhances security through isolation and compartmentalization, discussed in the text?
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What does Qubes emphasize in relation to continuous innovation and collective strategies to combat pervasive data surveillance?
What does Qubes emphasize in relation to continuous innovation and collective strategies to combat pervasive data surveillance?
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Which term best describes computer enthusiasts driven by an inquisitive passion for tech systems and committed to an ethical version of info freedom?
Which term best describes computer enthusiasts driven by an inquisitive passion for tech systems and committed to an ethical version of info freedom?
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Counter-Al technology uses compartmentalization like Qubes to address concerns about _________________.
Counter-Al technology uses compartmentalization like Qubes to address concerns about _________________.
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Counterdata collection is when institutions effectively collect and utilize data for analysis purposes.
Counterdata collection is when institutions effectively collect and utilize data for analysis purposes.
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Study Notes
Algorithm Definition
- An algorithm is a set of instructions for solving a problem or completing a task in a planned sequential order.
- It is a set of instructions telling a computer what to do.
Programs and Software
- A program is an algorithm + data structure.
- Algorithm alone does not make software; it needs to be computable.
Properties of Algorithm
- 5 properties of an algorithm: finiteness, definiteness, input, output, and effectiveness.
Machine Learning
- The notion that we can program a computer to learn itself.
Foucault's 3 Levels of Power
- Strategic games between liberties; Power through the algorithm, Power as a form of political, social, and economic domination.
Datafication
- The practices and relations through which data is constituted and made legible.
- Data is fundamental to Al, but often overlooked.
Data Positivism
- Data's role, but critics argue that it overlooks biases and errors in data-driven systems.
Al Discourse
- A prominent discussion in Al discourses is that it will change everything, reduce inequality, enhance productivity, and make things more accessible.
The Age of Al
- Promises of technical and economic efficiency, as well as fairness, are an important component in the discourses of "New elites".
Al Concerns
- Environmental cost/effects of computing material and labor conditions.
- Bias and discrimination.
- Epistemological challenges.
Critical Al Studies
- Al has become an empty word; it's not artificial intelligence, but rather an ideology.
- The fact that we think it's a technology is part of its ideology.
Enchanted Determinism
- Data-driven systems are often perceived as both magically unknowable and absolute in their prediction patterns.
Algorithmic Organization
- The way an algorithm organizes and orders the world is never neutral.
Demystifying Al
- Artificial intelligence equals machine learning algorithms.
Rule-Based Learning
- You tell the computer a set of rules, then the computer will complete; however, you need to already have the rules/solution.
Machine Learning
- Can solve things that we don't know how to do.
Machine Vision
- Very closely related to machine learning/pattern recognition; one problem is that the real world may not fit your rules.
Supervised Machine Learning
- The first age of machine learning; the data has to be labeled, which is expensive.
Unsupervised Machine Learning
- Just give data to the model and let it find the pattern; requires too much data.
Al Shift
- From automation of logic reasoning to automation of pattern recognition.
What Algorithms Can Do?
- They can classify and predict, but they also create.
Algorithmic Power
- Epistemic challenges; the way algorithms produce knowledge implies concerns for agency, responsibility, and accountability.
Black Box Effect
- Dealing with algorithms raises questions of responsibility and accountability.
- A core concern about this tech is that it's very good at recognizing patterns, but we don't exactly know how it works.
Algorithmic Bias
- These technologies are not neutral; they are biased over certain groups of people and knowledge.
- 3 types of algorithmic bias: Historical bias, Data set bias, and Algorithmic bias.### Correlation and Causality
- Our knowledge and thinking are based on causality, but we are starting to think that causality is not the only way to understand the world, especially with machine learning and prediction without causality.
Worker Conditions and Labor Automation
- Worker conditions are not good, with many experiencing stress and physical strain to keep up with picking rate quotas (e.g., Amazon).
- Workers are considered less valued or trusted compared to robotic systems, and are often treated like robots.
- Human-Al collaboration often lacks fair negotiation, and is akin to 19th-century labor dynamics.
Ghost Work and Exploitation
- Ghost work involves unpaid workers contributing to building, maintaining, and testing AI systems.
- AI functionability relies on workers performing repetitive digital tasks, but they receive little credit and fair compensation.
- Content moderators perform crucial tasks, but are poorly paid and can suffer psychological trauma.
Facade of Automation and Exploitation
- Some systems present a facade of automation while relying heavily on human labor behind the scenes.
- Workers face exploitation due to interchangeable roles and low compensation.
- AI development relies on the exploitation of human labor across the supply chain.
Amazon Labor and AI Development
- Amazon presents a positive image to the public with incentives and slogans, but workers voice concerns about physical strain and scheduling issues.
- AI and algorithms intensify worker exploitation across various sectors.
Annotator Labor and Exploitation
- Annotation is critical but often seen as temporary or unimportant by engineers focused on AI model building.
- Annotators perform tasks like labeling emotions in videos, classifying spam, and correcting AI errors, often underpaid.
Leisure Society and Mass Unemployment
- AI is automating human labor at a speed that's never been done before, leading to concerns about mass unemployment.
- There are two views: a utopian view, where AI brings increased leisure, quality of life, safety, and security; and a dystopian view, where AI leads to economic inequality, loss of purpose, surveillance, and control.
Hidden Labor and Asymmetric Distribution
- There are three types of hidden labor: data annotation, human reinforcement, and content moderation.
- The benefits and disadvantages of technology are not equally distributed.
Algorithmic Management and Surveillance
- Algorithmic management is used to manage labor, not replace it.
- Surveillance, standardization, and flexibility are used to control labor.
- Researchers buy labor without knowing the conditions or pay, leading to exploitation of cheap labor in other countries.
Reconfiguration of Societal Structures
- AI transforms societal structures, power dynamics, labor, governance, ethics, and cultural practices.
- There is a need to reshape the economy in response to profound changes by AI and automation.
Bias in Machine Learning
- Machine learning data can be biased, but it's not only the data's fault.
- The machine learning pipeline involves a series of choices, and bias can arise at any stage.
- Bias can be allocative, representational, historical, or deployment-based.
Fairness and Bias
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Fairness algorithms modify the modeling to satisfy particular notions of fairness.
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Data sets are used to train computers, but bias can arise from technical constraints or pre-existing biases.
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Emergent bias arises in a context of use with real users.
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To remedy bias, we need to be able to identify or diagnose bias in a system and develop methods to avoid and correct it.### Data Sets and Bias
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Data sets consist of a collection of images labeled and sorted into categories, but lack of representation can lead to underperformance due to bias.
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Algorithmic bias can occur due to interconnected aggregation of biases, and researchers' decisions on model development.
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Bias in data labeling can promote harmful stereotypes and representations.
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Bias cannot be simply fixed due to lack of social context, imperfect processes, and no clear definition of fairness.
Model Cards and Fairness
- Model cards provide essential information about an AI model, including its development, intended use cases, and details about its performance.
- Fairness is about justice and technical solutions, and investigation of categorization and classification of people.
- Language models have biases towards languages that are already well-represented, leaving many languages underrepresented.
Fighting Bias in AI
- Researcher Joy Buoamwini fought against algorithmic bias, highlighting how facial recognition algorithms failed to recognize Black women's faces.
- Hallucinations in AI models occur due to the gap between statistical production of language and the real world.
Environmental Effects of Large Language Models
- Large language models require substantial energy and result in high CO2 emissions and financial costs.
- These effects disproportionately affect marginalized communities.
Mitigation Strategies
- Prioritize environmental impact assessments in model research.
- Budget research for dataset curation and documentation to minimize bias.
- Intersectional bias in LM's can encode more bias against marginalized identities.
Curation and Documentation
- Crucial for curation and documentation are datasets, and minimizing bias.
- Lovelace effect: users perceiving computing systems as original and creative, but focusing on human reactions, not machine capabilities.
Foundation Models
- Foundation models are a type of machine/deep learning model that starts with a large dataset and finds patterns.
- They are pre-trained and require a whole ecosystem, and need tons of data.
Dangers of Large Language Models
- Environmental cost: data centers need huge computer power and result in carbon emissions.
- Unfathomable training data: size of datasets and refusal of company transparency make it difficult to critically assess.
Language Models and Understanding
- Attribution of models: trained on the most probable way to repeat, but lack strong semantic understanding.
- Logical thought: models do not understand logical thought, only trained on statistical prediction.
Pandemic and Education
- Shift to remote online learning during the pandemic worsened existing systematic inequalities in education.
- Al became prominent, raising concerns about bias and fairness.
Critical Theorist
- Argue that education AI systems aim to present visions of a particular type of learner, but reproduce inequitable societal structures.
- Call for a more radical approach to rethink what is possible and remake the world in a more just and equitable way.
Surveillance in Schools
- Surveillance tech in classrooms is widely accepted, but students rarely know and consent to it.
- Monitoring emails and online use at school gives false positive rates for LGBTQ-related terms, claiming it's hate speech.
Biopolitical AI Systems
- Model students' socio-emotional states to influence desired behaviors.
- Algorithmic controversies in the UK: using algorithms to predict A-levels and giving lower grades to kids in lower-class schools.
Early Dreams of Machine Teaching
- Pressy's MC teaching machine (1920s): reward structure.
- B F Skimmer's adaptive teaching machine (1950s): emphasizes the closeness of the student and machine.
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Description
Learn about the definition of an algorithm in computer science, including its role in solving problems and completing tasks.
