Trust & Safety Overview
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Questions and Answers

Creating new classifiers for CSAM is straightforward.

False

What does harassment and bullying typically involve?

Interpersonal aggression or offensive behavior communicated over the internet.

Which of the following is an example of hate speech?

  • Criticizing a public figure
  • Expressing an unpopular opinion
  • Sharing political views
  • Calling for violence against a specific group (correct)

    • What are the categories of hate speech mentioned?

    <p>Religious, racist, gender &amp; sexuality</p> Signup and view all the answers

    Hate speech is illegal everywhere.

    <p>False</p> Signup and view all the answers

    Which law criminalizes the apology of fascism in Italy?

    <p>Law 645/1952</p> Signup and view all the answers

    What does §230 protect in the United States?

    <p>Good Samaritan blocking and screening of offensive material</p> Signup and view all the answers

    What is the main objective of the Digital Services Act (DSA)?

    <p>Create a safer online environment</p> Signup and view all the answers

    What is one challenge of online hate speech?

    <p>Anonymity of the speaker</p> Signup and view all the answers

    What is Trust & Safety?

    <p>Trust and safety is the study of how people abuse the internet to cause real human harm, often using online products the way they are designed to work.</p> Signup and view all the answers

    Which of the following are factors that drive Trust & Safety? (Select all that apply)

    <p>Regulatory pressure</p> Signup and view all the answers

    Match the type of online harm with its description:

    <p>Child Abuse &amp; Nudity = Depicting or engaging in the abuse or exploitation of children Violence = Directly threatening or enabling acts of physical violence Hateful Content = Expressing or encouraging hatred against specific groups Fraud = Attempting to wrongfully deceive for financial benefit</p> Signup and view all the answers

    Trust & Safety teams only work reactively.

    <p>False</p> Signup and view all the answers

    What is the role of algorithms in Trust & Safety?

    <p>Algorithms help identify, sort, and remove harmful content across various media formats.</p> Signup and view all the answers

    What is a common shortcoming of natural language processing classifiers in content moderation?

    <p>Struggles with nuance</p> Signup and view all the answers

    Deepfakes are considered a beneficial aspect of AI technology in Trust & Safety.

    <p>False</p> Signup and view all the answers

    What is one purpose of the Bad News game?

    <p>To expose manipulation techniques used in disinformation.</p> Signup and view all the answers

    What is the definition of disinformation?

    <p>Information intended to mislead.</p> Signup and view all the answers

    What are some proactive measures Trust & Safety teams can take?

    <p>Counter-messaging, awareness-raising, and education.</p> Signup and view all the answers

    What is one method used to identify new CSAM?

    <p>User reports</p> Signup and view all the answers

    Automated systems for identifying CSAM are as widespread as hash-matching.

    <p>False</p> Signup and view all the answers

    What is a significant legal challenge in creating classifiers for CSAM?

    <p>Restrictions regarding possession of CSAM</p> Signup and view all the answers

    Bullying typically involves targeted, repeated behavior intended to cause _____ harm.

    <p>physical, social and/or psychological</p> Signup and view all the answers

    What is one of the factors that makes hate speech online particularly challenging?

    <p>Anonymity of the speaker</p> Signup and view all the answers

    Hate speech is illegal everywhere in the world.

    <p>False</p> Signup and view all the answers

    What does the Digital Services Act aim to create?

    <p>A safer online environment</p> Signup and view all the answers

    Which of these laws criminalizes gender-based violence online in Italy?

    <p>Law 115/2013</p> Signup and view all the answers

    What is Trust & Safety?

    <p>Trust and safety is the study of how people abuse the internet to cause real human harm, often using online products as intended.</p> Signup and view all the answers

    Which of these is NOT a main factor that drives Trust & Safety?

    <p>User profitability</p> Signup and view all the answers

    Which category includes sexual exploitation and child abuse?

    <p>Violent &amp; Criminal Behavior</p> Signup and view all the answers

    Regulatory pressure is a driving factor for Trust & Safety.

    <p>True</p> Signup and view all the answers

    What was one of the first companies to use the term Trust & Safety?

    <p>eBay</p> Signup and view all the answers

    What is disinformation?

    <p>Harmful content intended to influence an outcome</p> Signup and view all the answers

    The acronym T&S stands for ______.

    <p>Trust and Safety</p> Signup and view all the answers

    Match the form of problematic content to its category:

    <p>Disinformation = Dis-, Misinformation, &amp; Propaganda Hate speech = Harassment &amp; Hate Speech Child pornography = Child Sexual Abuse &amp; Exploitation Extremism = Terrorism, Radicalization, and Extremism</p> Signup and view all the answers

    AI moderation is generally more effective for clearly defined content categories.

    <p>True</p> Signup and view all the answers

    What is a potential strategy for mitigating misinformation?

    <p>Improving individuals' ability to identify misinformation</p> Signup and view all the answers

    Study Notes

    Trust & Safety (T&S)

    • Definition: The study and practice of preventing and reducing online harm using technology and policies.

    • Core Factors Driving T&S:

      • Corporate Responsibility: Companies are increasingly seen as responsible for online harm.
      • Crisis Sensitivity: T&S teams often emerge in response to crises like scams, fake reviews, and safety concerns.
      • Regulation: Global laws, like the EU DSA and UK Online Safety Act, mandate T&S measures for large online platforms.
      • Technological Standards: Platforms like Apple impose rules on app content and behavior.

    Taxonomy of T&S Issues

    • Violent & Criminal Behavior:

      • Dangerous Organizations: Presence and support of criminal groups.
      • Violence: Threats, encouragement, or enabling of physical violence.
      • Child Abuse & Nudity: Depictions or engagement in child abuse.
      • Sexual Exploitation: Depictions, threats, or enabling of sexual violence or exploitation.
      • Human Exploitation: Engaging in or enabling human trafficking or coercion.

    • Regulated Goods & Services:

      • Regulated Goods: Sale or trade of restricted or illegal goods.
      • Regulated Services: Sale or trade of restricted or illegal services.
      • Commercial Sexual Activity: Depictions and offerings of sex acts or nudity for money.

    • Offensive & Objectionable Content:

      • Hateful Content: Expressions of hatred, contempt, discrimination, or violence against protected groups based on race, religion, gender, or sexual orientation.
      • Graphic & Violent Content: Shocking or offensive depictions of violence, death, and injuries.
      • Nudity & Sexual Activity: Non-commercial depictions, solicitations, or offerings of sex acts or nudity.

    • User Safety:

      • Suicide and Self-Harm: Content encouraging or enabling self-harm.
      • Harassment and Bullying: Intimidating, degrading, or humiliating individuals or groups.
      • Dangerous Misinformation and Endangerment: Content that could unintentionally cause harm.
      • Hateful Conduct & Slurs: Targeted hate speech directed at individuals.

    • Scaled Abuse:

      • Spam: Unsolicited and unwanted content, often commercial advertising.
      • Malware: Links to malicious software.
      • Inauthentic Behavior: Using fake accounts to deceive or manipulate users.

    • Deceptive & Fraudulent Behavior:

      • Fraud: Deception for financial gain, encouraging or supporting fraudulent activities.
      • Impersonation: Taking over the identity of another user or group.
      • Cybersecurity: Attempts to compromise accounts or sensitive information.
      • Intellectual Property: Use of trademarks or copyrighted content without permission.
      • Defamation: Damaging the reputation of others.

    • ** Platform-Specific Rules**:

      • Format: Rules on the form of content, such as word limits, restrictions on links or shared files, and required detail levels.
      • Content Limitations: Rules on specific topics, like off-topic content, restrictions on selling or advertising, spoilers, and trigger warnings.

    Evolution of T&S

    • Early Origins:

      • eBay used the term T&S in 1999, forming a "Rules, Trust and Safety" team in 2002 to combat fraud.
      • Academics explored the term in a conference article around the same time.

    • Growth and Diversification:

      • Companies initially managed T&S within departments like operations, legal, and cybersecurity.
      • Modern T&S teams have varied scopes, missions, and structures.

    • Academic Focus:

      • T&S intersects with internet governance, policy, platform governance, disinformation, and other areas.

    From Twitter to X: Content Moderation

    • Free Speech Absolutism:

      • Websites initially resisted content filtering, prioritizing unfettered expression.

    • The Moderator’s Dilemma:

      • The Prodigy case (1995) held online services liable for defamatory content, while CompuServe (1993) was shielded for its hands-off approach.

    • Shift to Moderation:

      • The rise of hate speech, pornography, and threats to user safety forced websites to adopt moderation practices.

    • Current Approach:

      • Calls for increased moderation around illegal content.
      • Mixed opinions about legal but harmful content.

    Two Main Approaches to T&S

    • Reactive:

      • Responding to user reports and flagged content.
      • Escalation to human moderators.

    • Proactive:

      • Content detection and removal before user visibility.
      • AI-based content moderation.

    Technologies Used in T&S

    • Tools:

      • Digital Hash: Identifying content through unique digital signatures.
      • Image Recognition: Analyzing visuals for content violations.
      • Metadata Filtering: Identifying content based on data like file type or location.
      • Natural Language Processing (NLP) Classifiers: Analyzing text for policy violations.

    • Shortcomings:

      • Circumvention Techniques: Techniques to bypass detection, like altering file formats.
      • Biases in Training Data: Skews in data representation can lead to bias in AI models.
      • Lack of Transparency: Balancing transparency with potential downsides in database population.
      • NLP Challenges: Difficulty in accurately identifying nuanced language patterns, leading to over- or under-inclusiveness.

    Advancements in LLMs and Generative AI

    • New and Enhanced Risks: LLMs can be used to generate harmful content.
    • Potential for Assistance: LLMs could potentially aid in content moderation.

    Models of Content Moderation (CoMo)

    • Artisanal: In-house moderation on a case-by-case basis.
    • Community: Moderation decisions made by volunteer user networks or committees.
    • Industrial: Decisions made by specialized teams using automated tools and contractors.

    Human Moderators

    • Mental Health Impact: The job can be emotionally taxing and stressful.
    • Decision-Making Challenges: Quick and complex decisions under time pressure.
    • Bias Concerns: The risk of bias is inherent in human judgment.
    • Inadequate Support: Limited support for moderators before, during, and after their work.

    Problematic Content Categories

    • Dis-, Misinformation, & Propaganda:

      • Disinformation: Intentionally harmful or destructive content to influence outcomes.
      • Misinformation: Content that unintentionally contradicts or distorts facts.
      • Propaganda: Information used to promote a cause, often featuring biased or misleading information.

    • Harassment & Hate Speech:

      • Harassment: Interpersonal aggression communicated online.
      • Hate Speech: Speech that aims to incite hatred or discrimination against groups.

    • Terrorism, Radicalization, & Extremism:

      • Terrorism: Violence aimed at generating fear.
      • Radicalization: Change in beliefs and behaviors towards justifying violence.
      • Extremism: Belief system based on unwavering hostility towards specific groups.

    • Child Sexual Abuse & Exploitation (CSAM):

      • U.S. Law: Requires U.S.-based companies to report potential CSAM to NCMEC.

    • Tech Solutions for CSAM:

      • Apple: Uses on-device hash matching of known CSAM images for detection, prioritizing user privacy.
      • Digital Hash & Image Recognition: Digital hash technology converts images into numerical signatures for identification.

    • Challenges with New CSAM Detection:

      • Data Set Limitations: Legal restrictions on possessing CSAM make it difficult to train AI models effectively.
      • High Error Rates: AI classifiers trained with limited data can have high error rates, leading to potential misidentifications with serious consequences.

    • Harassment & Hate Speech:

      • Challenges: Anonymity and offline consequences make online harassment a complex issue.
      • Factors: Anonymity, power dynamics, and psychological impact contribute to hate speech's effectiveness.

    Generative AI and Disinformation

    • Impact: GenAI tools can produce realistic fake images and content, increasing the risk of disinformation.
    • Challenges:
      • Hallucinations: LLMs can generate inaccurate or nonsensical content.
      • Scale: The ability to generate vast amounts of text can facilitate widespread dissemination.
      • Ease of Access: Users can readily utilize LLMs without intermediaries, increasing the potential for misuse.

    Mitigation Strategies

    • Individual-Level:

      • Pre-bunking/Inoculation: Educating individuals to identify and resist misinformation.
      • Debunking: Providing accurate information to counter misinformation.

    • Systemic-Level:

      • Algorithms: Designing algorithms to prioritize trusted content and filter harmful content.
      • Business Models: Promoting reliable media through business models that reward quality content.
      • Legislation: Enacting laws to combat misinformation and online harm.

    Counter-Terrorism and Counter-Extremism

    • Platform Approaches:

      • Reactive: Content removal, account suspensions, counter-speech or counter-activism.
      • Proactive: Counter-messaging, awareness-raising, and education.

    • AI Challenges:

      • Rapid Content Generation: AI accelerates and simplifies the creation of extremist content.
      • Deepfakes: AI-generated images and videos can be used to mislead or manipulate.

    • AI Solutions:

      • Spotting Manipulation: Training AI models to detect manipulated content.
      • Hashing Databases: Using databases of known extremist content for identification.

    This is a comprehensive study note based on the provided text, outlining key concepts, issues, technologies, and approaches in the field of Trust & Safety.

    Online Hate Speech

    • Online hate speech can reach a wider audience compared to offline speech.
    • Online hate speech is easier to access as there is no requirement to join a group or physically opt-in to be in a space accepting of such speech.
    • Online hate speech's reach is easier to track down on various platforms, making it difficult to remove or control.
    • Hate speech laws vary across countries.
    • The US does not criminalize racist or sexist online speech.
    • The EU has more stringent laws against hate speech, making certain speech a crime.
    • The EU considers "apology of fascism," online gender-based violence, and online hate speech propaganda by neo-Nazi groups illegal.

    Online Hate Speech Regulation

    • The US initially led the way in internet regulation.
    • Section 230 of the US Communication Decency Act (CDA) protects online platforms from liability for content posted by users.
    • The EU is setting a new trend with the Digital Services Act (DSA).
    • The DSA aims to create a safer online environment, defining responsibilities for platforms (marketplaces and social media), and addressing challenges like illegal products, hate speech, and disinformation.
    • The DSA does not mandate platforms to remove legal content.
    • The "Brussels Effect" refers to the economic incentive for companies to comply with EU laws to avoid fines and access the lucrative EU market.
    • The EU's proactive approach in tech law reform has led to a one-rule-for-all approach, simplifying compliance for companies.

    Trust and Safety

    • The study of how people abuse the internet to cause real human harm, often using online products as designed.
    • A practice and a field within technology companies concerned with reducing, preventing, and mitigating online harms.

    Drivers of Trust and Safety

    • Corporate Responsibility: Companies have a responsibility to ensure safe and enjoyable user experiences.
    • Crisis Sensitivity: Trust and Safety departments often emerge as a response to harmful events, like scams or fake reviews.
    • Regulation: Growing global regulations like the EU DSA, UK Online Safety Act, and Australia SbD Framework require platforms to establish trust and safety teams.
    • Technological Standards: Platforms must adhere to technological standards like Apple's app rules.

    Taxonomy of Trust and Safety Policies

    • While policies vary between companies, common themes emerge based on underlying human misbehavoir:
      • Violent and Criminal Behavior: Includes dangerous organizations, violence, child abuse, sexual exploitation, and human exploitation.
      • Regulated Goods and Services: Covers sale/trade of regulated or banned goods and services, including commercial sexual activity.
      • Offensive and Objectionable Content: Includes hateful content, graphic and violent content, and nudity and sexual activity.
      • User Safety: Covers suicide and self-harm, harassment and bullying, misinformation and endangerment, and hateful conduct.
      • Scaled Abuse: Includes spam, malware, and inauthentic behavior.
      • Deceptive and Fraudulent Behavior: Covers fraud, impersonation, cybersecurity, intellectual property, and defamation.
      • Platform-Specific Rules: Encompasses content format, limitations on topics, and restrictions on selling/advertising.

    A Brief Overview of Trust and Safety

    • eBay was one of the first companies to use the term "Trust and Safety" in 1999.
    • The concept has been evolving since, with companies building trust and safety teams within various departments like operations, legal, and information security.
    • Trust and Safety as an academic topic overlaps with internet governance, policy, and disinformation.

    Content Moderation - From Free Speech Absolutism to Moderation

    • The "moderator's dilemma" emerged as online platforms faced challenges in balancing free speech with user safety.
    • Early platforms like CompuServe and Prodigy struggled with defamation lawsuits due to their content moderation practices.
    • The introduction of Section 230 aimed to address this dilemma, enabling online platforms to moderate content without being held liable as publishers.
    • The rise of hate speech, pornography, and threats to user safety prompted platforms to increase moderation efforts.
    • Today, there are calls for greater moderation of illegal content, with mixed opinions regarding the moderation of legally harmful content.

    Two Main Approaches to Trust and Safety: Reactive and Proactive

    • Reactive: Relies on user reports, content flagging, and human moderation to respond to harmful content.
    • Proactive: Employs content moderation tools like AI-based classifiers to identify and remove potential problems before they reach users.

    Technologies Used in Trust and Safety:

    • Digital Hash: Creates unique fingerprints for images and videos to identify duplicates and known harmful content.
    • Image Recognition: Uses AI to detect and categorize visual content.
    • Metadata Filtering: Analyzes data associated with files, like timestamps and location data, to identify potential issues.
    • Natural Language Processing Classifiers: Utilize AI to analyze text and identify problematic content based on keywords, patterns, and sentiment.
    • Shortcomings: Circumvention techniques, bias in training data, a lack of transparency, difficulty in capturing nuanced language, and over- or under-inclusiveness.

    Advancements in LLMs and Generative AI:

    • New and enhanced risks associated with the ability of these technologies to generate harmful content.
    • Potential for LLMs to assist with content moderation.

    Models of Content Moderation (CoMo)

    • Artisanal: Content moderation decisions are made on a case-by-case basis by in-house teams.
    • Community: Decisions are made by networks or committees of volunteer users.
    • Industrial: Content moderation decisions are handled by specialized teams with automated tools and contractors.

    Human Moderators

    • Face significant mental health challenges due to the demanding nature of their work.
    • Frequently need to make complex decisions within seconds.
    • Prone to bias.
    • Often receive inadequate support.

    Human Moderators vs. AI in Content Moderation

    • AI excels at detecting clearly defined content with many examples to work with.
    • AI struggles with poorly defined categories, rare content, and subjectivity.

    Problematic Content Categories

    • Dis-, Misinformation, and Propaganda:
      • Disinformation: Harmful or destructive content intended to influence an outcome.
      • Misinformation: Unintentional or inadvertent information that contradicts or distorts facts.
      • Propaganda: Information, potentially true, used to promote a specific political cause or point of view.
    • Harassment and Hate Speech:
      • Harassment: Aggressive or offensive behavior communicated electronically.
      • Bullying: Targeted, repeated behavior intended to cause harm; can be online or offline.
      • Hate Speech: Speech promoting hatred, contempt, and discrimination against specific groups based on protected classes like race, religion, and sexual orientation.
    • Terrorism, Radicalization, and Extremism:
      • Terrorism: Acts of violence intended to generate fear.
      • Radicalization: A shift in beliefs, feelings, and behaviors that justify violence towards others.
      • Extremism: Belief systems that promote hostility towards a specific group.
    • Child Sexual Abuse and Exploitation (CSAM):
      • U.S. law requires companies to report apparent CSAM to NCMEC.
      • Apple uses on-device hash matching to identify known CSAM.
      • Digital hashing and image recognition are used to identify CSAM across platforms.
      • Circumvention techniques and the difficulty in creating new classifiers for unknown CSAM present challenges.

    Hate Speech

    • Explicit Hate Speech: Directly identifies the target group and uses explicit attacks.
    • Implicit Hate Speech: May not directly identify the target group but uses implicit language or context-specific references.

    Challenges in Combating Hate Speech Online

    • Anonymity: Speakers may feel less risk when speaking online compared to in person.
    • Lack of Social Consequences: Consequences for hate speech online are often less direct than in offline settings.
    • Spread and Amplification: Hate speech can spread quickly online, reaching wider audiences more easily.

    Hate Speech

    • Hate speech can be disseminated easily and widely due to its mobility and reach.

    • While hate speech can be easily shared, it can also be challenging to track down across various platforms, resulting in its potential disappearance or indefinite existence.

    • Online hate speech has the advantage of targeting broader or more specific audiences compared to offline speech.

    • Accessing hate speech online is effortless, requiring no physical participation or specific group affiliation.

    • Hate speech isn't illegal everywhere. The US does not criminalize racist, sexist, or other hateful speech online. However, it can be considered a hate crime when associated with other offenses.

    • The EU and its member states have stricter laws regarding hate speech, classifying certain forms as criminal offenses.

    • Italy exemplifies this with laws prohibiting:

      • Apologies for fascism (Law 645/1952)
      • Gender-based violence online (stalking) (Law 115/2013)
      • Criminal conspiracy (Art 416 c.p.)

    • The US, initially a leader in internet regulation, implemented §230 and other laws supporting internet development:

      • Intermediary liability
      • Intellectual property
      • Privacy
      • Corporate
      • First Amendment

    • §230 provides protection for platforms:

      • They are not held liable for content posted by others.
      • "Good Samaritan" provisions allow them to remove objectionable content without liability.

    • The EU has emerged as a new trendsetter with the Digital Services Act (DSA).

    • The DSA aims to:

      • Create a safer online environment.
      • Define platform responsibilities (marketplaces and social media).
      • Address digital challenges like illegal products, hate speech, and disinformation.
      • Implement transparent data reporting and oversight.

    • The DSA doesn't mandate platforms to remove legal content.

    • The Brussels Effect refers to the economic incentive for companies to follow EU law to:

      • Avoid substantial fines (up to 10% of global turnover).
      • Access the lucrative EU market (400 million population).

    • The EU's early adoption of tech law reform provides a significant advantage, as companies prefer a single set of rules for all users, similar to cookie warnings.

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