• We discussed AI in healthcare and education
• Promise vs reality: lab results ≠ deployment
• Human-AI collaboration works better than either alone
• Equity concerns cut both ways
• Today: What happens when AI is used to deceive?

Part 1: The landscape

• What is misinformation?
• AI and synthetic media
• Why it matters now

Part 2: Deepfakes

• Technical foundations
• Real-world examples

Part 3: Why we fall for it

• Evolutionary wiring and fake news
• System 1 vs System 2
• Confirmation bias and motivated reasoning
• Bandwagon effect and the “in the know” feeling

Part 4: Responses

• Technical detection
• Platform policies
• Legal frameworks
• Media literacy

Key distinctions:

Why the distinctions matter:

• Intent shapes appropriate responses
• Different actors, different motivations
• Legal treatment and solutions vary by category

The old problem:

• Misinformation is ancient
• Technology changes scale and speed
• But the human vulnerabilities remain

Before generative AI:

• Fake images required Photoshop skills
• Fake videos needed studios, fake audio needed voice actors
• Production was expensive and slow
• Detection was often possible

After generative AI:

• Anyone can create convincing fakes
• Production cost approaches zero
• Quality improves rapidly
• Detection becomes harder as generators improve

The scale problem:

• Generate thousands of fake articles instantly
• Create targeted content for specific audiences
• Human moderators can’t keep up

How platforms amplify misinformation:

• Engagement = revenue
• Outrage drives engagement
• False information is often more engaging
• Algorithms optimise for clicks, not truth

The correction problem:

• Lies spread faster than corrections
• False stories reach millions
• Corrections reach thousands
• Even debunked claims persist in memory

Vosoughi et al. (2018):

• Analysed 126,000 stories on Twitter
• False stories 6x more likely to be retweeted
• Reached more people, spread faster

Political deepfakes:

Financial fraud:

• CEO voice clone authorised £200K transfer
• Estimated $25 million scam in Hong Kong (2024)

Non-consensual intimate imagery:

• Vast majority of deepfakes (96%+)
• Women overwhelmingly targeted
• Tool for harassment and extortion
• Legal response lagging

Key insights from RAND research:

• Deepfakes are one tool among many
• “Cheap fakes” often just as effective
• Context matters more than quality
• Social vulnerability enables technical attacks

The liar’s dividend:

• When anything could be fake…
• Real evidence can be dismissed as fake
• “That video of me is a deepfake!”
• Truth becomes contested terrain

RAND’s threat assessment:

• Near-term: Targeted harassment, fraud
• Medium-term: Election manipulation
• Long-term: Erosion of shared reality

The evolutionary angle:

• Our brains evolved for small groups and immediate threats
• Novelty once meant survival-relevant information
• We still react to new, surprising claims the same way
• False news is almost by definition more novel than truth

Why outrage spreads:

• Threat detection is fast and automatic
• Emotionally charged content bypasses careful evaluation
• Fear and disgust trigger sharing behaviour
• We evolved to warn the group, not fact-check first

Kahneman’s (2011) dual-process theory:

Social media is a System 1 environment:

• Infinite scroll rewards quick reactions
• You see a video, react, share, move on
• System 2 never kicks in unless something jolts you
• Deepfakes are designed to feel real at System 1 speed

The problem:

• Catching a fake requires System 2
• But the platforms are built for System 1
• Strong emotion (outrage, fear) suppresses System 2

What it is:

• We seek evidence that confirms what we already believe
• We dismiss evidence that contradicts it
• This isn’t laziness; it’s how brains manage information overload
• Nickerson (1998) called it the most pervasive bias in human reasoning

How it works with deepfakes:

• Same technology, same quality. Different reaction
• Taber & Lodge (2006): correcting misinformation can strengthen the original belief
• Corrections feel like an attack on identity, not just on facts

What Asch showed in the 1950s:

• People will deny what their own eyes tell them if enough others disagree
• About 75% of participants conformed at least once
• Even when the correct answer was obvious

On social media, numbers are social proof:

• A video with 2 million views “must be real”
• 50,000 retweets = credibility
• We outsource our judgment to the crowd
• The crowd is just as fooled as we are

How this helps deepfakes spread:

• Early shares create a snowball
• Each share adds perceived legitimacy
• By the time fact-checkers respond, millions have seen it
• Taking it down can actually increase belief (“they’re hiding something”)

Sharing as social currency:

• Breaking news makes you feel like an insider
• “Did you see this?!” = status in your group
• Being first to share gets the attention

Why this matters:

• Creates a race to share before verifying
• People share content they haven’t fully watched
• Emotional headlines are enough to trigger a repost

Combined with the other biases:

• Confirmation bias picks what we share (things we agree with)
• The bandwagon effect picks when (once it’s already popular)
• The “in the know” feeling picks how fast (immediately, no checking)

Detection approaches:

Current state:

• Detection works on yesterday’s fakes
• Generators improve faster than detectors
• Arms race with no end in sight

The fundamental problem:

• Detectors can be fooled
• Adversarial training improves generators
• Real-time detection is difficult
• Scale: Millions of images, few moderators

What platforms do:

Challenges:

• Volume: Billions of uploads
• Speed: Viral before review
• Jurisdiction: Global platforms, local laws
• Incentives: Engagement vs truth

What happens when platforms remove content:

• Moves to less moderated platforms
• Removal can increase belief (“they’re hiding something”)

Emerging legislation:

• US: State laws (Texas, California), federal bills pending
• EU: AI Act includes deepfake disclosure
• UK: Online Safety Act addresses synthetic media
• China: Requires disclosure of AI-generated content

Challenges:

• Jurisdictional: Creators can be anywhere
• Enforcement: Finding perpetrators is hard
• Definition: What counts as a “deepfake”?
• Speech: Satire, art, legitimate uses

No single solution works:

• Detection: Arms race, always behind
• Provenance: Adoption takes years
• Platform policies: Incentives misaligned
• Legal frameworks: Slow, jurisdictional
• Media literacy: Helps but doesn’t scale

The realistic goal:

• Not elimination of misinformation
• Raise costs of deception
• Slow the spread
• Maintain enough trust for democracy

The landscape

• Misinformation is old; AI changes scale
• Attention economy amplifies false content
• Platform incentives work against truth

Deepfakes

• Synthetic media quality improving rapidly
• Political manipulation, NCII, fraud
• The “liar’s dividend” undermines all evidence

Why we fall for it

• Evolutionary wiring makes novelty irresistible
• System 1 scrolling, System 2 sleeping
• Confirmation bias is the vulnerability no patch can fix
• Bandwagon and “in the know” feeling accelerate sharing

Responses

• Detection: necessary, insufficient
• Provenance: promising, years away
• Legal: slow, jurisdictional
• Literacy: helps, doesn’t scale
• Comprehensive approach required