DATASCI 185: Introduction to AI Applications
Lecture 24: Long-term Safety, Alignment, and Future of AI
Danilo Freire 
Department of Data and Decision Sciences
Emory University
Recap of last class
- Last class: misinformation, deepfakes, and trust online
- Scale, speed, accessibility of synthetic content
- Political manipulation, non-consensual intimate imagery, fraud
- System 1 vs System 2, confirmation bias, bandwagon effect
- Detection (AI vs AI), provenance, platform policies, media literacy
- Today: Looking further ahead at long-term safety and alignment
Source: Chris Ume
Meme of the day!
Source: Cheezburger
Near-term vs long-term concerns
Near-term safety concerns:
- Bias and discrimination (already happening)
- Privacy violations
- Job displacement
- Misinformation (covered last lecture)
- Security vulnerabilities
Long-term safety concerns:
- Alignment: AI pursuing wrong goals
- Loss of human control
- Concentration of power
- Existential risk (controversial)
- Some argue long-term concerns distract from near-term harms
- Others argue near-term focus misses bigger picture
- “Black Swan” events
- They’re connected: Safe systems now → safer systems later
Source: Saetra & Donaher (2023)
Why think about long-term safety now?
- AI capabilities advancing faster than expected
- Safety research takes time
- Hard to retrofit safety later
- Better to be prepared
Historical analogies:
| Technology | Safety lag |
|---|---|
| Nuclear | Developed first, safety after |
| Internet | Security an afterthought |
| Social media | Harms discovered in deployment |
| Biotech | Ongoing debate |
- We’re still early enough to shape development
- Safety research is growing
- Industry increasingly engaged, governments are starting to catch up
Source: Center for AI Safety
Concrete problems in AI safety
Amodei et al. (2016) identified five big challenges:
| Problem | Description |
|---|---|
| Safe exploration | How to learn without dangerous actions |
| Avoiding negative side effects | Don’t break things achieving goals |
| Avoiding reward hacking | Don’t game the objective |
| Scalable oversight | How to supervise complex systems |
| Robustness to distributional shift | Handle novel situations safely |
Why these matter:
- Not speculative, but already happening in deployed systems
- Scale with capability
- Unsolved even for current AI
- Let’s analyse these issues in more detail… 🤓
Source: Brian Christian
Safe exploration
- Learning requires trying new things
- Some actions are irreversible
- “Explore safely” is hard to specify
Examples:
- Robot learning to walk: Don’t break yourself
- Self-driving: Don’t explore by crashing
- Financial AI: Don’t bankrupt the company
- Medical AI: Don’t kill patients while learning
Current approaches:
- Simulation: Learn in low-risk environments first
- Conservative policies: Keep actions within a safe region
- Human oversight: Ask before novel actions
- Reward shaping: Penalise dangerous states
Source: The Wall Street Journal
How do you specify “safe” without already knowing everything about the domain?
Negative side effects
- AI optimises for specified objective
- It ignores everything else
- Unintended consequences not in objective
- “You didn’t say not to…”
Classic example (thought experiment):
- Robot tasked with fetching coffee
- Knocks over obstacles in path
- Harms humans in the way
- Technically: Coffee fetched ✓
Real-world version:
- Content algorithm maximises engagement
- Side effect: Polarisation, addiction
- Not in objective function
- Nobody specified “don’t harm society”
The world is too complex to specify everything we care about. AI must somehow learn to preserve what matters.
What is alignment?
- Alignment: AI systems that do what we want
- Pursue goals we actually intend
- Not just stated objectives
- Behave safely even when we can’t supervise
Why the word “alignment”:
- AI goals aligned with human values
- Not orthogonal or opposed
- Not pursuing random objectives
- Not technically satisfying letter while violating spirit
Why it’s hard:
- Hard to specify what we want
- Humans disagree about values
- Our stated preferences aren’t always true preferences
- Context matters enormously
Source: Anthropic
Why alignment is hard
Specification problem:
- Can’t write down everything we care about
- Edge cases are infinite
- Values are context-dependent
- Humans can’t articulate their own values perfectly
Goodhart’s Law: (remember this!)
- “When a measure becomes a target…”
- “…it ceases to be a good measure”
- Optimise metric ≠ achieve goal
Examples:
- Click rates → clickbait
- Engagement → addiction
- GDP → environmental destruction
The King Midas problem: (Russell, 2014)
- Gets exactly what he asked for
- Not what he wanted
- Literal interpretation of wishes
- Common AI failure mode
The alignment survey
- Survey of alignment problem and techniques
- Published in foundational AI research
However…
- No solved problem: Active research area
- Multiple approaches needed
- Uncertainty about scaling
- Theoretical foundations lacking
Open questions:
- Will RLHF scale?
- Can we detect deceptive alignment?
- How do we handle value disagreement?
- What’s the role of interpretability?
- When is good enough “good enough”?
Source: Ngo et al. (2022)
Research directions:
| Area | Focus |
|---|---|
| Interpretability | Understanding what AI “thinks” |
| Robustness | Maintaining alignment under pressure |
| Scalable oversight | Supervising superhuman AI |
| Value learning | Inferring human values |
| Corrigibility | Making AI correctable |
Where is AI heading?
Current trends:
- Models getting larger and more capable
- More general-purpose systems
- Multimodal capabilities
Uncertainties:
- Will scaling continue to work?
- When do we hit diminishing returns?
- What capabilities emerge unexpectedly?
- How fast is too fast?
Expert disagreement:
- Wide variation in predictions
- Timeline estimates vary by decades
- Some expect AGI soon, others never
- Confidence often exceeds evidence
Source: Our World in Data
Honest answer: Nobody knows. Uncertainty is high. Be sceptical of confident predictions.
Scenarios to consider
Scenario 1: Gradual improvement
- AI gets better slowly
- Humans adapt
- Society adjusts incrementally
- Most likely?
Scenario 2: Capability jumps
- Sudden breakthroughs
- Unexpected capabilities
- Rapid deployment
- Less time to adapt
Scenario 3: Plateau
- Current approaches hit limits
- Progress slows dramatically
- Different paradigms needed
- Also possible
Scenario 4: Transformative AI
- Systems vastly more capable than humans
- Fundamental changes to economy, society
- Either very good or very bad
- Uncertain timeline
Source: American Enterprise Institute
Stuart Russell’s perspective
Russell’s argument (TED talk):
- We’re building systems whose objectives we don’t fully control
- Standard AI paradigm: optimise given objective
- Problem: We can’t specify objectives correctly
- Need fundamentally different approach
His proposal:
- AI should be uncertain about objectives
- Should defer to humans
- Should allow itself to be switched off
- Objectives learned, not programmed
“You cannot fetch the coffee if you are dead”
- AI should value human control
- Not because told to
- Because it’s instrumentally useful for actual goals
Source: TED
Russell is a leading AI researcher who takes long-term safety very seriously. Worth hearing him out.
Existential risk: the debate
Those who worry:
- AI could become uncontrollable
- Misaligned powerful AI = catastrophe
- Even small probability × huge harm = important
- “We might not get a second chance”
- Hinton, Bengio, Russell, many others
Those who are sceptical:
- Speculative, distracts from real present harms
- “Sci-fi thinking” not grounded
- We control the off switch
- Capabilities overstated
The actual state of debate:
- Serious researchers on both sides
- Uncertainty and disagreement are genuine
Research directions
Technical safety research:
| Area | Goal |
|---|---|
| Interpretability | Understand AI internals |
| Robustness | Resist adversarial inputs |
| Alignment | Ensure AI pursues intended goals |
| Oversight | Scale human supervision |
| Honesty | AI that doesn’t deceive |
Growing field:
- Anthropic, OpenAI, DeepMind safety teams, academic labs
- Still small relative to capabilities
Career opportunity:
- High-impact work
- Talent shortage
- Many paths in (technical, governance, policy)
