Concrete Problems in AI Safety

• Concrete Problems in AI Safety is a framework defining operational research challenges such as safe exploration, negative side effects, and scalable oversight in AI system deployment.
• It highlights real-world failures in autonomous vehicles, recommendation systems, and healthcare, emphasizing the need for both technical and socio-technical solutions.
• The approach integrates ML model constraints with organizational practices and regulatory oversight to mitigate unintended harms and promote robust, ethical AI usage.

Concrete problems in AI safety refer to a collection of operationally defined research challenges that emerge from unintended and harmful behaviors of machine learning and AI systems due to failures in objective function specification, underspecified cost structures, and the dynamics of autonomous learning. Rather than focusing on hypotheticals or distant existential risks, this area prioritizes tractable, empirically motivated failures that arise when deploying contemporary AI systems. The field originated with the taxonomy of Amodei et al., which systematized these risks, and has expanded to incorporate socio-technical dimensions after a critical reappraisal by Raji & Dobbe. Their expansions recognize that technical solutions alone are inadequate, and that organizational, regulatory, and societal layers must be integrated for robust AI safety assurance (Amodei et al., 2016, Raji et al., 2023, Hendrycks et al., 2021).

1. Formal Definitions and Core Problem Areas

Amodei et al. originally enumerated five foundational problems: Avoiding Negative Side Effects, Avoiding Reward Hacking, Scalable Oversight, Safe Exploration, and Robustness to Distributional Shift (Amodei et al., 2016). Raji & Dobbe revisit three—Safe Exploration, Avoiding Negative Side Effects, and Scalable Oversight—with reformulated definitions reflecting both technical and socio-technical complexity (Raji et al., 2023).

1.1 Safe Exploration

Safe exploration seeks to constrain the learning process, especially in RL, so that the agent with high probability avoids states or actions classified as unsafe:

$$\max_\pi\; \mathbb{E}\Bigl[\sum_{t=0}^\infty \gamma^t r(s_t,a_t)\Bigr],\quad\text{subject to}\;\forall t:\;\Pr_{(s_t,a_t)\sim\pi}[\text{unsafe}(s_t,a_t)] \le \delta$$

Failures in safe exploration are evidenced by autonomous systems (e.g., self-driving vehicles) causing harm due to lapses not only in technical modules (perception, planning) but also breakdowns in human-system interaction, supervisory protocols, and safety culture (Raji et al., 2023). The quantifiable metrics recommended include near-miss rates, human attention lapses, and trade-off curves between false alarms and missed interventions.

1.2 Avoiding Negative Side Effects

Negative side effects are unintended, possibly irreversible changes to the environment occurring in pursuit of formal objectives. The technical framing penalizes deviations via an auxiliary measure $\Delta(s_{t-1},s_t)$:

$$\max_\pi \mathbb{E}\bigl[ \sum_t r(s_t,a_t) - \lambda\Delta(s_{t-1},s_t)\bigr]$$

Classical examples include systems optimizing for recommender accuracy while undermining privacy (e.g., Netflix Prize re-identification) or large-scale facial recognition models generating labor exploitation and environmental costs (Raji et al., 2023). Extended definitions now encompass externalities at every pipeline stage, including data, infrastructure, and governance—proposing multi-agent externalities and impact-weighted utility as next-generation constraints.

1.3 Scalable Oversight

Scalable oversight addresses situations where the ground-truth objective $r^*(s,a)$ is too costly to evaluate continuously, necessitating periodic sampling and proxy metrics. The challenge is to select a schedule $\{t_i\}$ of queries with budget $B$ to optimize performance:

• True labels: Only for $i \in \mathcal{L}$, observe $R^*_i$
• Proxy: For $i \notin \mathcal{L}$, use $\hat R_i$

Empirical failures arise when overreliance on cheap proxies misleads learning, such as clinical decision support systems (IBM Watson for Oncology) failing due to lack of exposure to current patient data and inadequate stakeholder feedback loops (Raji et al., 2023).

2. Real-World Failure Modes and Case Studies

The field’s methodology is to ground problem definitions with real-world incidents, demonstrating failure propagation not just within “core” ML modules but across artifacts, organizational practices, and societal structures.

Problem Area	Notable Case	Manifestation of Failure
Safe Exploration	Uber AV, Tesla Autopilot	Fatal accidents due to interface/protocol breakdowns
Avoiding Negative Side Effects	Netflix Prize, Face Datasets	Privacy breaches, labor and environmental harms
Scalable Oversight	IBM Watson Oncology	Deployment failures due to misaligned proxies

The significance of these case studies is their exemplification of accidents emerging from the confluence of technical, operational, and governance failures—not isolated code bugs or reward misspecifications (Raji et al., 2023).

3. Socio-Technical Framework for AI Safety

Traditional taxonomies emphasized technical solution spaces: reward function optimization, impact regularization, or formal guarantees. Raji & Dobbe systematize failures across four interacting layers:

1. Technical Artifact Layer: ML models, embedded constraints, explicit safety mechanisms.
2. Engineering Practice Layer: Integration, deployment, audit protocols, quality control.
3. Stakeholder Governance Layer: Roles, safety culture, responsibilities, legal structures.
4. Societal Context Layer: Economic, environmental, ethical, and regulatory environment.

Directed dependencies can be notated as:

$$\text{Societal Context} \to \text{Stakeholder Governance} \to \text{Engineering Practice} \to \text{Technical Artifact}$$

Failures propagate oppositely. This framework directly addresses prior shortcomings by explicitly modeling “objective function” as a reflection of power and values, recognizing inductive validation over formal proof, and elevating stakeholder and public accountability (Raji et al., 2023).

4. Key Research Directions and Open Questions

Addressing these concrete problems now entails coupled technical-socio-technical interventions:

• Safe Exploration: Multi-mode protocols integrating human-machine interfaces, formal constraints, and organizational escalation. Metrics include near-miss incident rates and attention lapse statistics. Open questions: incentivizing near-miss reporting, quantitative auditing of safety culture (Raji et al., 2023).
• Negative Side Effects: Incorporation of social and environmental constraints into formal objectives, participatory design with affected communities, and computation of impact-weighted utilities. Open questions: formal tradeoffs between utility and externalities, governance for fairness in annotation/data sourcing (Raji et al., 2023).
• Scalable Oversight: Active learning with experts-in-the-loop, building audit trails, and explainability aligned with regulatory requirements. Metrics: label efficiency, calibration under drift, abstention rates. Open questions: aligning privacy law with oversight needs, empirical thresholds for human override (Raji et al., 2023).

The research agenda outlined by Hendrycks et al. extends this perspective: technical challenges around robustness (minimizing worst-case expected loss over distribution sets), monitoring (anomaly and OOD detection, calibration), alignment (value learning, side-effect minimization, proxy gaming), and systemic safety (cybersecurity, decision support under uncertainty) must be addressed in parallel, with each layer acting as a sieve in the “Swiss cheese” defense (Hendrycks et al., 2021).

5. Evaluation Metrics and Methodological Considerations

Quantitative assessment of safety interventions employs bespoke metrics tailored to each risk category:

• Safe Exploration: Rate of safety-critical near misses, expected vs observed human interventions, trade-off curves between false-alarm and miss rates.
• Side Effects: Impact-weighted utility, privacy/adversarial risk, compute carbon footprint.
• Oversight: Query-efficiency (performance vs number of expert labels), calibration under domain shift, abstention/override frequency.

A distinguishing methodological feature is the insistence on continuous, inductive validation: deployments must be regularly audited, and safety mechanisms must be empirically stress-tested in situ, rather than certified solely via static proofs or benchmarks (Raji et al., 2023).

6. From Technical to Multidisciplinary Solutions

The expanded vocabulary now encompasses not only mathematical and algorithmic advances but also interventions in engineering process, governance, and societal interface. Critical insights include:

• Technical fixes (e.g., constraint-based RL, impact regularizers) are insufficient if unaccompanied by robust interfaces and explicit responsibility allocation.
• Socio-technical integration—through hybrid teams, participatory governance, and regulatory alignment—provides feedback channels robust to “unknown unknowns.”
• Objective functions themselves are artifacts of societal power, and technical alignment must adapt to the evolving landscape of social values and stakeholder priorities (Raji et al., 2023).

A plausible implication is that adopting a layered, multidisciplinary approach is necessary for robust deployment of AI in safety-critical environments. Further, open methodological and normative questions remain at the intersection of ML, human-computer interaction, ethics, and public policy.

• "Concrete Problems in AI Safety" (Amodei et al., 2016)
• "Concrete Problems in AI Safety, Revisited" (Raji et al., 2023)
• "Unsolved Problems in ML Safety" (Hendrycks et al., 2021)