In my previous post, I introduced the Interpretability Tax - the extra resources (like compute power, time or human effort) needed to make an AI's decisions understandable. This tax becomes especially relevant when we give AI broad, autonomous goals - like "make more money" in trading - versus narrow, specific tasks - like "execute these kind of trades."
In the financial sector, interpretability is critical to ensure the AI isn't taking undue risks or breaking laws. But this tension between autonomy and explainability isn't unique to finance.
Let's explore how the Interpretability Tax plays out in other sectors, such as healthcare, autonomous vehicles, manufacturing, energy management and education, and compare the stakes and costs involved.
The Financial Sector: Autonomous Trading
Imagine an AI tasked with "maximising profits" in stock trading. It might autonomously buy and sell securities, adapting to market trends. Without interpretability, we wouldn't know if it's engaging in risky bets or illegal practices like market manipulation.
Why It Matters: Some traders or executives might not care about the AI's inner workings as long as profits roll in, but financial losses or regulatory violations could be catastrophic. Interpretability ensures accountability, catches risky patterns early and builds confidence among stakeholders, preventing disasters that could tank markets or firms.
The Tax: Extra systems to log trades, generate explanations and meet compliance rules - which utilises a % of compute.
Healthcare: Autonomous Diagnostics
In medicine, an AI might be asked to "optimise patient outcomes," autonomously diagnosing conditions or suggesting treatments, rather than just performing a specific task like "analyse this x-ray."
Why It Matters: A wrong diagnosis or treatment could cost lives. Doctors and patients need to understand the AI's reasoning to ensure safety and trust.
Autonomous Vehicles: Driving Decisions
Self-driving cars operate with the broad goal of "navigate safely to the destination," making split-second choices about braking, turning, or avoiding obstacles.
Why It Matters: An unexplained decision in an accident could lead to legal battles or loss of life. Interpretability ensures accountability and safety.
The Tax: Extra computational power and storage to record and explain every move, adding complexity to the system.
Manufacturing: Production Optimisation
In a factory, an AI might be tasked with "maximising efficiency" on the production line, adjusting schedules or machinery settings on its own.
Why It Matters: Unexplained changes could cause downtime, safety issues or defective products. Engineers need clarity to maintain quality.
The Tax: Additional sensors or data logs to track decisions, increasing operational costs.
Education: Personalised Learning
In education, an AI could be told to "maximise student success," tailoring lesson plans for individual learners.
Why It Matters: A poor recommendation could hinder learning or introduce bias. Teachers, parents and students alike need to see the reasoning.
Comparing the Interpretability Tax
In each case, the Interpretability Tax balances the freedom of AI autonomy with the need for human understanding. The cost - whether in compute power, time or oversight depends on the decision's complexity and the consequences of failure.
Conclusion
The Interpretability Tax isn't just a technical hurdle; it's a safeguard. As we push AI toward greater autonomy - whether trading stocks, treating patients or teaching kids - we must ensure we can still follow its logic. Each sector shows that while the tax varies, its purpose remains: to keep AI powerful yet accountable. The trick is designing systems where autonomy doesn't outpace our ability to trust them.