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Intellectual Debt and the Death of the Programmer

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at BBC Lectures on ML on Mar 9, 2020 [reveal]
Neil D. Lawrence, University of Cambridge

Abstract

Technical debt is incurred when complex systems are rapidly deployed without due thought as to how they will be maintained. Intellectual debt is incurred when complex systems are rapidly deployed without due thought to how they’ll be explained. Both problems are pervasive in the design and deployment of large scale algorithmic decision making engines. In this talk we’ll review the origin of the problem, and propose a roadmap for obtaining solutions. It’s a journey that will require collaboration between industry, academia, third sector, and government.

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\newcommand{\weightedAdjacencyScalar}{a} \newcommand{\weightedAdjacencyVector}{\mathbf{ \weightedAdjacencyScalar}} \newcommand{\onesVector}{\mathbf{1}} \newcommand{\zerosVector}{\mathbf{0}} $$

The Great AI Fallacy

I don’t know what Artificial Intelligence means, but one commonality I see in the use of the term is an idea which I’m coming to think of as “The Great AI Fallacy”.

The fallacy is associated with an implicit promise that is embedded in many statements about Artificial Intelligence. Artificial Intelligence, as it currently exists, is merely a form of automated decision making. The implicit promise of Artificial Intelligence is that it will be the first wave of automation where the machine adapts to the human, rather than the human adapting to the machine.

How else can we explain the suspension of sensible business judgment that is accompanying the hype surrounding AI?

This fallacy is particularly pernicious because there are serious benefits to society in deploying this new wave of data-driven automated decision making. But the AI Fallacy is causing us to suspend our calibrated skepticism that is needed to deploy these systems safely and efficiently.

The problem is compounded because many of the techniques that we’re speaking of were originally developed in academic laboratories in isolation from real-world deployment.

Figure: We seem to have fallen for a perspective on AI that suggests it will adapt to our schedule, rather in the manner of a 1930s manservant.

The News

Figure: Three news items from the Today programme seemed to combine on Wednesday for me.

Intellectual Debt

Figure: Jonathan Zittrain’s term to describe the challenges of explanation that come with AI is Intellectual Debt.

Lean Startup Methodology

The Mythical Man-month

Figure: The Mythical Man-month (Brooks, n.d.) is a 1975 book focussed on the challenges of software project coordination.

Separation of Concerns

  • Technical debt is the inability to maintain your complex software system.

  • Intellectual debt is the inability to explain your software system.

Virtual Gemba Walks

Service-oriented Architecture

Test-oriented Software

Adding Data

  1. ML Models deployed as ‘regular software’
  2. Sanity checks are being suspended
  3. Models are driven by “average case”

The Death of the Programmer

Figure: Malory’s book "Le Morte d’Arthur. A guide to team building in the age of chivalry.

Lancelot

Figure: Lancelot quashing another software issue. Lancelot was Arthur’s most trusted knight. In the software ecosystem the Lancelot figure is an old-hand software engineer who comes closest to having the full system overview.

FIT Models to FIT Systems

Data-oriented Architectures

Milan

Context

Stateless Services

Meta Modelling

Figure: The Emukit software is a set of software tools for emulation and surrogate modeling. https://amzn.github.io/emukit/

News

OfCom

The Census and the Big Data Paradox

Accounting

Conclusion

Brooks, Frederick. n.d. The Mythical Man-Month. Addison-Wesley.