“The masters are liable to  get  replaced  because  as  soon  as  any  technique becomes  at  all  stereotyped  it  becomes  possible  to  devise  a  system  of  instruction  tables which will enable the electronic computer to do it for itself. They may be unwilling to let their jobs be stolen from them in this way. In that case they would surround the whole of  their work  with  mystery  and  make  excuses,  couched  in  well-chosen  gibberish,  whenever  any  dangerous  suggestions  were  made.” – Alan Turing

“Free your mind, and the rest will follow” – En Vouge

In my Great Liberation series (Part I, Part II), I argued that the way AI is being rapidly injected and measured in software testing, a discipline that profoundly shapes modern life, has moved to borderline recklessness. Banking, healthcare, transport, education, and government infrastructure all run on software we need to be reliable and AI adoption is racing ahead at a pace and human judgment can’t keep up.

The reason this matters is because AI is being treated as a substitute for critical thinking and responsibility rather than a tool that demands more of it. Poorly tested systems cause real harm, and when organizations replace human evaluation with automation in the name of speed or cost savings, they increase risks to their business and society.

Despite what you may have heard or read, AI in testing is not neutral or low risk. The dream being sold of closed loop systems validating themselves, with minimal external oversight will make failures more systemic, less visible, and harder to correct. More than ever, we need to be pushing back against the hype cycle selling AI adoption as inevitable, discouraging skepticism and trading on FOMO.

But unfortunately, the software testing industry has failed to meet the challenge.

Happy to be included in the 297th issue of Software Testing Weekly – thanks, Dawid Dylowicz!

What can be asserted without evidence can also be dismissed without evidence. – Hitchens Razor

One of the biggest mistakes organizations make when building and testing systems is to measure it badly and then make decisions based on faulty and invalid metrics. Software testing has a long, rich history of goal displacement and metric validity problems when it comes to measurement and GenAI evaluation is currently running that same crucible.

Happy to be included in the 197th issue of Software Testing Notes  – thank you!

I had a great time discussing leadership in enterprise tech with Michael Hunter for his Uncommon Leadership interview series.

I’ve managed teams all over the world of different shapes and sizes and rarely get to talk about it, so I appreciated the opportunity to discuss continually learning and improving my approach to leadership.

We spoke about bringing your whole self to work, career management vs stewardship, building (and changing into) cultures that empower people, and how to help people navigate uncertain times….enjoy!

References in the discussion: EuroSTAR NSTC Pay360 KPMG UK The Secrets of Consulting: A Guide to Giving and Getting Advice Successfully Rethinking Expertise The 48 Laws Of Power 

Very excited to announce I’ll be speaking at Agile on the Beach in July next year. AOTB has been a conference I’ve admired for a long time, and I am honored to finally attend and as well, talk about a subject that is close to my heart and have never spoken about in public before: ethics in technology.

Hope to see you there!

When the Whistle Blows: An Ethics Experience Report

Juvenal said “Honesty is praised and starves”, and nothing could be more true when it comes to whistle blowing.

Reporting unethical behavior is an incredibly stressful experience that is seldom ever rewarded and often comes with adverse personal consequences. Over the course of my 20+ year career in enterprise tech, I’ve been involved either directly or as a manager with multiple whistle blowing incidents as the result of organizational ethics complaints.

Through this talk I will present experience reports on of some of the most serious cases I’ve been party to including the process, lessons learned, and what I would do differently. So join me as I share the most challenging events that nearly broke me, but were always career defining moments of my life.

Putting together a good conference program is hard. Ensuring the topics are relevant and attracting talented speakers that people want to hear is only further complicated by the commercial aspects of covering costs and turning a profit for the organizers.

But a couple weeks ago, I happened to be walking out of the same conference talk as Richard Bradshaw, and we ended up having a chat about how we seem to be slipping back into “male only” line ups for not just keynotes but also track talks.

“If the rise of an all-powerful artificial intelligence is inevitable, well it stands to reason that when they take power, our digital overlords will punish those of us who did not help them get there. Ergo, I would like to be a helpful idiot. Like yourself.”
Bertram Gilfoyle

A while ago, I wrote a series of posts on how testing can support your business through a “transformation”, either through disruption, optimization, or by managing expectations around risk and what testing can do to help navigate digital transformation. As GenAI has stampeded into every aspect of technology delivery and innovation, I thought it only appropriate to add another entry into my “Test as Transformation” series.

I’ve always contended delivering technology value to your business will either be constrained or accelerated by your approach to testing, and adding AI into that risk is like injecting it with steroids. By layering black boxes into everything from customer service to decision support, testing has rapidly become one of the most important sources of business intelligence a company has and a true market differentiator.

Testing has always been about information – learning things about systems we didn’t know and confirming things we thought we did, but there are new risks AI presents for your business I’d like to highlight that testing should address.

The tragedy of Adam Raines death by suicide at age 16 this year should have shocked the world into a renewed focus on accountability in Silicon Valley but instead, we heard the familiar excuse from the no-man’s land of moral hazard – user error.

In the first court filings from OpenAI they defended themselves by victim blaming. “Misuse” and “unforeseeable use” are cited even though according to the parents claims, ChatGPT basically gave him instructions for self-harm including offering to help write the suicide note.

Using legalese and technical “terms and conditions” arguments to defend jeopardising a vulnerable teen only further lays bare the morally hollow position the company has taken. GenAI is an incredibly powerful tool that must come with responsibility instead of marketing gimmicks – we now see what’s at stake.

And not knowing is no longer an excuse.

The research paper, The Illusions of Thinking published by Apple earlier this year is a pretty chilling read in this context. We know that as the complexity of problems hit a threshold, the models basically collapse and their accuracy and the reduction in “thinking” drops dramatically.

That’s not just a problem for automating a business workflow with AI, it speaks directly to the complex problems being fed into these models by everyday people. People who struggle with depression, loneliness, and other mental health issues requiring empathy, nuance, and a depth of humanity not possible with GenAI.

Further to that, in 2023 the G7 countries agreed to implement a code of conduct for developing “Advanced AI Systems” including “risk-based design, post-deployment safety, transparency about limitations” and accountability when things go wrong because these systems require more than a TOR like an app that plays music on your phone.

The point is, we know to do better and are on the familiar path to let big tech off the hook on the grounds of “user error”, which ultimately treats a teenager’s tragic death as just a design flaw.

Happy to be included in the 294th issue of Software Testing Weekly – thanks, Dawid Dylowicz!